2015 Ingenium Undergraduate Research Journal

2015

Highlighting Undergraduate Research at the University of Pittsburgh Swanson School of Engineering Spring 2015

The University of Pittsburgh is an affirmative action, equal opportunity institution.

Published in cooperation with the Department of Communications Services. DCS100669-0415 Please note that neither Ingenium nor the Swanson School of Engineering retains any copyright of the original work produced in this issue. However, the Swanson School does retain the right to nonexclusive use in print and electronic formats of all papers as published in Ingenium.

University of Pittsburgh Swanson School of Engineering Benedum Hall 3700 O’Hara Street, Pittsburgh, PA 15261 USA

UN D E RG R AD UAT E R E S E A RC H AT T H E SWA NSO N SC H O O L O F E NG I NE E R I N G

Table of Contents 4 A Message from the Associate Dean for Research 5 A Message from the Co-Editors-in-Chief 6 Editorial Board Members Articles 7 Uphill Walking Increases the Extent of Learning of a New Stepping Pattern Performed on a Split-Belt Treadmill

Calvert, J.S., Sombric, C.J., and Torres-Oviedo, G. Department of Bioengineering

12 Case Study for Sustainable Building Modeling on a University Campus Cortes, S.P. and McDermott, T.E.

Department of Electrical and Computer Engineering

18 Predicting Strength of Nanocrystalline Copper from Molecular Dynamic Simulations Dahal, B., Wang, G., and Lei, Y.

Department of Mechanical Engineering and Materials Science

23 Differentiation of Perivascular Progenitor Cells from Human Aorta and Their Role in Neovascularization Ellis, B.W., Green, B.R., Hill, J.C., Donnenberg, V.S., Gleason, T.G., and Phillippi, J.A. Department of Bioengineering

28 Towards Structure-Cytotoxicity Correlations for Complex Engineered Nanomaterials

Gong, Y., Mahoney, S., Richardson, T., Padgaonkar, K., Hinkle, S., Banerjee, I., and Veser, G. Department of Chemical and Petroleum Engineering

34 Determination of the Hydrodynamic and Mass Transfer Parameters in a Pilot-Scale Slurry Bubble Column Reactor for Fischer-Tropsch Synthesis

Hong, Y., Basha, O., Sehabiague, L., and Morsi, B.I. Department of Chemical and Petroleum Engineering

40 Dynamic Reactor Simulations of Chemical Looping Combustion Hughes, J. and Veser, G.

Department of Chemical and Petroleum Engineering

48 Quantifying Tibiofemoral Joint Contact Forces in Patients with Knee Osteoarthritis Using OpenSim

Kendell, P., Anderton, W., Gustafson, J., and Farrokhi, S. Department of Bioengineering

54 Domain-wall Memory Buffer for Low-energy Networks on Chip

Kline Jr., D., Xu, H., Chen, F., Melhem, R., and Jones, A.K. Department of Electrical and Computer Engineering

61 Body Segment Parameters in Normal Weight versus Obese Young Females Knewtson, M., Merrill, Z., Cham, R., and Chambers, A. Department of Bioengineering

66 An Adipose Stem Cell Suspension In Keratin Hydrogel for Peripheral Nerve Injury Treatment Marra, L., Minteer, D., and Marra, K. Department of Bioengineering

72 Adipose-Derived Stem Cells from Diabetic Patients Display a Pro-Thrombogenic Phenotype Pezzone, D.J., Krawiec, J.T., Weinbaum, J.S., Rubin, J.P., and Vorp, D.A. Department of Bioengineering

77 Open-Hole Strength of Bamboo Laminate for Low-Impact Timber Repair Platt S.L. and Harries, K.A.

Department of Civil and Environmental Engineering

82 Surge Generator Design for Electric Power Systems Lab

Smith, Z.T., Doucette, M.R., Freeman, J.D., Barchowsky, A., Grainger, B., Reed, G.F., and Carnovale, D.J. Department of Electrical and Computer Engineering

88 Regenerating Composite Layers from Severed Nanorod-Filled Gels

Snow, C.S., Yong, X., Kuksenok, O., and Balazs, A.C. Department of Chemical and Petroleum Engineering

93 Pore Distribution, Mechanical and Compositional Characterization of Inconel 718 Manufactured by Laser Engineered Net Shaping

Stevens, E., Toman, J., Zhang, P., To, A., and Chmielus, M. Department of Mechanical Engineering and Materials Science

Ingenium 2015

A Message from the Associate Dean for Research “Ingenium” is medieval English vernacular for “an ingenious contrivance.” In his book Ingenium: Five Machines That Changed the World, Mark Denny describes “useful and ingenious applications of physical principles.” To celebrate the “ingenium” inherent in the practice of engineering, the University of Pittsburgh Swanson School of Engineering presents this inaugural edition of Ingenium: Undergraduate Research at the Swanson School of Engineering.

David A. Vorp, PhD

Ingenium is a compilation of reports representing the achievements of selected Swanson School undergraduate students who demonstrated excellence in our summer research program during the summer of 2014. The students worked with a chosen faculty mentor—some at the University of Pittsburgh and some outside, even through international experiences. At the conclusion of the program, students were asked to submit an abstract summarizing the results of their research, which were reviewed by the Ingenium editorial board, made up of Swanson School graduate student volunteers.

The authors of the highest-ranking abstracts then were invited to submit full manuscripts for consideration for this inaugural issue, and those manuscripts were peer-reviewed by the editorial board.

Ingenium, therefore, is more than a record of our undergraduate students’ excellence in research; it serves as practical experience for our undergraduate students in scientific writing and the author’s perspective of the peer review process. It also provides practical experience for our graduate students in editorial review and the reviewer’s perspective of the peer review process.

I would like to acknowledge the hard work of the co-editors-in-chief of this inaugural issue of Ingenium, Omar Basha and Dariush Mohammadyani, and the production assistance of Melissa Penkrot, Paul Kovach, Marygrace Reder, and the Department of Communications Services. This also would not have been possible without the hard work of the graduate student volunteers who constitute the Ingenium editorial board (individually listed on page 6). It also is appropriate to thank the faculty mentors of the authors and other coauthors of the reports included in this issue.

On behalf of the entire Swanson School of Engineering community, I hope that you enjoy reading Ingenium. In addition, I hope it will enlighten and encourage our undergraduate students to continue to strive toward excellence and offer solutions to the world through the practice of engineering.

David A. Vorp, PhD Associate Dean for Research University of Pittsburgh Swanson School of Engineering 4

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A Message from the Co-Editors-in-Chief

Omar Basha

It is our pleasure to introduce to you the inaugural issue of Ingenium: Undergraduate Research at the Swanson School of Engineering. Ingenium is an interdisciplinary publication that aims to showcase the wide variety of original research by our diverse and vibrant undergraduate researchers at the University of Pittsburgh Swanson School of Engineering. This publication has been inspired by the diversity of our authors.

We are glad to present a rich and impressive array of work covering a huge variety of topics, spanning both the experimental and the theoretical and ranging from the nano to the industrial scales.

Each paper presented in this issue underwent a two-step peer review process, involving evaluation of extended abstracts and full manuscripts. We are thankful for the tireless efforts of the editorial board, which is composed of outstanding graduate students at the Swanson School of Engineering. These individuals volunteered their time and experience to this project.

We are grateful to Associate Dean for Research David A. Vorp, PhD, for his thoughtful and effective guidance; to Melissa Penkrot for her invaluable help and advice; to Paul Kovach, Marygrace Reder, and the Department of Communications Services for their production assistance; to our brilliant contributors; and, of course to you, the reader, from whom the entire process and presentation of research derives its significance.

Dariush Mohammadyani

Thank you!

Omar Basha Dariush Mohammadyani Co-Editor-in-Chief Co-Editor-in-Chief

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Editorial Board Members Ingenium: Undergraduate Research at the Swanson School of Engineering Co-Editors-in-Chief: Omar Basha (Chemical Engineering) and Dariush Mohammadyani (Bioengineering) Editorial Board

Department

Al Hassan, Hashim............................................................................................ Electrical and Computer Engineering Bagheri, Abdollah............................................................................................. Civil and Environmental Engineering Bickta, Janelle...................................................................................................................................... Bioengineering Bocan, Kara....................................................................................................... Electrical and Computer Engineering Borrero, Juan............................................................................................................................. Industrial Engineering Campion, Nicole............................................................................................... Civil and Environmental Engineering Cardoza, Alvaro................................................................................................ Electrical and Computer Engineering Chen, Rongzhang.............................................................................................. Electrical and Computer Engineering Erhard, Amanda................................................................................................ Electrical and Computer Engineering Franconi, Nicholas............................................................................................ Electrical and Computer Engineering Gau, David........................................................................................................................................... Bioengineering Goh, Saik Kia....................................................................................................................................... Bioengineering Grasinger, Matthew........................................................................................... Civil and Environmental Engineering Jampani Hanumantha, Prashanth...................................................................... Chemical and Petroleum Engineering Jiang, Minlin..................................................................................................... Electrical and Computer Engineering Keane, Tim........................................................................................................................................... Bioengineering Kozak, Joseph................................................................................................... Electrical and Computer Engineering Marshall, Brandon..............................................................................Mechanical Engineering and Materials Science Mathew, Shibin................................................................................................. Chemical and Petroleum Engineering McClain, Nicole................................................................................................................................... Bioengineering Mehta, Karan............................................................................................................................. Industrial Engineering Minteer, Danielle................................................................................................................................. Bioengineering Ostrowski, Nicole................................................................................................................................ Bioengineering Palangappa, Poovaiah....................................................................................... Electrical and Computer Engineering Patil, Mitali.......................................................................................................................................... Bioengineering Rothfuss, Michael............................................................................................. Electrical and Computer Engineering Sachs, Steven.................................................................................................... Civil and Environmental Engineering Shekhar, Sudhanshu............................................................................................................................. Bioengineering Simon, Cesar..................................................................................................... Civil and Environmental Engineering Stachel, Joshua.................................................................................................. Electrical and Computer Engineering Streiner, Scott............................................................................................................................ Industrial Engineering Sweriduk, Michael............................................................................................ Civil and Environmental Engineering Zare, M. Hosein........................................................................................................................ Industrial Engineering Zu, Hongfei........................................................................................Mechanical Engineering and Materials Science 6

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Uphill Walking Increases the Extent of Learning of a New Stepping Pattern Performed on a Split-Belt Treadmill Jonathan S. Calvert, Carly J. Sombric, and Dr. Gelsy Torres-Oviedo Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

Post-stroke patients often possess an asymmetric gait that affects their mobility and quality of life. Promising studies have shown that this gait asymmetry can be corrected by walking on a split-belt treadmill that forces the subjects’ legs to move at different speeds.

Therefore, there is an interest in increasing the extent of these positive effects during and after split-belt walking. Walking at an incline has been shown to modulate the forces that subjects apply at their feet, which we hypothesize drive the adaptation of walking patterns during split-belt walking. In this study we investigated the effect that sloped surface has on splitbelt adaptation. Fifteen subjects were divided among uphill, downhill, and flat walking groups. The groups walked on a split-belt treadmill, in which one leg moved at 0.5 m/s and the other leg moved at 1.5 m/s for 600 strides. Subjects also walked on the treadmill when the two belts moved at the same speed (1 m/s) before and after the split-belt condition. To compare the walking pattern of the groups, we computed their asymmetry in 1) step length, 2) step position, and 3) step time, which are measures known to adapt independently during split-belt walking. The uphill group had spatial asymmetries that were significantly higher than the other two groups during and immediately following split-belt walking. However, the step time asymmetry was not significantly different across groups.

Therefore, uphill walking caused a greater extent of learning of the step pattern, but not of the step rhythm acquired on the treadmill.

Keywords: Biomechanics; Locomotor Adaptation; Motor Learning; Stroke Rehabilitation Abbreviations: SL – Step Length; HS – Heel Strike; TO – Toe Off

Introduction

One of the most common problems for stroke survivors is an asymmetric walking pattern that adversely affects their mobility and quality of life. Previous studies have shown promising results that stroke patients can correct their gait asymmetries after walking on a split-belt treadmill [1]. A split-belt treadmill is a device that has two belts that can move at different speeds. Subjects are made to walk with one leg moving two or three times faster than the other leg, which forces the body to adapt its walking pattern to the speed asymmetry of the belts. This process has been used to study how the motor system adapts to perturbations to locomotion, and has been used in stroke patients to adapt their gait to a more symmetric walking pattern. Thus, there is an interest in enhancing the extent and duration of these positive effects after split-belt walking.

Sloped walking has been shown to naturally modulate the forces experienced at the feet when compared to flat walking [2]. Downhill walking has been shown to increase the breaking force (i.e., the force applied to the foot when it lands) compared to level walking, and uphill walking has been shown to decrease the breaking force [2]. Importantly, the breaking force has been shown to behave like an error signal during splitbelt adaptation [3]. It suddenly increases when the split-belt perturbation is introduced and it is reduced as subjects adapt their gait. Thus, we hypothesize downhill and uphill walking might change the extent of gait adaptation on the split-belt treadmill by accentuating the effect of the split-belt perturbation. To test this, we assessed the extent of adaptation effects during splitbelt walking and the retention of adaptation effects after split-belt walking when healthy subjects walked uphill or downhill.

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Methods

Fifteen healthy volunteers (mean age 24.2±5.3, 9 men, 6 women) participated in the study. The subjects were equally divided among three groups: flat, uphill, and downhill walking (n=5 on each). The three groups walked on a split-belt treadmill (Bertec), in which one leg moved at 0.5 m/s and the other leg moved at 1.5 m/s (3:1 belt speed ratio) for 600 strides. Subjects also walked on the treadmill when the two belts moved at the same speed (1 m/s) before (50 strides) and after the split-belt condition (400 strides). In order to control for footedness, subjects were adapted with their selfreported dominant leg on the fast belt. Kinematic data were recorded in all groups with a motion tracking system (Vicon) via reflective markers placed bilaterally on bony landmarks on the ankles (lateral malleolus) and hips (greater trochanter). In the uphill and downhill groups, the treadmill was inclined to a 15% grade (~8.5°) before, during, and after split-belt walking.

Outcome Measures To accomplish this, we computed in each group their asymmetry in 1) step length, 2) step position, and 3) step time, which are spatial and temporal measures known to adapt independently during split-belt walking [5].

All asymmetry values were normalized by total step length in order to compare across subjects. Step length asymmetry is calculated as

where fast indicates the leg on the fast belt and slow indicates the leg on the slow belt. Step length is defined as the distance the foot travels from TO to HS of one step. Additionally, step position asymmetry is defined as

8

The foot position, α, indicates the relative distance between the hip and the ankle of the leg taking a step (leading leg) in the sagittal plane. A difference, ∆α, in feet positions when taking a step with one leg or the other is computed. Feet positions when taking a step with either the fast (∆αf) or the slow (∆αs) leg being compared to the previous feet position of the slow or fast leg steps, respectively. Step time asymmetry is defined as

where v indicates the velocity of the respective leg and t indicates the time from HS of one leg to the HS of the other.

We evaluated the extent of adaptation effects (i.e. aftereffects) on these outcome measures by comparing their average values before split-belt walking and the initial steps (i.e., average of the first 5 steps) after split-belt walking. We also compared the decay of adaptation effects across the three groups. To this end we computed the exponential decay of each of the groups to determine if the groups retained the newly learned walking pattern for a longer duration after split-belt adaptation.

Statistical Analysis The raw kinematic data was extracted from Vicon and processed in MATLAB. Values from pre-splitbelt walking were subtracted from the post-splitbelt walking values to assess how well the subjects retained the new walking pattern. A one-way ANOVA with Tukey post hoc analysis was used to determine if there were differences between the walking conditions (α=0.05). To determine if the rate of decay of the stepping pattern after split-belt adaptation was different across experimental groups, curves were fitted with an exponential decay with time constants that had 95% confidence intervals.

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Results

The change in step length, step position, and step time asymmetries from early to late adaptation are shown in Figure 1. Likewise, the step length, step position, and step time asymmetries after split-belt walking are shown in Figure 2. In both cases, the uphill group had spatial asymmetries (i.e., step length and step position) that were significantly different to those of the other two groups. However, the step time asymmetry was not significantly different across groups during and after adaptation. Additionally, the flat and downhill groups were not significantly different among any of the asymmetry values.

The step position asymmetry values after split-belt adaptation in all three experimental groups is shown in Figure 3. In order to determine if the decay rate is the same across all three groups, the step position values were divided by their maximum value for each subject. These results were then averaged and are shown in Figure 4. As these two figures show, the spatial parameters start at a higher value after splitbelt adaptation, but appear to decay at the same rate. In order to qualitatively assess the decay rate, the curves in Figure 4 were fitted to an exponential decay. The step position asymmetry decay after split-belt adaptation had decay values of -0.0089, -0.0084, and -0.011 for the flat, uphill, and downhill conditions, respectively.

The flat and uphill conditions were statistically similar in their decay rates (p<0.05). The downhill condition was statistically different from the other two conditions (p>0.05), indicating a faster decay. Temporal values were statistically similar in magnitude as well as rate of decay across all three conditions.

Figure 1. Gait Asymmetries From Early to Late Adaptation.

Differences in step length, step position, and step timing asymmetries from early adaptation to late adaptation in the three experimental groups are shown. The step length and step position of the uphill group is statistically significantly different from the other two groups, however the step timing is not significantly different. The downhill group was not statistically significantly different from the flat group in any of the parameters.

Figure 2. Gait Asymmetries Following Adaptation.

Differences in step length, step position, and step timing asymmetries immediately following adaptation in the three experimental groups are shown. Similar to Figure 1, the step length and step position asymmetries were significantly larger in the uphill group, but statistically similar in the downhill group. No statistically significant difference in the step timing values were found.

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Figure 3. Step Position Asymmetry Following Split-Belt Walking. Time course of the average step position asymmetry following split-belt walking for all three experimental groups. The uphill condition starts at a higher initial value before decaying at a similar rate to the flat condition.

Figure 4. Normalized Step Position Asymmetry Following Split-Belt Walking. In order to normalize the values to the same starting position, each subject was divided by the maximum step position asymmetry value and then averaged across groups. The decay rates were statistically similar for the uphill and flat conditions, but statistically larger for the downhill group compared to the other two groups.

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Discussion

Uphill walking significantly increased the extent of gait adaptation in the spatial domain, but not the temporal domain. Previous studies have shown that slope walking in humans causes changes to the biomechanics of gait including altered ground reaction forces and joint torques [2]. Therefore, the increased adaptation of spatial parameters during and after split-belt walking may be due to the biomechanical changes caused by uphill walking. The increase in after-effects in step position and step length, but not step time, in the uphill condition for healthy subjects suggests that split-belt walking on an incline could enhance the correction of spatial asymmetries in stroke patients via split-belt walking. It is known that stroke patients often have larger deficits in one domain over the other [6]. An increase in adaptation of only spatial adaptation effects is important because it will allow clinicians to target those with primarily spatial asymmetries. Therefore, it is pertinent to identify ways to adapt spatial and temporal asymmetries independently. Although the incline walking condition caused the subjects to have a larger value in the spatial parameters during and after split-belt walking, the decay occurred at a similar rate to what was seen in the other two conditions. This implies that although incline walking increases the adaptation effects of split-belt walking, it does not increase the duration of the effect. Future studies will examine if the increased extent of adaptation during uphill split-belt walking in the spatial parameters generalizes to overground and flat walking post-adaptation, and if the results seen in healthy controls transfers to stroke subjects.

Acknowledgements

We would like to acknowledge the Swanson School of Engineering for providing funding to complete this project. Additionally, we would like to thank our lab engineer William Anderton for aiding us in running our experiments.

References

[1] DS Reisman, R Wityk, K Silver, AJ Bastian, Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke, Brain. 130.7 (2007) 1861-1872.

[2] AN Lay, CJ Hass, RJ Gregor, The effects of sloped surfaces on locomotion: A kinematic and kinetic analysis, J. Biomech. 39.9 (2006) 1621-1628.

[3] T Ogawa, N Kawashima, T Ogata, K Nakazawa, Predictive control of ankle stiffness at heel contact is a key element of locomotor adaptation during splitbelt treadmill walking in humans, J. Neurophys. 111.4 (2014) 722-732.

[4] F Mawase, T Haizler, S Bar-Haim, A Karniel, Kinetic adaptation during locomotion on a split-belt treadmill, J. Neurphys. 109.8 (2013) 2216-2227.

[5] JM Finley, A Long, AJ Bastian, G. Torres-Oviedo.

Spatial and Temporal Control Contribute to Step Length Asymmetry during Split-Belt Adaptation and Hemiparetic Gait, Neurorehabilitation and Neural Repair, (In Publication).

[6] LA Malone, AJ Bastian, Spatial and Temporal Asymmetries in Gait Predict Split-Belt Adaptation Behavior in Stroke, Neurorehabilitation and Neural Repair. 28.3 (2014) 230-240.

Conclusion

Overall, the uphill condition caused subjects to have a larger extent of adaptation of spatial gait features, but did not change the decay of adaptation effects.

Downhill walking did not have an effect on the extent of gait adaptation but after-effects decayed slightly faster than in the other two conditions. The adaptation and after-effects decay of temporal gait features was not modulated by uphill and downhill walking. In conclusion, uphill walking could be used to adapt, and correct, more spatial asymmetries post-stroke.

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Case Study for Sustainable Building Modeling on a University Campus S.P. Cortes and T.E. McDermott Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

conservation measures (ECMs) towards design and renovation. Particularly, the significance of verifying simulation results with actual data and the impact of integrating ECMs into the model of the Mascaro Center for Sustainable Innovation (MCSI) will offer a case study that can be generalized to other buildings.

LEED (Leadership in Energy and Environmental Design) is a certification program that increases awareness and promotes greener buildings.

Recognized as the standard for measuring building sustainability, LEED takes into account new building design and construction, renovation of older buildings, and the operation and maintenance of buildings. Apart from energy, water, and financial savings, one of the motivations of having a LEED-certified building is the availability of numerous state and local government incentives. Incentives vary for each location and can include expedited permitting, tax credits, fee reductions or waivers, grants, and even technical and marketing assistance [2]. There are four levels of LEED certification and six categories under which a project can be certified. The overall well-roundedness of LEED allows for a green project that can be tailored to each building.

Keywords: green buildings, energy conservation, photovoltaic cells, renewable energy.

One way to find an optimal, green design is through building modeling. EnergyPlus is a prominent wholebuilding simulation program created by the Department of Energy (DOE). EnergyPlus allows for the optimization of building designs towards less energy and water consumption by modeling performance of a building that includes heating, cooling, lighting, ventilation, and water [3]. EnergyPlus was built modularly and object oriented facilitate the addition of component simulation modules by developers [4].

The U.S. buildings sector accounted for 41% of total energy consumption in 2010 according to the U.S. Department of Energy (DOE). Sustainable building design will have an impact on this number and play an important role in reducing it. A LEED-Gold certified building at the University of Pittsburgh, the Mascaro Center for Sustainable Innovation (MCSI), was modeled using DOE’s EnergyPlus software.

A particular LEED criterion, “on-site renewable energy”, was considered as a means to further reduce MCSI’s net energy consumption. Building Integrated Photovoltaics (BIPV) façades were investigated as an energy conservation measure (ECM) that would also serve as a replacement for window glazing. Different types of BIPV, including both PV generation and glazing effects, were integrated into the EnergyPlus model of MCSI. Simulation results show that BIPV can significantly impact the building thermal performance, with varying energy offsets from PV generation. In some cases, the net energy consumption increased with BIPV. This work demonstrates the value of building simulation in carefully designing and evaluating LEED green-building initiatives.

Introduction

From affecting the environment to affecting the health of people all around the world, buildings hold a significant role in communities on a global scale.

Considering that approximately 5 billion square feet are built each year [1] and that Americans spend most of their time indoors, constructing more efficient buildings and understanding building impacts on the environment and on people’s health is increasingly vital. The primary aim of this study is to understand the importance of buildings, modeling performance and efficiency, and to consider alternative energy 12

Testing and validation of EnergyPlus is a significant, ongoing process [5]. The developers of EnergyPlus are continuously conducting analytical tests, comparative tests, and release and executable tests in an effort to

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make EnergyPlus as bug-free as possible [6]. Because EnergyPlus is based on userâ&#x20AC;&#x2122;s description of a building, there is a large amount of potential error from the user.

The verification process contains abundant uncertainty.

Accurate and detailed monitoring of buildings is the best way to overcome the uncertainty.

MCSI is a 3-story extension of the 12-story Benedum Engineering Hall, and has achieved LEED Gold certification (Fig. 1a). The renovations, finished in 2010, included low-VOC (Volatile Organic Compound) emitting building materials, a green roof (the first in the University), installation of occupancy sensors, reduced need for artificial lighting due to daylighting features, low-flow plumbing fixtures that use one-third less water than traditional fixtures, and long-term monitoring of green building performance [7].

The study presented here builds on a previous study completed by DeBlois [8]. Two different types of building integrated photovoltaic (BIPV) panels were evaluated as possible energy conservation measures (ECMs) that could be integrated into MCSI to provide on-site renewable energy. For this purpose, each of the BIPV panels were integrated into different EnergyPlus models of MCSI and the results from the simulations were compared to the results from the simulation of the original model of MCSI. The results produced from this study demonstrate the importance of understanding buildings and of modeling alternative ECMs towards the design and renovation of buildings. An objective of this study is that more accurate monitoring of building performance will be encouraged and implemented.

Methods

The study consisted of an EnergyPlus model that had been built in a previous study by DeBlois (Fig. 1b) [8]. The model had been calibrated for both the hourly and monthly methods as detailed in another study [9]. Each EnergyPlus build has been tested and validated extensively through comparisons of the model data to metered consumption data, has been detailed in a previous study [10] and determined to meet standards developed by the American Society of Heating, Refrigeration and Air Conditioning Engineers (ASHRAE) [8].

More data has been made available from the electric meter since the verification of the model detailed by DeBlois. Thus, prior to any other investigation into the MCSI model, the outputs for lighting and electric equipment loads from the EnergyPlus simulation were compared to measurements obtained from the sensor meters. This allowed for increased confidence that the model continues to be representative of the actual building performance.

One particular category under which MCSI could have improved (only having scored 4 points out of 14 possible) was Energy & Atmosphere [11]. In particular, this study looks at â&#x20AC;&#x153;on-site renewable energy,â&#x20AC;? a criterion with the availability of one credit. Ultimately, this would not have made a significant difference in the final certification level since 10 more points, under the LEED v2.0 Core & Shell system, would have been needed for MCSI to be awarded LEED Platinum. Nonetheless, it demonstrates how various alternative measures can be taken to improve building sustainability through the examination of LEED standards that can be applied to the building in question.

Figure 1. a) Picture of MCSI after renovations. b) Model of MCSI used in EnergyPlus

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Table 1. BIPV Input Values into EnergyPlus

The most common types of solar panels available are mono- and polycrystalline silicon and thin film.

Amorphous silicon, a thin film known for being extremely economical but having low efficiency, and monocrystalline silicon, the least economical with high efficiency, solar cells were considered in this document.

Solar panels manufactured from monocrystalline silicon solar panels were also investigated Spectral (i.e. glazing) and electrical properties from the datasheets for two commercial products, one that was amorphous silicon [12] and the other that was monocrystalline silicon [13] were used to simulate each faรงade in EnergyPlus (Table 1). For this preliminary investigation, a simple glazing system was used in the EnergyPlus model to completely replace the prior exterior window constructions. The amount of energy generated from the solar panels and the heating and cooling loads, which are affected by the glazing properties of the panels, were also analyzed with respect to the currently existing windows on MCSI.

Results

The metered electric energy for the lights and electric equipment were averaged for each hour across each month for each year from Feb. 2012 to Jun. 2014.

The resulting data were graphed on the same plot and the respective EnergyPlus output was overlaid on the same plot (Fig. 2a). The fan data from the meter was also compared to the output from the simulation (Fig. 2b). The simulated fan data takes into account the userinput occupancy and load schedules (from the lights and electric equipment meter data) and the weather files. This was validated in the study by DeBlois [8].

The meter data for 2012 and early 2013 were very similar to the results from the simulation, because the meter data during that time were used to make the schedules for EnergyPlus. Thus, the significance 14

of evaluating more recent data not included in the original verification (end of 2013 and first half of 2014) is to ensure that the input into the model continues to represent the actual building schedules. From Fig. 2a, it is evident that this is the case. It should be noted that discrepancies in the supply and return fan data output seen in Fig. 2b can be explained by the meter not being actually connected to the same areas of MCSI as were simulated in EnergyPlus.

Integrating the BIPV faรงades into all of the exterior windows of the model (i.e. the windows in the first through third wings and second floor tower of Benedum Hall) resulted in the generation of electricity that could be used to help serve the building energy needs. Additionally, the total heating and cooling loads were compared to those of the original model. Due to the glazing property of the BIPV windows, the cooling load in MCSI was expected to decrease with minimal effect to the heating load, resulting in an expected electricity consumption reduction of MCSI. The cooling load results for both BIPV faรงades were as expected (Table 2); additionally, the heating load was greater for both BIPV scenarios. It is suspected that neither of the products simulated are as effective at thermal insulation as the existing windows. However, the thermal insulation was not further investigated in this study and remains a subject for future research.

The heating load increase was especially large for the case with amorphous silicon solar panels, resulting in an overall increased annual energy use. The increased heating load is offset in the model with the monocrystalline silicon panels, resulting in an annual reduction of energy.

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Figure 2. Electric loads in EnergyPlus: a) the lights and electric equipment depend on user-input schedules while b) is the output resulting from building performance and takes into account the scheduled loads, as well as weather files and other building descriptions.

Table 2. Annual Energy Results for Integration of BIPVs

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Discussion

This study has demonstrated the importance of understanding building impact, sustainable measures that can be applied to buildings, and how building simulation programs are used to determine ECMs that will result in optimal building performance. Inaccurate or incomplete monitoring can hinder successful validation of a model.

Through the study of two types of BIPV windows, the importance of having several alternative ECMs and of evaluating the overall picture was highlighted.

Heating is a significant component in the building energy use. An increase in the annual heating load was observed when either BIPV window was integrated into the building model. In an effort to further explore the effect of building heating, the total heat gain, total heat loss, and transmitted solar radiation of the windows should be analyzed. Furthermore, the impact of different window units – due to their unique spectral properties – on heating should be investigated before making a final decision on ECMs. Another consideration is that simple systems were used to model the glazing and PV properties of the BIPV window. Using more accurate and complex models could yield a deeper understanding of the reductions in cooling and gains in heating.

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Conclusion

The research presented here compared two different types of solar panels, one that was amorphous silicon and the other that was monocrystalline silicon, as BPIV façades, a form of on-site renewable energy. The case study on the LEED Gold certified MCSI consisted of integrating solar panel of each type into an existing model of MCSI in EnergyPlus. The results from the two simulations were compared with the results from the simulation of the original model. The results showed that the monocrystalline silicon BIPV façade reduced the overall energy consumption while the amorphous crystalline façade increased the energy consumption.

One consideration is that both BIPV façades were integrated using a simple model. Furthermore, this study showed how useful green initiatives such as LEED and simulation programs such as EnergyPlus are to the evaluation of building performance. Future directions include simulating more complex systems of the solar panels and evaluating the economic feasibility of implementing and maintaining BIPV façades.

Acknowledgements

The contribution of W.O. Collinge is gratefully acknowledged for the immense amount of time he spent and for the help he provided throughout this research. This work is supported by the Mascaro Center for Sustainable Innovation (MCSI) and the Bevier Scholar Foundation.

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References

[1] “Solution: The Building Sector.” Architecture 2030.

[Online]. Available: http://www.architecture2030.org/ the_solution/buildings_solution_how/.

Architectural & Planning Firm). [Online]. Available: https://d3pxppq3195xue.cloudfront.net/media/files/ University_of_Pittsburgh_BenedumHall_nbbj_case_ study.pdf.

[2] (2014 Jul.) “LEED Certification Information.”

Natural Resources Defense Council.

[Online].

Available: http://www.nrdc.org/buildinggreen/leed. asp.

[8] J. Deblois. “Building Energy Modeling for Green Architecture and Intelligent Dashboard Applications.”

PhD dissertation, Dept. Mech. Eng., Univ. Pittsburgh, 2013.

[3] “EnergyPlus.” U.S. Department of Energy.

[Online]. Available: http://apps1.eere.energy.gov/ buildings/energyplus/.

[9] Raftery, P., M. Keane, and A. Costa, Energy and Buildings, 2011. 43(12): 3666-3679.

[4] (2014 Jul.). “EnergyPlus Overview.” U.S. Department of Energy. [Online]. Available: http:// apps1.eere.energy.gov/buildings/energyplus/pdfs/ gettingstarted.pdf.

[5] R.G. Sargent. (2014 Jul.) “Verification and Validation of Simulation Models.” Journal of Simulation. (2013). 7, 12-24. doi:10.1057/jos.2012.20.

[Online]. Available: http://www.palgrave-journals. com/jos/journal/v7/n1/full/jos201220a.html.

[6] (2014 Jul.). “Testing and Validation.” U.S. Department of Energy. [Online]. Available: http:// apps1.eere.energy.gov/buildings/energyplus/ energyplus_testing.cfm.

[7] (2014 Jul.). “University of Pittsburgh.” Naramore, Bain, Brady & Johanson (Seattle, Washington

[10] Collinge, W.O., et al., “Measuring Whole-Building Performance with Dynamic LCA: A Case Study of a Green University Building in International Symposium of Life Cycle Assessment and Construction,” 2012: Nantes, France.

[11] “University of Pittsburgh Benedum Hall, LEED BD+C: Core and Shell (v2.0).” U.S. Green Building Council. Scorecard.

[12] (2014 Jul.). “Suntech See Thru BIPV Solar Modules.” Arcman Solar Power. [Online] Available: http://www.arcmansolar.com/catalog/7-5-13.aspx.

[13] (2014 Jul.). “Technology Overview.” Pythagoras Solar. [Online]. Available: http://www.pythagorassolar.com/technology-solutions/technology-overviewenergy-efficient-windows/.

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Predicting Strength of Nanocrystalline Copper from Molecular Dynamic Simulations Bhim Dahal, Guofeng Wang, and Yinkai Lei Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

The strength of polycrystalline materials depends strongly on their grain size. The conventional Hall-Petch effect states that the hardness and yield strength of polycrystalline materials increases with the decreasing grain size. However, below a critical grain size, the reverse Hall-Petch effect, i.e. strength decreases with decreasing grain size, is observed in nanocrystalline materials. In this paper, molecular dynamic simulations were implemented to investigate the reverse Hall-Petch effect in nanocrystalline Cu. It is found that the deformation mechanism is switched from dislocation glide to grain boundary motion when the grain size was smaller than the critical value. Such switching in deformation mechanisms provides an explanation to the reverse Hall-Petch effect.

have found that the reverse Hall-Petch is caused by the change in deformation mechanisms from dislocation glide to grain boundary (GB) motion [1-5]. In this paper, molecular dynamic (MD) simulations were used to investigate the deformation of an NC Cu. The deformation mechanisms for NC Cu with grain sizes below and above the critical grain size were investigated. This analysis confirms that the changing in deformation mechanisms is the reason for the reverse Hall-Petch relation in NC Cu.

Keywords: Nanocrystalline Cu, Molecular Dynamics, grain size, deformation mechanism

Introduction

Reducing the grain size of polycrystalline materials have been long known as an efficient strengthening method. As shown in Fig. 1, Hall-Petch relation predicts that the strength of the material increase as the grain size decreases. However, it is found in nanocrystalline (NC) materials that the Hall-Petch relation is no longer valid below a critical grain size [1-5]. When the grain size of NC materials is very small, the strength of the materials decreases as the grain size decreases. This is called the reverse Hall-Petch relation. Various works

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Figure 1. Illustration of the Hall-Petch and the reverse Hall-Petch relationship. Here, ﾏペ and d is the yield strength and average grain size of the material respectively. Above a critical grain size, the yield strength increases as the grain size decreases (Hall-Petch relation). Below the critical grain size, the yield strength decreases as the grain size decreases (reverse Hall-Petch relation).

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Methodology

The deformation of NC Cu was studied using Largescale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [6,7]. The interaction between the Cu atoms was described by the modified embedded atom method (MEAM) potential [8]. In MEAM potential, the total energy E of a system of atoms is given by: (1) where F is the embedding energy, ρi is the electron density at site i, φij is the pair potential between atom i and j, and rij is the distance between atoms i and j. The pair potential is summed over all neighbors j of atom i within a cutoff distance of 4 Å. MD simulations were performed at 300 K with a time step of 1 fs. At first, an NPT ensemble was used to equilibrate each model for 50 ps. NPT command performs time integration on NoseHoover style non-Hamiltonian equations of motion which are designed to generate positions and velocities sampled from the isothermal-isobaric ensemble [9].

After the equilibration, the constant strain rate of 109/s was applied for 150 ps to deform the model in the z-direction while the boundaries in x and y direction were fixed. The final strain in the z-direction was 15%.

The models of NC Cu were built by Voronoi construction implemented in AtomEye [10]. Three models with different grain sizes were considered in this paper. The number of atoms, number of grains and average diameter of grains for each model are given in Table 1. Three different average grain sizes of the models were chosen such that the grain size of the model 1 and 2 was below the critical value of the Cu (10~15 nm [11]) and the grain size of the model 3 was above the critical value. Therefore, by studying these three models, it was observed that the deformation mechanisms had changed as the grain size was reduced.

Results

First, the structure of the initial models was analyzed.

The undeformed atomic structure of model 2 is shown in Fig. 2. The atoms are colored by central symmetric parameters [12]. From table 1 it can be seen that 25.7% of the total volume of atoms reside in the GBs for model 2. The fraction of number of atoms in the GBs for each model is also given in Table 1. It is clear that the model with larger grain size has fewer fractions of atoms in the GBs.

Table 1. The number of atoms, average grain diameter, number of grains, and percentage of atoms in GBs for three NC Cu models we simulated. It is clear that the larger the grain size, the fewer atoms are in the GBs.

Figure 2. The atomic structure of model 2. The atoms are colored by central symmetric parameters.

The dark blue atoms are in the interior of the grains and remaining atoms are in the GBs.

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Figure 3. A) The stress-strain curve of each model during the simulations. The linear part before 2% strain was used to fit the Young’s modulus. And the yield strength was determined by the stress at which the stress-strain curve deviated from the linear fitting. B) The yield strength as a function of average diameter of grains. The reverse Hall-Petch relation can be seen.

The stress-strain curve of model 1, 2, and 3 is given in Fig. 3A. Two regions can be seen for each stressstrain curve. Below 2-3% strain, a linear relation between stress and strain can be observed. This region corresponds to the elastic deformation of the model.

Above 2-3%, the stress-strain curve deviates from the linear relation in elastic region, reaches a maximum and then starts to oscillate. This region corresponds to the plastic deformation of the model. Young’s modulus of the materials can be obtained by fitting the slope of the stress-strain curve in the elastic region. The fitted Young’s modulus of model 1, 2 and 3 are 81.0 GPa, 81.0 GPa and 93.0 GPa respectively. These values are in good agreement with previous simulation results by Schiotz et al [3]. They have shown the Young’s modulus of NC Cu is between 90.0~105.0 GPa when the average grain diameter is between 3.28~6.56 nm. This data shows that Young’s modulus doesn’t depend strongly on the grain size.

Other than Young’s modulus, yield strength can also be evaluated from the stress-strain curve. It is determined by stress at the boundary of the elastic and plastic region. Figure. 3B shows the yield strength of model 1, 2, and 3 as a function of the average grain diameter.

The yield strength of all three models lies between 1.7 GPa and 2.0 GPa. It is in good agreement with previous simulations by Choi et al [5]. They found that at 300K, the yield strength of NC Cu to be between 1.7 GPa to 20

2.5 GPa for grain diameters ranging from 5.0 nm to 50.0 nm. It is clear from Figure 3B that the yield strength decreases as the grain size decreases, which obeys the reverse Hall-Petch relation.

In order to understand this reverse Hall-Petch relation, the atomic structure of each model was investigated during the deformation processes. Figure. 4 shows the atomic structure of each model at strain of 0%, 4% and 15%. The atoms are again colored by central symmetric parameter so that the GBs and stacking faults can be seen. For model 1, the motion of GBs can be observed during the deformation. And the small grains are rotating to align with each other so that the final structure at 15% strain has only fewer grains compared to the undeformed structure. For model 2, similar results can be observed, with GB motion and grain agglomeration. However, stacking fault caused by a dislocation glide can also be found in a large grain (bottom grain at 15% strain shown by red arrow) but only at large strain. For model 3, on the contrary, the GBs are almost static. The number of grains is the same at 0% and 15% strain. Meanwhile, stacking fault can be observed when the material starts to yield at 4% strain (shown by red arrow). And more stacking faults can be found in the model with 15% strain. Therefore, the deformation mechanism for model 3 is dislocation glide while model 1 and 2 deformed through the motion of GBs.

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Figure 4. Atomic structure of model 1, 2 and 3 at 0%, 4% and 15% strain. The atoms are colored by central symmetric parameter. The cyan atoms in model 1 and model 2 are in the interior of the grains and remaining atoms are in GBs or stacking faults. The dark blue atoms in model 3 are in the interior of the grains and remaining atoms are in GBs or stacking faults. We used different coloring scheme to adjust the contrast so we can show GBs and stacking faults easily.

This change in deformation mechanisms can be used to explain the reverse Hall-Petch relation. It is already shown in Table 1 that as the grain size deceases the fractions of atoms in the GBs increase. GBs are hard to move and the atoms serve as obstacle for dislocation glide. Under this condition, increasing the number of GBs make the dislocations harder to slip. Hence increase the yield strength. However, when the grain size reduces to a critical value, a large amount of atoms reside in the GBs. This makes GBs unstable so that GBs can move before dislocations start to slide. In that case, further decreasing the grain size makes the GBs even more unstable, further reducing the yield strength of the materials.

Conclusion

In this paper, MD simulations were used to study the deformation of the NC Cu. It is found that the Youngâ&#x20AC;&#x2122;s modulus of NC Cu does not have a strong dependence of grain size, while the yield strength of the NC Cu decreases as the grain size decreases below a critical value. A detailed analysis on the deformation mechanisms show that the above the critical grain size, NC Cu deform through dislocations slide and below the critical value, NC Cu deform through the motions of GBs. This change in deformation mechanisms is the reason for the reverse Hall-Petch relation in NC Cu.

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Acknowledgements

I am grateful to Swanson School of Engineering (SSOE) by providing me SSOE summer research fellowship to carry out my proposed research in the summer of 2014. I am thankful to my mentor Dr. Guofeng Wang who provided me a privilege to work with him. I am thankful to Yinkai Lei, a PhD candidate and a student of Dr. Wang who helped me edit the several aspects of this paper. I am also thankful to following persons: Corinne Gray, Zhenyu Liu, and Kexi Liu.

References

[1] K.S. Kumar, H. Van Swygenhoven, S. Suresh, Acta Materialia 51 (2003) 5743-5774.

[2] P.G. Sanders, J.A. Eastman, J.R. Weertman, Acta Materialia 45 (1997) 4019-4025.

[3] J. Schiøtz, F.D. Di Tolla, K.W. Jacobsen, Nature 391 (1998) 561-563.

[5] Y. Choi, Y. Park, S. Hyun, Physics Letters A 376 (2012) 758-762.

[6] S. Plimpton, Journal of computational physics 117 (1995) 1-19.

[7] Retrieved 11/25, 2014, from http://lammps.sandia. gov.

[8] M.I. Baskes, Physical Review B 46 (1992) 2727.

[9] G.J. Martyna, D.J.Tobias, M.L. Klein, The Journal of Chemical Physics 101 (1994) 4177-4189.

[10] J. Li, Modelling and Simulation in Materials Science and Engineering 11 (2003) 173.

[11] J. Schiøtz, K.W. Jacobsen, Science 301 (2003) 1357-1359.

[12] C.L. Kelchner, S.J. Plimpton, J.C. Hamilton, Physical Review B 58 (1998) 11085.[12] C.L. Kelchner, S.J. Plimpton, J.C. Hamilton, Physical Review B 58 (1998) 11085.

[4] H. Van Swygenhoven, M. Spaczer, A. Caro, Acta Materialia 47 (1999) 3117-3126.

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Differentiation of Perivascular Progenitor Cells from Human Aorta and Their Role in Neovascularization Bradley W. Ellisa,b, Benjamin R. Greenb, Jennifer C. Hillb, Vera S. Donnenbergb,c, Thomas G. Gleasona,b,c, and Julie A. Phillippia,b,c a

Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA b Department of Cardiothoracic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA c McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

Pericytes associated with small blood vessels have been documented as multi-lineage progenitor cells in various tissues. This study explored the differentiation capabilities of primary pericytes from the adventitial vasa vasorum, and their ability to form vascular networks with human endothelial cells in vitro. Primary aortic adventitial cells expressing an antigenic profile of pericytes were cultured for 14 days with various medias and growth factors to promote differentiation towards smooth muscle cell (SMC) or endothelial cell lineages, respectively. Gene expression of SMC and endothelial markers were quantified and compared between treated and nontreated pericytes. Additionally, pericytes and human pulmonary endothelial cells (HPAECs) were cocultured on or in GFR-Matrigel. SMC differentiated pericytes displayed an increase in Îą-SMA expression and developed a spindle-like morphology indicative of differentiated SMCs. Pericytes cultured under endothelial lineage-promoting conditions exhibited an increase in CD31, an endothelial cell marker. Pericytes exhibited endothelial cell-like sprouting when cultured alone and co-localized in a perivascular location when co-cultured with endothelial cells on Matrigel substrates.

Keywords: Pericytes, Progenitor Cells, Vasa Vasorum, Bicuspid Aortic Valve, Aneurysm Abbreviations: BAV, TAA, BAV-TAA, FACS, SMC, qPCR, vWF, Îą-SMA, MHC, HPAECs, DMEM, VEGF, PDGF

Introduction

Bicuspid aortic valve (BAV) is a congenital heart malformation that occurs in 1% - 2% of the general population [1]. BAV patients are at an increased risk of developing thoracic aortic aneurysm (TAA), which can lead to an aortic catastrophe such as aortic dissection or free rupture [2,3]. Surgical intervention is commonly recommended when the aneurysm reaches a maximum orthogonal diameter between 50-55 mm [4]. Though aortic surgery is becoming increasingly safe [4], it remains imperative to find a less invasive method of repairing TAAs and preventing rupture.

Despite the high prevalence of BAV, little is known about the underlying mechanisms that lead to the associated deadly complications. It has been suggested that tissue degeneration of the aorta caused by inefficient repair of the artery plays an important role in the development of TAA [5]. In order to more effectively treat TAA, it is pertinent to define the governing pathways of this tissue degeneration.

Progenitor cells have been shown to play an important role in tissue repair throughout the human body including vessel repair [5]. In previous studies, isolated pericyte cells located in small blood vessels have been shown to possess progenitor cell characteristics [6, 7]. The vasa vasorum, microvessels located in the adventitia of larger vessels, supply blood and nutrients to larger vessels such as the aorta, and can be considered analogous to the small blood vessels previously described. Preliminary studies by our group showed that the vasa vasorum contain coverage by pericytes [8]. If the role of pericytes in aortic tissue repair can

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be ascertained, a method of non-surgical intervention might be utilized to repair the aortic wall of TAA patients (nearly 50% of whom have BAV) [2]. We hypothesized that the vasa vasorum serves as a stem cell niche, harboring cells that exhibit the potential to differentiate into functionally-relevant blood vessel cell lineages and participate in neovascularization. In this study, we explored the differentiation capabilities of primary human adventitia-derived pericytes, and examined their ability to form vascular networks with human endothelial cells in vitro.

Pericyte Isolation Pericytes were isolated as previously described [8].

Briefly, the aortic adventitia was dissected away from the media and intima, and enzymatically digested to obtain a cell suspension. The cells were cultivated in basal growth medium (Dulbecco’s Modified Eagle media, 10% FBS, 1% penicillin/streptomycin (Invitrogen) (DMEM)) for approximately 2 weeks until the population reached >1 x 106 cells. Cells expressing the antigenic profile of pericytes (CD146+/CD90+/ CD56-/CD45-/CD34-/CD31-) were sorted through the use of multi-dimensional Fluorescence- Activated Cell Sorting (FACS) using a three-laser MoFlo high speed cell sorter (Beckman Coulter, University of Pittsburgh Cancer Institute Flow Cytometry Core Facility).

smooth muscle lineage progression, pericytes were cultured in medium supplemented with transforming growth factor beta(TGF-beta) 1(2ng/mL) and platelet derived growth factor (PDGF) (50 ng/mL) in commercial SMC media (Cell Applications Inc. San Diego, CA). All growth factors were purchased from R&D Systems (Minneapolis, MN). Cells were cultured in DMEM with 10% fetal bovine serum and 1% penicillin/streptomycin (Invitrogen) as a negative control. Media was replenished every second day.

Images were captured using phase contrast microscopy on a Nikon TE-2000-E inverted microscope (Nikon Corporation, Melville, NY) at 0, 2, 4, 8, 10, and 14 days of treatment using a CoolSNAP ES2 Monochrome 1394x1040 High Resolution Camera (Photometrics, Tucson, AZ) and NIS Elements Software 3.2 (Nikon).

After the 14 days of culture in the above conditions, total RNA was isolated and qualitative Polymerase Chain Reaction (qPCR) was performed to quantify gene expression of endothelial markers; von Willebrand’s Factor (vWF) (Invitrogen, Grand Island, NY; Taqman Assay ID: Hs00169795), an adhesion protein found in endothelial cells, and CD31 (Invitrogen; Taqman Assay ID: Hs00169777), a vessel growth protein, and smooth muscle markers; alpha smooth muscle actin (α-SMA) (Invitrogen; Taqman Assay ID: Hs00199489), a contractile protein specific to SMC, and calponin (Invitrogen; Taqman Assay ID: Hs00154543), a calcium binding protein found in SMC. A two-tailed Student’s t-test was then performed to determine differences in gene expression for each marker and a p < 0.05 was considered significant. In addition to gene expression analysis, the cultures were analyzed for lineage-specific markers using immunocytochemistry to detect expression of endothelial markers VWF (US Biological, Pittsburgh, PA) and CD31(Abcam, Cambridge, UK and Cell Signaling (Beverly, MA)) and smooth muscle markers α-SMA (Sigma, St. Louis, MO), calponin (Abcam), and myosin heavy chain (MHC) (Dako, Carpinteria, CA), a contractile protein seen in SMC.

Differentiation Assays Pericytes were cultured in basal growth conditions to near confluency. Cells were cultured in the presence of vascular endothelial growth factor (VEGF) (50 ng/mL) in endothelial cell basal growth media (Cell Applications Inc., San Diego, CA) to promote endothelial cell lineage progression. To promote

In vitro Endothelial Branching Assay Isolated human primary aortic adventitia-derived pericytes and commercially obtained HPAECs (Lonza, Basel, Switzerland) were co-cultured either on (2D, 25 x 103 cells/cell type/well) or in (3D 125 x 103 cells/cell type/well) 300 µL pre-gelled GFR-Matrigel (Corning, Tewksbury, Massachusetts) substrate in 24-well tissue

Materials and Methods

Patient Enrollment and Tissue Collection To investigate the biological mechanisms for BAV patients suffering from TAA (BAV-TAA), we actively maintain a tissue bank of prospectively collected aortic specimens from patients who are undergoing elective surgery for ascending aorta/aortic valve replacement. Patient specimens selected for this study were within 10 years of age (50-59) and within 5mm of aortic diameter (50mm-55mm). All specimens were harvested by a surgeon during elective surgery for aortic valve/aortic replacement with informed patient consent and Institutional Review Board approval.

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culture plates. Cells were maintained at a sub confluent state prior to the experiment. Single cultures of either pericytes or HPAECs (50 x 103cells/well for 2D, 25 x 104 cells/well for 3D) were seeded as controls. Cells were then incubated for 8 days at 37°C. Cells cultured on GFR-Matrigel were visualized using phase contrast microscopy on a Nikon TE-2000-E inverted microscope and images were captured on days 1, 2, 3, and 8 using a CoolSNAP ES2 Monochrome 1394x1040 High Resolution Camera and NIS Elements Software 3.2.

Results

Growth Factor Treated Pericytes Show Changes in Gene Expression and Morphology Pericytes cultured under SMC lineage-specific conditions adopted a spindle-like morphology when compared to non-treated pericytes. This spindle-like morphology is indicative of SMCs in a contractile phenotype (Figure 1C). Gene expression analysis showed an increase in calponin expression (Figure 2) and an increased trend in α-SMA expression (13.4 ± 6.9 fold, p=0.28). The immunocytochemical detection displayed a change in the distribution of α-SMA, and an increased in the number of calponin and MHCexpressing cells (Figure 3).

Figure 1. (A) Untreated pericytes after 14 days of treatment, (B) Endothelial differentiation treated pericytes after 14 days of treatment, (C) SMC differentiation treated pericytes after 14 days of treatment, (D) confirmed endothelial cells, (E) confirmed SMCs. All scale bars are 100µm.

Figure 2. Change in fold of calponin expression of SMC treated cells compared to untreated pericytes. Standard error bars are presented. Endothelial differentiated pericytes displayed an increase in calponin expression (p<0.05).

Figure 3. (A) Non-treated pericytes stained for α-SMA (red) and calponin (green). (B) SMC treated pericytes stained for α-SMA (red) and calponin (green). (C) Endothelial treated pericytes stained for α-SMA (red) and calponin (green). (D) Non-treated pericytes stained for MHC (red) and calponin (green). (E) SMC treated pericytes stained for MHC (red) and calponin green). (F) Non-treated pericytes stained for vWF (green) and CD31 (red). (G) Endothelial treated pericytes stained for vWF (green) and CD31 (red). Scale bar=100µm.

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Pericytes cultured under endothelial lineage-specific conditions revealed no change in vWF expression.

However, there was an increase in CD31 expression (p < 0.01) (Figure 4). There was no observed change in cell morphology for the endothelial differentiated pericytes (Figure 1B). There was a decrease in α-SMA and an increase in CD31 in over 95% of the pericytes observed through ICC (Figure 3).

FIGURE 5. (A) 2D HPAECs on Matrigel 2 days after incubation. (B) 3D Pericytes on Matrigel 3 days after incubation. (C) 2D Pericytes on Matrigel 2 days after incubation. (D) 2D 1:1 mixture of pericytes and HPAECs on Matrigel after 2 days of incubation. All scale bars are 100µm.

Discussion FIGURE 4. Change in fold of CD31 expression of endothelial treated cells compared to untreated pericytes. Standard error bars are presented. Endothelial differentiated pericytes displayed an increase in CD31 expression (p<0.01).

Pericytes Formed Spheroids and Co-localized with HPAECs on Matrigel For HPAECs cultured on Matrigel, cells displayed endothelial tube branching within the first 24 hours of incubation (Figure 5A). Pericytes alone formed spontaneous spheroids within 4-6 hr and then displayed endothelial-like sprouting with 5 days of seeding.

Whereas, pericytes co-cultured with HPAECs colocalized in a perivascular location. Furthermore, coculture of pericytes with HPAECs seemed to stabilize the endothelial network longer than HPAECs cultured alone. HPAECs showed more extensive branching after 2 days when compared to the pericytes alone and pericyte/HPAECs co-cultures (Figure 5C and 5D).

Cells cultured within Matrigel as a 3-D gel, showed less initial branching than the 2D culture, but began to show tube-like structures after 3 days in culture (Figure 5B).

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Pericytes increased expression of smooth muscle and endothelial lineage-specific markers when cultured in defined medium, and exhibited immature endotheliallike sprouting when cultured alone, or organized with endothelial cells on Matrigel substrates. The increase in CD31 gene and protein expression supports the hypothesis that aortic pericyte cells demonstrate the ability to differentiate into functionally relevant blood vessel cell lineages such as endothelial cells. CD31 is universally expressed in endothelial cells and is considered a classic marker for this lineage. The lack of increased vWF expression could be due to diversity in vWF expression among endothelial cells[8]. There was some patient-to-patient variability observed in responses to lineage-specific medium conditions.

With the capacity pericytes to increase expression of the hallmark endothelial marker CD31, it can be surmised that pericytes located in aortic vasa vasorum are similar in profile and function to pericytes located in other vascularized tissues.

The spindle-like morphology, up-regulation of calponin gene expression, the trend of increased α-SMA expression, and of calponin and SM-MHC+ cells following culture in SMC-defined medium indicate differentiation towards a contractile SMC phenotype that is an integral component to aortic wall tissue integrity. These findings support our

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hypothesis that adventitia-derived aortic pericyte cells are capable of differentiating into more mature cells of the SMC lineage. In future studies, an optimal time of treatment for specific cell lines could be found to more conclusively show the differentiation capabilities of pericytes. Future experiments will be focused on defining pericyte function in normal vs. diseased aorta as well as ascertaining their putative role in tissue regeneration.

The Matrigel-based assays revealed that both pericytes and HPAECs have the ability to form immature vascular networks. However with the HPAECs only forming a network for a relatively short amount of time, and the pericytes forming spheroids shortly after initial branching neither of the cell populations is capable of forming long-term vasculature. The ability of pericytes to support the immature endothelial vascular network is in agreement with prior studies that pericyte cells are an integral component to vascular maturation that is essential to tissue regeneration [5]. Ongoing experiments are focused on examining the impact of putative differences in normal versus diseased microenvironments on endothelial cell functions.

Future studies will also examine the importance of pericytes-endothelial cell interactions in homeostasis of the medial layer and consider the influence of aortic disease on these processes.

Conclusion

In total, our data reveal that pericytes possess the potential to differentiate into more mature cells of the smooth muscle lineage, can organize with branching endothelial cells and display endotheliallike sprouting from spontaneously-formed spheroid cultures. Ongoing studies in our group are focused on ascertaining differences in differentiation capabilities between diseased and non-diseased pericytes as well as between pericytes isolated from patients with tricuspid aortic valve and BAV.

Acknowledgements

Research reported in this publication was supported by the University of Pittsburgh Swanson School of Engineering (BWE), the Competitive Medical Research Fund of the UPMC Health System (JAP), and the National Heart, Lung and Blood Institute of the National Institutes of Health under Award Number R01HL109132 (TGG and JAP). The authors

acknowledge the assistance of Kristin Konopka and Julie Schreiber for IRB protocols and obtaining informed patient consent. We are grateful to our surgical colleagues Drs. Christian Bermudez, Jay Bhama, Forozan Navid, and Lawrence Wei of the Department of Cardiothoracic Surgery, University of Pittsburgh Medical Center for assistance with aortic specimen acquisition. We are also grateful to Ben Green and Jen Hill for tissue processing and technical assistance.

References

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Arteriosclerosis, Thrombosis, and Vascular Biology.

2013; 33:66-74 [4] Danyi P, Elefteriades JA, Jovin IS. Medical therapy of thoracic aortic aneurysms are we there yet?

Circulation. 2011; 124:1469-1476 Cenciarini et al. J Neurophysiol 95. 2733-2750, 2006.

[5] Shen Y, Hu X, Zou S, Wu D, Coselli JS, Lemaire SA. Stem cells in thoracic aortic aneurysms and dissections: potential contributors to aortic repair. The Annals of Thoracic Surgery. 2012; 93: 1524-1533 [6] Corselli M, Crisan M, Murray IR, West CC, Scholes J, Codrea F, Khan N, PĂŠault B. Identification of perivascular mesenchymal stromal/stem cells by flow cytometry. Cytometry. 2013; 83:714-720 [7] Crisan M, et al. A perivascular origin for mesenchymal stem cells in multiple human organs.

Cell Stem Cell. 2008; 3:301-313 [8] Green BR, Donnenberg VS, Meyer EM, Ellis BW, Donnenberg AD, Gleason TG, Phillippi JA. Phenotypic diversity of perivascular progenitor cells from human aorta. ISACB Biennial Meeting. April 2-5, 2014; Abstract

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Towards Structure-Cytotoxicity Correlations for Complex Engineered Nanomaterials Yutao Gong, Sharlee Mahoney, Thomas Richardson, Kimaya Padgaonkar, Suzanna Hinkle, Ipsita Banerjee, and Götz Veser Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

Nanomaterials are widely used across many technologies, but increasing evidence shows that they may have health and environmental effects due to novel or altered toxicity. Therefore, it is necessary to develop high-throughput and sensitive nanotoxicity detection methods. The present study is part of a larger project which aims to address the current lack of established correlations between nanomaterials characteristics and cellular toxicity. Specifically, the rate of settling, dissolution, and agglomeration—all of which serve as potential dose modifiers in toxicity assays—were evaluated for three different complex engineered nickel-silica nanomaterials (hollow Ni in SiO2 (hNi@SiO2), non-hollow Ni in SiO2 (nhNi@ SiO2) and Ni on SiO2 (Ni-SiO2), respectively). While all three Ni nanomaterials had virtually the same settling rate after 2 hours, dissolution and aggregation differed significantly, with Ni-SiO2 showing the lowest dissolution and hNi@SiO2 and nhNi@SiO2 the smallest nanoparticles aggregate sizes. The results are nonintuitive, i.e. they cannot be simply predicted a priori from the materials structure, and thus demonstrate the importance to conduct thorough physicochemical characterization of complex engineered Ni nanoparticles before toxicity evaluation.

Keywords: Nanoparticles; Characteristics; Aggregation, Dissolution, Sedimentation

Introduction

Nanomaterials are already widely used in different fields such as electronics, catalysis, and medical treatment. At the same time, there are increasing concerns regarding potential health and environmental effects resulting from this wide-spread use of nanomaterials due to the lack of standardized tests for toxicity evaluations [1, 2].

Due to their small size, nanomaterials are able to pass through the human body by inhalation, ingestion, and skin penetration [3, 4]. Additionally, their minute size 28

makes it easier for them to overcome cell barriers and react with intracellular structure and macromolecules [5]. For example, both metallic nickel nano- and fine particles induce a dose-related increase in cytotoxicity in JB6 cells after 24 h exposure [6].

To date, most of the (very limited number of) studies on the toxicity of nanoparticles are focused on individual nanoparticles. Yet, in technical application and consumer products, nanomaterials are typically used in embedded or supported configurations, socalled “complex engineered nanomaterials” (CENs).

The present study aims to address this gap by studying Ni and SiO2 complex engineered nanomaterials as model CEN structures. Ni itself is a toxic metal, while (amorphous) SiO2 is known to be largely nontoxic.

The goal of this project is to investigate the impact of different nanostructuring on settling, dissolution and agglomeration using three different prototypes of Ni/ SiO2 CENs: Nickel deposited on the exterior of SiO2 nanoparticles (denoted as Ni-SiO2), nickel embedded throughout porous SiO2 nanoparticles (nhNi@SiO2), and Ni encapsulated in a hollow core within a porous SiO2 shell (hNi@SiO2). All three processes are known to affect the toxicity of materials significantly. Settling has an important impact on the effective dose in toxicity studies. For 2-D toxicity tests (as most cell studies are), nanomaterials that exhibit more settling will have a higher effective dose than a more stable nanomaterials.

Dissolution results in the release of metal ions (as opposed to metal nanoparticles) in the media which are known for many metals (incl. Ni) to be toxic.

Agglomeration, finally, affects the effective diameter of the nanomaterial in solution and is hence critical for cellular uptake of the nanomaterials.

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Materials and Methods

Chemicals The test materials—hollow Ni in SiO2 (hNi@SiO2), non-hollow Ni in SiO2 (nhNi@SiO2) Ni on SiO2 (NiSiO2) and raw material SiO2—were synthesized using a modified Stöber method [7]. For SiO2 nanomaterials synthesis, 10g detergent, Brij 58 (Sigma-Aldrich), was added into 50 ml cyclohexane (Fischer Scientific 99.5%) and refluxed and stirred at 340 RPM and 50° C. When all Brij 58 was dissolved, 1.5 ml DI water was added. After 30 minutes, 3 ml ammonium hydroxide (Fisher Scientific 30%) was added dropwise and stirred for 1 hour. Next, 10 g tetraethyl orthosilicate (TEOS, Sigma-Aldrich) was added. Silica growth was allowed to occur for 2 hours. Finally, it was crashed out of solution using isopropanol, washed three times, dried, and then calcined at 500° C for 2 hours in air.

For non-hollow Ni in SiO2 synthesis, instead of adding DI water, 1.5 ml 1M Ni(NO3)2 (Sigma-Aldrich) was added, 5 g TEOS was used. Hollow Ni in SiO2 (hNi@ SiO2) synthesis followed similar procedures to nonhollow Ni. The difference is that 1.5 ml hydrazine was added after Ni(NO3)2 for one hour before the ammonia. For Ni on SiO2 (Ni-SiO2) synthesis, 0.6 g silica nanoparticles was dispersed in 15 ml of DI water by 10 minutes sonication. Next, Ni salt solution (0.4 g NiCl2 in 10 ml DI water) was added, and the mixture was again sonicated for 20 minutes. Ammonium hydroxide was then added dropwise until the pH of the solutions reached approximately 9.5. After mixing for 20 minutes, the material was washed with isopropanol three times. The nanomaterials were then calcined at 300° C for 2 hours in air. Nanomaterial particle size and morphology were characterized with transition electron microscopy (TEM, JEOL-2000FX electron microscope). Particle measurements of TEM images were done using ImageJ software [8]. In previous studies, Energy-dispersive X-ray spectroscopy (EDX) was used to measure the ratio of SiO2 and Ni in each nanomaterial. The porosity of the nanomaterials was analyzed Brunauer–Emmett–Teller (BET).

Settling rate Settling was measured via UV-vis spectroscopy. 4 mL solutions of 200 mg Ni/L dispersed in 3T3 medium (Dulbecco’s Modified Eagle Medium; DMEM) supplemented with 10% Fetal bovine serum (FBS) and 1% Penicillin streptomycin supplemented with 20 mM hepes) were made for each Ni nanomaterials. The

tubes were sonicated for 10 minutes for nanomaterials redispersion. 1 mL nanoparticles was transferred from the tube to a cuvette. 3T3 medium was used as a blank.

The range of wavelength for the UV-Vis spectroscopy was set to 200 nm- 600 nm. Absorbance was recorded multiple times over five hours.

Dissolution Dissolution was measured via inductively coupled plasma-optical emission spectrometry (ICP-OES).

Again, solutions of 200 mg Ni/L in 3T3 medium were made for each Ni nanoparticles. The nanoparticles were sonicated for 15 minutes. At 5 time points (0 hours, 4 hours, 24 hours, 48 hours, and 120 hours) the medium was transferred into a milipore filter (Amicon 10,000 molecular weight cut-off filters, ~3.1 nm) and centrifuged to filter out the Ni nanoparticles. The concentration of Ni ions of three Ni nanoparticles at different time spot was measured using ICP-OES (Thermo Electron Corporation iCAP6500 Duo Series ICP-OES Spectrometer). Standards were formulated from a stock standard solution (Fischer Scientific) with 3 wt. % HNO3 in deionized water to generate a standard curve.

Agglomeration Agglomeration was measured via dynamic light scattering. 4 ml solutions of 50 mg Ni/L in medium were sonicated for 15 minutes. The agglomeration was then measured using dynamic light scattering (DLS, Zetasizer Series Nano-ZS) after 24 hours. 24 hours is selected as a time point to stay consistent with cell experiments (conducted in parallel and not discussed in this manuscript) that use a 24-hour exposure period. The refractive index of silica was used for all measurements.

Results

As seen in the Figure 1, three Ni and SiO2 complex engineered nanomaterials were successfully synthesized (hNi@SiO2, nhNi@SiO2 and Ni-SiO2).

For hNi@SiO2, Ni nanoparticles were encapsulated in silica in a hollow core. For nhNi@SiO2, Ni nanoparticles were embedded within silica. For NiSiO2, Ni nanoparticles were deposited on the surface of silica. It is important to note that in all cases the silica structure itself is highly porous, thus allowing access to the embedded Ni nanoparticles. The total diameter of Ni and SiO2 complex engineered nanomaterials is

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27 to 50 nm and Ni nanoparticles have a diameter of 2 to 3 nm. According to the EDX results, these three nanomaterials have nearly identical SiO2-to-Ni ratios (8-10wt% Ni) and BET measurements indicate similar porosity. Thus all the nanomaterials are made of similar building blocks, but only differ in the placement of the nickel nanoparticles.

Results As seen in the Figure 1, three Ni and SiO2 complex engineered nanomaterials were successfully synthesized (hNi@SiO2, nhNi@SiO2 and Ni-SiO2).

For hNi@SiO2, Ni nanoparticles were encapsulated in silica in a hollow core. For nhNi@SiO2, Ni

nanoparticles were embedded within silica. For NiSiO2, Ni nanoparticles were deposited on the surface of silica. It is important to note that in all cases the silica structure itself is highly porous, thus allowing access to the embedded Ni nanoparticles. The total diameter of Ni and SiO2 complex engineered nanomaterials is 27 to 50 nm and Ni nanoparticles have a diameter of 2 to 3 nm. According to the EDX results, these three nanomaterials have nearly identical SiO2-to-Ni ratios (8-10wt% Ni) and BET measurements indicate similar porosity. Thus all the nanomaterials are made of similar building blocks, but only differ in the placement of the nickel nanoparticles.

Figure 1. TEM images of hNi@SiO2, nhNi@SiO2, SiO2, and Ni-SiO2, along with schematics of the nanomaterials. In the TEM images, silica appears as light grey, while Ni NP appear black.

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Since higher settling rate will lead to higher effective dosing on cells, UV-visible spectroscopy was used to study the settling rate of the nanomaterials for a high concentration of 200 mg Ni/L over five hours. The absorbance was normalized by dividing with the initial absorbance. As seen in Figure 2, Ni-SiO2 initially settled down slightly faster than the other two Ni nanoparticles.

However, after about 1-2 hours the settling rates for all three nanomaterials are similar.

Figure 2. The settling rate of 200 mg Ni/L nhNi@SiO2, hNi@ SiO2, and Ni-SiO2 over 5 hours using Uv-vis spectroscopy.

The absorbance (A) was normalized by dividing with the initial absorbance (A0). Ni-SiO2 initially settled down slightly faster than the other two Ni nanomaterials. But after 2 hours the settling rate is similar.

Ni ions released from Ni nanomaterials are a route of toxicity for nickel nanomaterials which connects nanomaterials toxicity with the known toxicity for the respective metal salts [9,10]. To investigate dissolution, ICP-OES was used to detect the dissolved nickel ions in 3T3 medium for the three nanomaterials. Figure 3 shows that Ni dissolved from all three Ni nanoparticles to some extent. Ni-SiO2 has the lowest dissolution with only around 6 mg Ni/L and exhibits no major changes in the dissolution rate over duration of the experiment.

In contrast, nhNi@SiO2 had the highest dissolution with 32 mg Ni/L and dissolved rapidly during the first 20 hours. hNi@SiO2 had a similar dissolution trend with nhNi@SiO2 and the final dissolution is 23 mg Ni/L. Agglomeration can drastically affect the nanomaterials cellular uptake, thus the nanomaterials agglomerate size was studied using DLS. As seen in figure 4A, all three Ni nanoparticles agglomerated to sizes around 1000 nm in the DMEM medium (without FBS).

Figure 3. The dissolution of 200 mg Ni/L nhNi@SiO2, hNi@SiO2, and Ni-SiO2 over 120 hours using ICP-AES. All three Ni nanoparticles were dissolved as Ni to some extent. Ni-SiO2 has the lowest dissolution. In contrast, nhNi@SiO2 had the highest dissolution and dissolved rapidly during the first 20 hours.

nhNi@SiO2 was the only nanomaterial with smaller nanoparticle agglomerates, approximately 200 nm.

When FBS was added, the medium itself showed large peaks between 10 nm and 100 nm (see figure 4B). All three Ni nanoparticles show aggregate sizes near 10,000 nm. Ni-SiO2 also had agglomerates of 1000 nm. hNi@ SiO2 and nhNi@SiO2 had both larger (700- 1000 nm) and smaller (50-300 nm) agglomerates.

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Figure 4. Agglomeration of nhNi@SiO2, hNi@SiO2, and Ni-SiO2 in DMEM measured via DLS (left graph). All three Ni nanoparticles agglomerated in the size of 1000 nm in DMEM medium (without the presence of FBS). nhNi@SiO2 was the only nanomaterial with smaller nanoparticles agglomerate approximately 200 nm. Right graph: Agglomeration of nhNi@ SiO2, hNi@SiO2, and Ni-SiO2 in DMEM+FBS. All three Ni nanoparticles show aggregate sizes near 10,000 nm.Ni-SiO2 had only agglomerates of 1000 nm. hNi@SiO2 and nhNi@SiO2 had both larger and smaller agglomerates.

Discussion

Dosing is an important metric for evaluating nanotoxicity. Thus, it is important to consider the settling behavior of the nanomaterials and the dosing effect it has on the cells. In a typical cytotoxicity assay, live 3T3 fibroblast cells adhere to the bottom of the plate, thus experiencing a higher effective exposure to nanoparticles that settle more and faster. While there was slight settling rate difference over the first 2 hours for these three Ni nanoparticles, the settling rate was similar after this initial time. The agreement in longterm settling rates can be explained by due to similar nanomaterials porosity and Ni:SiO2 ratio. A possible explanation for the difference in initial settling rate could be the different external surface of the three materials, where the Ni-SiO2 has a largely Ni-covered external surface while the other two materials have a silica external surface. Silica is known to hydroxylate in aqueous environments, and the surface hydroxyls might be responsible for the initial stabilization of the materials in solution. However, the difference is small and only present for the initial phase, and one should hence expect that the settling rate will not be an important factor to cause toxicity differences within the three Ni nanoparticles.

Ni ions are a known toxic to human tissues [11].

Therefore, a higher dissolution of Ni ions is likely to lead to higher toxicity to cells. The fact that Ni-SiO2 showed the lowest dissolution is surprising at first, 32

since one would expect that the material with the most direct exposure of Ni to the solution would show the highest dissolution. However, the formation of a protein corona on the nanomaterials surface can cap the Ni particles on the surface of the SiO2, stabilizing them and limiting dissolution [12]. For the other two nanomaterials, the porosity of the silica shells is too small (<1 nm) to allow penetration by proteins. The nickel nanoparticles are hence “protected” by the silica support against protein capping and thus dissolve into the medium. Hence, if toxicity was based solely on the nanomaterials dissolution into the medium, one would expect the nhNi@SiO2 to be more toxic followed by hNi@SiO2 and lastly Ni-SiO2.

An average size for the mouse 3T3 cells is 15-20 microns in diameter for a suspended cell and optimal nanomaterial uptake size is 25 to 50 nm. Nanomaterials tend to aggregate to form larger particles in medium supplemented with FBS because FBS forms protein coronas around the nanomaterials aggregates, stabilizing aggregates with ~ 10 to 100 individual nanoparticles. Based on the results of agglomeration, nhNi@SiO2 and hNi@SiO2 had smaller aggregate sizes compared to Ni-SiO2. The smaller aggregates will be more readily absorbed by cells and result in increased intracellular Ni ions release. Since Ni-SiO2 had more aggregates in the size of ~10,000 nm—which is comparable to the diameter of 3t3 cells—it is likely to cause least nanoparticle uptake.

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Conclusions

Nanomaterials exhibit a unique toxicity due to their small size. For complex engineered nanomaterials, this toxicity can be correlated with the different physiochemical properties resulting from the different nanostructure of these materials. Thus it is critical to study the characteristics, including dissolution, agglomeration, and settling. The results show that the three Ni and SiO2 complex engineered nanomaterials show significant differences in dissolution and agglomeration behavior based on their respective structures. This suggests that they are likely to show different cytotoxicity based on their structure. In the future, these results will be used to directly correlate the physicochemical properties with cell toxicity studies to allow for derivation of structure toxicity correlations.

Acknowledgements

This work was supported by the Swanson School of Engineering.

References

[1] Nel, A, Xia, T, Mädler, L, Li, N. (2006). “Toxic potential of materials at the nanolevel.” Science 311(5761): 622-627.

[2] Oberdoester, G. (2010). “Nanotoxicology: An Emerging Discipline Evolving from Studies of Ultrafine Particles (vol 113, pg 23, 2005).” Environmental Health Perspectives 118(9): A380-A380.

[3] Y Yang, T Lin, XL Weng, JA Darr, XZ Wang. (2011).

“Data flow modeling, data mining and QSAR in highthroughput discovery of functional nanomaterials.”

Computers & Chemical Engineering 35(4): 671-678.

[4] Carbo-Dorca, R. and E. Besalu (2011).

“Construction of coherent nano quantitative structure– properties relationships (nano-QSPR) models and catastrophe theory.” SAR and QSAR in Environmental Research 22(7-8): 661-665.

[5] S. Shahriar, S. Behzadi, S. Laurent, M. L. Forrest, P. Stroeve, and M. Mahmoudi. “Toxicity of nanomaterials.” Chem Soc Rev 41 (2012): 2323-2343.

[6] Zhao J, Bowman L, Zhang X, Shi X, Jiang B, Castranova V, Ding M. Metallic nickel nano-and fine particles induce JB6 cell apoptosis through a caspase-8/AIF mediated cytochrome c-independent pathway. J nanobiotechnology. 2009;7(2).

[7] Stöber, Werner, Arthur Fink, and Ernst Bohn.

“Controlled growth of monodisperse silica spheres in the micron size range.” Journal of colloid and interface science 26.1 (1968): 62-69 [8] “ImageJ.” ImageJ. NIH. http://rsb.info.nih.gov/ij/, last accessed on 03/03/2015.

[9] Ispas C, Andreescu D, Patel A, Goia DV, Andreescu S, Wallace KN. Toxicity and Developmental Defects of Different Sizes and Shape Nickel Nanoparticles in Zebrafish. Environmental Science & Technology.

2009;43(16):6349-56. doi: 10.1021/es9010543.

PubMed PMID: WOS:000268907700041.

[10] Griffitt RJ, Luo J, Gao J, Bonzongo JC, Barber DS. Effects of particle composition and species on toxicity of metallic nanomaterials in aquatic organisms. Environmental Toxicology and Chemistry.

2008;27(9):1972-8.

[11] Stohs, S. and D. Bagchi (1995). “Oxidative mechanisms in the toxicity of metal ions.” Free Radical Biology and Medicine 18(2): 321-336.

[12] Lundqvist, M., Stigler, J., Elia, G., Lynch, I., Cedervall, T., & Dawson, K. A.. “Nanoparticle size and surface properties determine the protein corona with possible implications for biological impacts.”

Proceedings of the National Academy of Sciences 105.38 (2008): 14265-14270.

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Determination of the Hydrodynamic and Mass Transfer Parameters in a Pilot-Scale Slurry Bubble Column Reactor for Fischer-Tropsch Synthesis Yemin Hong, Omar Basha, Laurent Sehabiague, and Badie I. Morsi Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

The hydrodynamic and mass transfer parameters (gas holdup, εG, Sauter-mean gas bubbles, diameter, d32, and volumetric liquid-side mass transfer coefficient, kLa) were measured for N2 and N2/He gaseous mixtures in a molten Fischer-Tropsch (F-T) reactor wax produced from an F-T plant by the National Institute of Cleanand-low-carbon Energy (NICE), China. The data were obtained in the pilot-scale (0.29-m ID, 3-m height) slurry bubble column reactor (SBCR) available at the Reactor and Process Engineering Laboratory (RAPEL), University of Pittsburgh. The data were obtained under different pressures (1.4-3.1 MPa), temperatures (430490 K), and superficial gas velocities (0.15-0.3 m/s) in the presence of different solid (iron-based catalyst) concentrations (0-15 vol. %).

The hydrodynamic and mass transfer data obtained showed that increasing reactor pressure increased εG and decreased d32 which led to high values of kLa due to the increase of the gas-phase density and subsequently its momentum. Increasing temperature increased εG and decreased d32 also resulting in high kLa value due mainly to the decrease of the liquid-phase viscosity and surface tension. The εG values increased, while d32 values decreased or remained unaffected by increasing the superficial gas velocity. Also, increasing the helium mole fraction in the He/N2 mixtures decreased εG and increased d32 due to the decrease of the gas-phase density and subsequently, its momentum.

Keywords: Slurry Bubble Column, Fischer-Tropsch, Hydrodynamics, Mass Transfer

Introduction

Fischer-Tropsch (F-T) synthesis provides a pathway for converting carbon-bearing resources, such as natural gas, coal, heavy residue, biomass, municipal 34

waste, etc., into liquid fuels and high value chemicals.

Initially called “Synthol”, the F-T synthesis was developed in the 1920s in Germany at the Kaiser Wilhelm Institute by two Germans, Franz Fischer and Hans Tropsch [1, 2], with the intent of producing synthetic hydrocarbons. The overall F-T synthesis process involves three main steps: syngas generation, F-T catalytic reactions, and product upgrading. Syngas generation involves converting the carbonaceous feedstock (natural gas, coal, biomass, municipal waste, etc.) into a H2-CO mixture (synthesis gas or syngas) via reactions with steam and air or oxygen. Solid feedstocks, such as coal and biomass, are converted in a gasifier, of which various types have been already used in industrial applications [3]. Natural gas, however, is converted to syngas in a reformer using either partial oxidation (POX), steam methane reforming (SMR), or auto-thermal reforming (ATR).

In F-T synthesis, the syngas reacts in the presence of a heterogeneous catalyst to produce a wide range of hydrocarbon products (Equation (1)), primarily linear alkanes and alkenes. Although many metals have been identified to catalyze the F-T reactions, only iron (Fe) and cobalt (Co) have been used in industrial applications [3]. Fe catalyst is cheap and has a high water-gas-shift (WGS) activity. However, Fe catalyst is prone to severe attrition and water deactivation [1]. Co-based catalyst, on the other hand, has higher activity since it is not strongly inhibited by water, and is more resistant to attrition. Co-based catalyst, however, is more expensive and has no WGS activity [1]. Fe catalyst has a high affinity for the WGS reaction as shown in Equation (2), resulting in the conversion of a significant portion of the oxygen from CO dissociation into CO2.

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Thus, the extent of the WGS reaction has to be closely considered as it affects the H2/CO ratio in the F-T process. In the case of gas-to-liquid (GTL) applications, the produced syngas is highly rich in H2 and any additional H2 via the WGS reaction is undesirable. In contrast, carbon-rich feedstocks, such as coal or biomass, produce a CO-rich syngas; and therefore require an extent of WGS in the F-T reactor. In 2013, the US proven oil reserves were 44.2 billion barrel; and oil production, consumption and net imports were 10.0, 18.9 and 7.7 million bbl/day, respectively, with over 90% of the oil produced being used in the transportation sector. The US oil imports were mainly from Canada, Mexico, Venezuela, Nigeria and the Middle East. It is a harsh fact that our oil imports could be abruptly altered, threatening our national security, if the ongoing turmoil in the Middle East and the civil war in Nigeria persist. On the other hand, in 2013, the US had huge natural gas reserves of 330 trillion ft3 and coal reserves totaling 237 billion tons, representing 26.6% of the total world’s proved reserve. Industrial GTL plants, such as the Oryx by Sasol and Pearl by Shell both in Qatar use mostly cobalt-supported catalyst, whereas industrial coal-toliquid (CTL) plants usually use Fe-based catalysts, such as the Sasol Synfuels complex in Secunda, South Africa and the planned CTL plants in China [4, 5].

Recently, increasing attention has been given to slurry bubble column reactors (SBCRs) to carry out the F-T synthesis. They provide a better temperature control and heat removal, and lower capital cost than fixed bed reactors. However SBCRs inherit some drawbacks, such as; challenging catalyst separation from the heavy products; and complex hydrodynamics.

The overall objective of our project is to measure the hydrodynamic and gas-liquid-solid mass transfer parameters for He and N2 as surrogates for H2 and CO in an F-T reactor wax produced in an F-T industrial plant in China. These parameters will be obtained under typical F-T conditions; and accordingly they could be directly used without any correction for the design, modeling, scaleup and optimization of industrial-scale SBCRs.

Experimental

Experimental Setup Wilkinson et al. [6] reported that in order to avoid wall effects and obtain representative data for scale-up

purposes, the hydrodynamic data should be obtained in reactors with ID > 0.15 m, since under this condition, the gas holdup was found, in several cases, to be independent of the column diameter. In this study, the SBCR used has an ID of 0.29 m; and therefore the wall effects should be minimal and the data obtained could be used for scale-up purposes. The details of the SBCR used in this study can be found elsewhere [4,6] and photographs of the experimental setup are shown in Figure 1.

Figure 1. SBCR at the Reactor and Process Engineering Laboratory (RAPEL)

The gases used in the experiments were He and N2 as surrogates for H2 and CO, respectively. It should be mentioned that CO and N2 gases, not only have identical molecular weights, but also found to exhibit almost identical solubilities and mass transfer coefficients in different FT cuts [7]. The liquid-phase used in the experiments was molten NICE reactor wax. The wax has a melting point of > 90 ºC and primarily consists of saturated linear paraffins and its composition in weight fraction are provided elsewhere [4,6]. The solid-phase used in experiments was an iron catalyst provided

by NICE with a skeletal density of 3,380 kg/m3 and average particle diameter of 81 μm.

Measurement and Calculation of the Hydrodynamic and Mass Transfer Parameters The hydrodynamic and mass transfer parameters were measured for N2 and various He/N2 gaseous mixtures in the molten NICE reactor wax in the pilot-scale SBCR under different operating conditions typical to those of Low-Temperature Fischer-Tropsch (LTFT) process: P (4-31 bar), T(380-500 K), UG (0.1-0.3 m/s) and CS (15 vol.%). The Transient Physical Gas Absorption technique (TPGA) was employed to obtain the overall volumetric liquid-side mass transfer coefficients (kLa)

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of the gases in the liquid-phase or slurry-phase. The manometric (hydrostatic head) method was used to obtain the total gas holdup. Also, the Dynamic Gas Disengagement (DGD) technique was used to obtain the bubble size distribution and the Sauter-mean bubble diameters of gas bubbles. The experimental procedures followed were identical to those detailed in previous publications [8, 9].

The kLa, εG and d32 values were calculated using Equations (3) through (5) and additional details can be found elsewhere [8, 9].

Results and Discussion Gas Hold-up (εG)

Figure 2 indicates that in the absence and presence of solid particles up to15 vol. %, the gas holdup for N2 in NICE molten reactor wax appeared to increase with temperature, which is in accord with other literature data [8, 9]. This behavior was attributed to the decrease of the liquid-phase viscosity and surface tension with

increasing temperature which led to high gas holdup values.

Moreover, the gas holdup values were found to increase with reactor pressure, which is in agreement with other reported findings [8, 9]. This behavior was due to the increase of the gas density and the gasphase momentum, which led to the increase of the gas holdup. Similarly, the gas holdup of N2 in the NICE molten reactor wax appeared to increase with the superficial gas velocity due once again to the increase of the gas-phase momentum.

The gas holdup values were also found to increase with increasing the mole fraction of N2 or decreasing the mole faction of He in the gas mixture which is not shown in this figure. This decrease is in agreement with earlier findings for N2, He and He/N2 mixtures in an isoparaffinic mixture (Isopar-M) and in 3 different F-T liquids [8, 9]. The presence of the heavier gas (N2) increases the density and thus the momentum of the gaseous mixture, which led to the increase of the gas

Table 1: Equations for Calculating the Hydrodynamic and Mass Transfer Parameters

Figure 2. Effect of Temperature, Gas Velocity, Pressure, and Solid Concentration on εG

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holdup and to the formation of smaller gas bubbles, which resulted in a greater gas-liquid interfacial area and consequently kLa. Indeed, kLa values increased with increasing the mole fraction of N2 in the gas mixture, with the highest kLa values observed for N2 as a single component.

Sauter-mean Bubble Diameter (d32) Figure 3 shows that the values of the Sauter mean bubble diameters decrease with increasing temperature, which can be attributed to the decrease of the liquid viscosity and surface tension with increasing temperature, leading to the formation of small gas bubbles [8].

Figure 3 also shows that increasing the temperature from 390 K to 450 K appears to shift the gas bubbles distribution towards small gas bubbles < 3 mm.

Furthermore, Figure 3 indicates that the superficial gas velocity and pressure appeared to have insignificant effect on the Sauter-mean gas bubble diameter for N2 in NICE wax. It seems that the addition of solid particles prevented the breakup of gas bubbles.

Volumetric Mass Transfer Coefficient (kLa) Figure 4 shows that in the absence and presence of solid

particles, the volumetric mass transfer coefficients values for N2 and He/N2 mixtures in NICE molten wax increase with increasing temperature. In the absence of solid particles, the kLa values appear to level off after 440 K where the minimum Sauter mean bubble diameter was reached. Also, at similar temperatures, kLa values for the same gas-liquid system decrease with increasing solid concentration, which is in agreement with other findings [9, 10]. This behavior can be related the decrease of the gas holdup and the increase of the Sauter mean bubble diameter. The kLa for N2 in NICE molten wax, with and without solid particles, also appeared to increase with increasing pressure, which was due to the increase of the gas holdup and the decrease of the Sauter mean gas bubble diameter, which led to the increase of the gas-liquid interfacial area (a). Moreover, as expected, kLa for N2 in NICE molten reactor wax always increased with increasing the superficial gas velocity, which was attributed to an increase of the gas holdup and turbulences, which increased the gas-liquid interfacial area (a) and the mass transfer coefficient (kL), respectively.

Figure 3. Effect of of Temperature, Gas Velocity, Pressure, and Solid Concentration on d32

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Correlation of the Experimental Data

The experimental data were used to update the correlations previously by Sehabiague and Morsi [9] for predicting , kLa and . The updated correlations are given in Table 2.

Conclusions

The hydrodynamic and mass transfer parameters for N2 and He/N2 gaseous mixtures were measured in NICE molten reactor wax in the presence of catalyst concentrations of up to 15 vol% using a pilot-scale (0.29-m ID, 3-m Height) SBCR. The effects of pressure, temperature, superficial gas velocity, and gas composition on these parameters were investigated and their behaviors were found to be mostly similar

to those reported in the literature for other FT liquids.

The data obtained showed that increasing reactor pressure increased εG and decreased d32 which led to high values of kLa due to the increase of the gas-phase density and subsequently its momentum. Increasing temperature increased εG and decreased d32 also resulting in high kLa value due mainly to the decrease of the liquid-phase viscosity and surface tension. The εG values increased, while d32 values decreased or remained unaffected by increasing the superficial gas velocity. Also, increasing the helium mole fraction in the He/N2 mixtures decreased εG and increased d32 due to the decrease of the gas-phase density and subsequently, its momentum.

Figure 4. Effect of Temperature and Solid Concentration on kLa Table 2. Correlations for predicting the hydrodynamic and mass transfer parameters in SBCRs

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Acknowledgements

The Swanson School of Engineering at the University of Pittsburgh is gratefully acknowledged for providing the funds for this opportunity. The Authors would also like to thank NICE, China, for the financial support and for providing the catalyst particles and reactor wax.

Nomenclature

C Concentration, mol.m-3 P Pressure, Pa C* Equilibrium concentration

(solubility), mol.m-3 PV Vapor pressure, Pa cV Volumetric concentration of

solid particles in the slurry R Ideal gas constant,

phase, vol.% 8.314 J.mol-1.K-1 db Gas bubbles diameter, m t Time, s d32 Sauter-mean gas bubbles T Temperature, K

diameter, m UG Superficial gas velocity, Di Diffusivity of component i m.s-1 in wax, m2.s-1 V Volume, m3 dR Reactor diameter, m X Molar fraction, G Acceleration due to gravity, Z Compressibility factor, 9.81 m s-2 kLa Volumetric liquid-side mass

transfer coefficient, s-1 MWi Molecular weight of

species i, kg/mol Greek Letters εG Gas holdup, - Ω Cross section area, m2 μ Viscosity, Pa.s

ρ Density, kg/m3 σ Surface tension, N/m

References

[1] M. E. Dry, “The Fischer-Tropsch process: 19502000,” Catalysis Today, vol. 71, pp. 227-241, 2002.10.1016/s0920-5861(01)00453-9 [2] A. Steynberg and M. Dry, Fischer-Tropsch Technology: Elsevier Science, 2004 [3] F. G. Botes, J. W. Niemantsverdriet, and J. van de Loosdrecht, “A comparison of cobalt and iron based slurry phase Fischer–Tropsch synthesis,” Catalysis Today, vol. 215, pp. 112-120, 2013.http://dx.doi. org/10.1016/j.cattod.2013.01.013 [4] Z. Liu, S. Shi, and Y. Li, “Coal liquefaction technologies—Development in China and challenges in chemical reaction engineering,” Chemical Engineering Science, vol. 65, pp. 12-17, 2010.10.1016/j.ces.2009.05.014 [5] A. de Klerk, Fischer-Tropsch Refining. Weinheim: Wiley-VCH Verlag & Co. KGaA, 2012 [6] P. M. Wilkinson, A. P. Spek, and L. L. van Dierendonck, “Design parameters estimation for scaleup of high-pressure bubble columns,” AIChE Journal, vol. 38, pp. 544-554, 1992.10.1002/aic.690380408 [7] Y. Rakymkul, “Solubilities and Mass Transfer Coefficients of Gases in Heavy Synthetic Hydrocarbon Liquids,” M.Sc., University of Pittsburgh, Pittsburgh, 2011 [8] A. Behkish, R. Lemoine, L. Sehabiague, R. Oukaci, and B. I. Morsi, “Gas holdup and bubble size behavior in a large-scale slurry bubble column reactor operating with an organic liquid under elevated pressures and temperatures,” Chemical Engineering Journal, vol.

128, pp. 69-84, 2007.http://dx.doi.org/10.1016/j. cej.2006.10.016 [9] L. Sehabiague and B. I. Morsi, “Hydrodynamic and Mass Transfer Characteristics in a Large-Scale Slurry Bubble Column Reactor for Gas Mixtures in Actual Fischer–Tropsch Cuts,” International Journal of Chemical Reactor Engineering, vol. 11, pp. 1-20, 2013.http://dx.doi.org/10.1515/ijcre-2012-0042 [10] A. Behkish, Z. Men, J. R. Inga, and B. I. Morsi, “Mass transfer characteristics in a large-scale slurry bubble column reactor with organic liquid mixtures,” Chemical Engineering Science, vol. 57, pp. 3307-3324, 2002.http://dx.doi.org/10.1016/S00092509(02)00201-4

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Dynamic Reactor Simulations of Chemical Looping Combustion Jonathan Hughes1,2 and Götz Veser1,2 Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA 2 Mascaro Center for Sustainable Innovation, University of Pittsburgh, Pittsburgh, PA, USA gveser@pitt.edu 1

Abstract

Traditional combustion of natural gas to generate energy produces a mixture of carbon dioxide and air, resulting in atmospheric carbon emissions. Postcombustion separation of CO2 to reduce emissions is costly in terms of capital and energy use. Chemical Looping Combustion (CLC) provides inherent separation of CO2 by combusting the fuel with a metal oxide instead of air, facilitating carbon sequestration.

The complexity of this process can be better understood through dynamic reactor simulations of the conversion of methane to combustion products. This work develops a computational model of a periodicallyoperated fixed-bed reactor for chemical looping combustion and demonstrates a good qualitative and quantitative fit between simulation and empirical data.

is passed over the metal oxide at temperatures between 600-1000°C [4,5]. The reduced metal is then reoxidized with air, either in a separate reactor (Figure 1), or after purging of the fuel gases in the same unit. The metal “loops” between oxidized and reduced states, acting as an oxygen carrier across a spatial or temporal separation between the air and the combustion phase.

Keywords: Chemical looping, kinetic modeling, periodically operated fixed-bed, nickel catalyst

Introduction

Growing carbon dioxide levels in the atmosphere and associated environmental impacts create a societal demand for reducing carbon emissions [1].

Fossil fuels remain a major component of the energy economy, supplying more than 60% of the United States’ electricity demands with little change predicted for at least several decades [2]. Carbon capture and sequestration has the potential to virtually eliminate carbon emissions from fossil fuels, but existing post-combustion separation technologies are energy intensive and uneconomical[3].

Chemical Looping Combustion (CLC) is a promising alternative which provides inherent separation of CO2 and air within the combustion process. CLC utilizes a metal oxide, such as NiO, in place of air as the oxygen source for combustion, eliminating the need for separation of incondensable gases. During the combustion phase, a gaseous fuel, typically methane, 40

Figure 1. Schematic of Chemical Looping Combustion

To better understand and optimize CLC operation, several computational models have been developed, commonly using nickel oxide as the oxygen carrier.

The combustion kinetics are often based on surface availability of oxide, although other models, including shrinking-core models, have been developed [5-7].

In particular, a kinetic model proposed by Iliuta et al.

[5] provides fair agreement with experimental data by supposing that the methane reaction with nickel oxide is a surface reaction catalyzed by pure (reduced) nickel.

However, it does not adequately reflect experimental data by Bhavsar and Veser [4]. The Iliuta et al. mechanism significantly over-predicts combustion products, and often encounters computational issues at higher temperatures ascribed to this same tendency to over-predict rates of combustion. This suggests the need for further improvements to the combustion

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reaction mechanism. The present work introduces a Ni-CH4 adsorption step into the methane combustion mechanism, building on the assumption of Nicatalyzed combustion to bring the model predictions into closer agreement with experimental data.

Methods

Our fixed-bed model of the fuel combustion phase in CLC considers 11 reactions, including four combustion reactions between gaseous species and the solid oxide, as well as seven catalytic reactions between gas-phase species. The computational model is set up as a system of partial differential equations describing the concentration of each species along a discretized grid across the reactor. The spatial derivatives are calculated with the finite difference method, and the resulting differential equations in time are integrated with the MatLab (2014) built-in ode15s solver.

To assess the robustness of our model in predicting reactor products at varying temperatures, experimental data were obtained by operating our reactor at 700째C with a NiOAl2O3 oxygen carrier under conditions otherwise identical to those described in section 4.2 of Bhavsar and Veser [4].

Combustion Reactions Methane combustion with nickel oxide is assumed to take place step-wise, with CH4 first reacting to form hydrogen and either carbon dioxide or carbon monoxide (r1 and r2), then CO and H2 further react with NiO to form CO2 and H2O (r3 and r4). Tables 1 and 2 contain the combustion rate expressions and parameters used in this model, where KCH4 is the Ni-CH4 adsorption coefficient, fNi is the mass of pure nickel per kg of oxygen carrier (so-called metal weight loading), and fNiO is the mass of nickel oxide per kg of oxygen carrier.

The combustion reactions include a surface area term, ST(1-XNiO), which describes the conversion of surface NiO sites into reduced Ni [5]. To better explain the methane breakthrough curve that has been commonly observed throughout the literature [5,6], Iliuta et al. included a nickel concentration term into the methane combustion rates [5], postulating that the reduced nickel acts is required as a catalyst for this reaction. Our model builds on this proposed mechanism by introducing a Ni-CH4 adsorption step into the combustion reaction mechanism. This aims to reduce overall methane combustion rate within the model and thus better reflect the exit concentrations of combustion products observed in the experimental data.

Catalytic Reactions As the reaction proceeds, the NiO oxygen carrier is reduced to pure nickel, which is a catalyst for multiple reactions between the gas-phase species [8-10]. Our model accounts for seven gas-phase reactions including steam and dry reforming, water gas shift, methane cracking, and coke gasification. The reaction rates are written according to Langmuir-Hinshelwood-HougenWatson (LHHW) kinetics, which consider adsorption and desorption from the catalyst active sites. The specific rate laws, drawn from Iliuta et al., are shown in Table 3, with accompanying parameters in Tables 4 and 5 [5]. The modification described in section 2.1, while improving the overall fit of model predictions, decreased fit in CH4 and CO. To address these issues, the kinetic rate expression for methane cracking used in [5] was substituted with one derived in Snoeck et al. [9] to provide a better fit to the coking rate observed in experimental data, and the pre-exponential factors for the methanation and steam gasification reactions, k0r7 and k0r10, were decreased by 70% to improve the fit of the predicted CO concentration.

Table 1. Combustion Reactions [5]

Table 2. Combustion Reaction Parameters [5]

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Table 3. Catalytic Reactions

Table 4. Ni-catalyzed Reaction Kinetic Parameters

Table 5. Adsorption Coefficient Parameters

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Mass Transport The flow of gases down the length of the reactor is modeled with convection and diffusion. Plug-flow conditions were assumed due to the rapid dispersion of gases in the radial direction (Pe = 10.22) [7,12].

The nickel-oxide particles used by Bhavsar and Veser (2013), and within the experiments conducted in this work, have a maximum diameter of 30 nm [4], so that intra-particle diffusion limitations were assumed to be negligible in this model.

(a)

Results

Simulation and Experimental Results The model predictions of the Iliuta et al. mechanism and our model at T=700°C are shown side-by-side in Figures 2 and 3, plotted in solid lines. Experimental data collected at an operating temperature of 700°C is plotted in dashed lines on both graphs for comparison.

Figure 3 shows the results from Iliuta et al. (3a) and our model (3b) compared to the data collected in Bhavsar and Veser operating at T=800°C [4].

(b)

Figure 2. Comparison of experimental exit gas composition (dotted) and model predictions (solid lines) versus time at 700°C. (a) Iliuta mechanism ([5]) and (b) current proposed mechanism

(a)

(b)

Figure 3. Comparison of experimental exit gas composition (dotted; Bhavsar and Veser [4]) and model predictions versus time at 800°C. (a) Iliuta mechanism ([5]) and (b) current proposed

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44

Computational modeling allows an exploration of conditions inside the reactor which can be difficult or impossible to measure experimentally. Figure 4 shows the conversion of NiO to reduced Ni over time at each point in the reactor as the reaction front moves down the reactor bed. Figure 5 shows the concentration of CO throughout the reactor over time, which is produced as an intermediate in conversion of methane to CO2, by the catalytic reactions occurring between gas-phase species, and by gasification of coke deposited on reduced nickel.

The overall rates of the combustion of methane to form CO and H2 (r2), and the catalytic steam gasification reaction (r10), are plotted over time during operation at 700째C in Figure 6, and at 800째C in Figure 7.

Figure 4. Oxygen carrier (NiO) conversion throughout the reactor over time.

Figure 5. Carbon monoxide concentration throughout the reactor over time.

Figure 6. Overall reaction rates for partial methane combustion and steam gasification of carbon at 700째C.

Figure 7. Overall reaction rates for partial methane combustion and steam gasification of carbon at 800째C.

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Statistical comparison of models A direct quantitative comparison of the accuracy of the Iliuta et al. mechanism and our model was performed by calculating the mean relative squared error for each species concentration in the reactor effluent at 700째C. Figure 8 graphs the relative squared error versus time for both models in CO2 effluent at 700째C. Sensitivity analysis To identify the key operating parameters which influence reactor performance, as well as to understand the crucial kinetic expressions governing the model, a local sensitivity analysis of all parameters was performed. Each model parameter in turn was increased by 1%, a simulation was run and the maximum concentration of each species in the reactor effluent during each run was recorded. Sensitivity was computed by taking the log of the peak concentration of a species (in this work, CO), at the adjusted parameter value over the maximum exit concentration under the standard parameter values:

Figure 8. Relative squared error of the Iliuta et al. model (blue, dashed) and our model (black, solid) in predicting the CO2 concentration in the reactor effluent stream at 700째C.

where C is the peak concentration after adjusting the parameter value being analyzed for sensitivity, and C0 is the peak concentration with the standard parameter values.

Figure 9 shows the sensitivity of the model to selected operating parameters as well as physical parameters of the oxygen carrier. Sensitivity analysis of the kinetic parameters is not shown due to space limitations.

Discussion

Model comparison The improvements introduced in this work result in a model which captures the experimental data very well across a range of reactor operating temperatures.

The proposed model shows significant quantitative improvement in predicting the effluence of CO2 over time (Figure 6), as expected from the introduction of a Ni-CH4 adsorption term to the combustion reactions r1 and r2. Overall, there is significant improvement in the model accuracy, with the exception of predicted CO

Figure 9. Sensitivity of our model to selected operating parameters.

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concentration (Table 6), where the proposed model overestimates carbon monoxide production (Figure 2). The relative sluggishness in the transition from water to hydrogen production of our model simulation may be due to the assumption of rapid intra-particle diffusion; in a diffusion-limited model, there will be less oxygen available to oxidize hydrogen at this stage of reactor operation.

The proposed model demonstrates an improved robustness in capturing the effects of temperature on the reaction network, as shown in Figure 3 where model predictions are compared with experimental data at an operating temperature of 800°C. While the original model by Iliuta et al. fails at t=300s, our model captures the experimental data well without computational issues. Table 7 shows that, as at 700°C, our model is generally superior with the exception of predicted CH4 and CO effluent, where the Iliuta et al. model is slightly more accurate. Potential routes for improving the fit of our CO predictions are discussed below.

Effects of temperature on reactor products In the experimental data, as well as the model described in this work, we observe that at 700°C, the reactor products show roughly symmetric peaks in the CO2 and H2O effluent, and very little CO is produced.

As the operating temperature is increased to 800°C, the total oxidation products CO2 and H2O reach their maxima rapidly, followed by a prolonged decline as the depletion of NiO causes partial oxidation products to appear in the exit gases. We also see a significant increase in the production of CO at higher temperatures experimentally, counter to the expectation that higher temperatures should favor total combustion. This is ascribed to the four-fold increase in the rate of steam gasification of deposited carbon (r10) between 700°C and 800°C. Figures 6 and 7 show that steam gasification of carbon becomes the dominant source of CO within our model after t=200s (34τ). As the simulation temperature is further increased to 900°C (not shown in this work), the kinetic rate expression for steam gasification leads to overprediction of CO production. The kinetics of steam gasification of coke thus warrant further investigation to improve the model accuracy at higher temperatures.

Table 6. Mean relative squared error between models and experimental data at T=700°C.

Table 7. Mean relative squared error between models and experimental data at T=800°C.

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Sensitivity analysis A sensitivity analysis of operating parameters in Figure 9 illuminates the density and void fraction of the oxygen carrier as being the two most significant parameters in reactor performance, while the specific surface area ST is seen to have a less pronounced impact on overall reactor performance. This suggests that further research and exploration of oxygen carriers should focus on optimization of the density and void fraction of the carrier.

References

Conclusions

[4] Bhavsar, S.; Veser, G. Reducible supports for Nibased oxygen carriers in chemical looping Combustion.

Energy and Fuels 27 (2013) 2073-84.

The kinetic model used in this work shows improved qualitative and quantitative agreement with the empirical data, particularly with the introduction of a methane-nickel adsorption term to the methane combustion reactions and the adjustment of methanation and steam gasification kinetic rates. The sensitivity analysis indicates that void fraction and density of the oxygen carrier are the most influential parameters and should be the subject of future experimental research.

Areas for further improvement of our computational model include transport limitations of intra-particle diffusion within the nickel oxide particles, energy balance and non-isothermal operation, and exploration of kinetic models for other types of oxygen carriers.

Acknowledgements

The authors would like to acknowledge Frank and Daphna Lederman for their financial support through the Mascaro Center for Sustainable Innovation.

This work was conducted as part of a development supported by the U.S. Department of Energy’s National Energy Technology Laboratory (through RDS contract DEFE0004000) and by the National Science Foundation (CBET #1159853).

[1] National Research Council, Advancing the Science of Climate Change, The National Academies Press, Washington, DC, 2010.

[2] Energy Information Administration, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2012, April 2014.

[3] International Energy Agency, Cost and Performance of Carbon Dioxide Capture from Power Generation, 2011.

[5] Iliuta, I.; Tahoces, R.; Patience, G. S.; Rifflart, S.; Luck, F. Chemical-looping combustion process: Kinetics and mathematical modeling. AIChE J. 56 (2010) 1063-79.

[6] Zhou, Z.; Han, L.; Bollas, G.M. Model-based Analysis of Bench-scale Fixed-bed Units for Chemical-looping Combustion. Chemical Eng. J. 233 (2013) 331-48.

[7] Han, L.; Zhou, Z.; Bollas, G.M. Heterogeneous modeling of chemical-looping combustion. Chemical Engineering Sci. 104 (2013) 233-249.

[8] Hou, K.; Hughes, R. The Kinetics of methane steam reforming over a Ni/α-Al2O catalyst. Chemical Eng. J. 82 (2001) 311-328.

[9] Snoeck, J. W.; Froment, G.F.; Fowles, M. Kinetic study of the carbon filament formation by methane cracking on a nickel catalyst. J. Catalysis 169 (1997) 250-262.

[10] Bradford, Vannice. Catalytic reforming of methane with carbon dioxide over nickel catalysts I. Catalyst characterization and activity. Applied Catalysis A: General 142.1 (1996) 73-96.

[11] Adanez et. al. Selection of oxygen carriers for chemical-looping combustion. Energy and Fuels 18 (2004) 371-377 [12] Delgado, J. A critical review of dispersion in packed beds. Heat Mass Transfer 42 (2005) 279-310.

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Quantifying Tibiofemoral Joint Contact Forces in Patients with Knee Osteoarthritis Using OpenSim Paige Kendella,b, William Andertona, Jonathan A. Gustafsona,b, and Shawn Farrokhia,b,c Human Movement and Balance Laboratory, bDepartment of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA c Department of Physical Therapy, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA

a

Abstract

Prolonged walking in patients with knee osteoarthritis (OA) is associated with increased symptoms and accelerated rates of disease progression due to elevated joint contact forces (JCFs). A subject-specific, muscledriven modeling approach for estimating JCFs using OpenSim in patients with knee OA is presented.

Passive marker trajectories and ground reaction forces during a continuous 45-minute bout of walking were used to create OpenSim models to quantify JCFs in 3 patients with knee OA. For model validation, surface electromyography (EMG) recordings were collected and compared to muscle excitation from an OpenSim model for a healthy subject.

Similar trends in the timings and patterns of experimental and predicted muscle activity were found for the healthy subject. Patients with knee OA demonstrated an increase in the first and second peak JCF for the affected limb over 45 minutes of walking equivalent to 56% and 24% of body weight (BW), respectively. The unaffected limb also demonstrated an increase in first peak JCF (28% BW), but a decreased second peak JCF (8% BW) after 45 minutes of walking. A compensatory 52% and 55% BW decrease in the first and second peak JCFs was observed for a patient with increased self-reported pain while walking compared to a patient without pain.

The methods presented here offer a reasonable modeling approach to quantify knee JCFs during prolonged walking. Future efforts will use this model to further our knowledge of the relationship between JCFs with symptoms and disease progression in patients with knee OA. Keywords: Joint contact force, knee osteoarthritis, walking, musculoskeletal model 48

Introduction

Knee osteoarthritis (OA) is a disease that commonly affects older adults in the United States and is characterized by pain, cartilage degradation and functional limitations during daily activities [1].

Walking is a common daily activity that is performed by patients with knee OA and is often prescribed as a form of exercise therapy to keep knee joints mobile, maintain or lose weight and to boost overall health for patients with knee OA. However, prolonged walking is often associated with increase in symptoms and could accelerate the risk of disease progression in those affected by knee OA due to increases in the knee joint contact forces (JCFs) [2, 3]. To date, information regarding the knee JCF profile during prolonged walking remains limited due to the technical challenges associated with estimating in vivo knee JCFs. It was previously suggested that the contribution of the muscle forces crossing the knee joint to the net JCF during gait in patients with knee OA could be significant as they approach several times the body weight in magnitude [2]. Therefore, the ability to modify the net JCF placed on the knee joint by reducing the muscle force contribution could have important clinical utility. However, the ability to directly quantify the contribution of muscle forces to JCF profiles in vivo does not currently exist. Subjectspecific musculoskeletal modelling has been proposed as one way of estimating muscle forces in-vivo through optimization techniques by using joint motion and ground reaction force data [4]. The estimated muscle forces are subsequently used to estimate the net JCFs at the knee joint. While utilizing musculoskeletal models to predict JCFs is an effective tool, validation of the model outputs is necessary before adequate confidence in their clinical use are attained.

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We present a methodology for estimating knee JCFs using OpenSim. OpenSim is a publically available 3-diemensional computational musculoskeletal modelling platform capable of predicting JCFs using subject-specific, muscle-driven gait simulations. The long-term goal of this line of research is to utilize our proposed modeling approach to gain better understanding of the association between JCF profile and presence of symptoms and increased rates of disease progression in patients with knee OA.

Materials and Methods

Our model development and validation process can be described in four parts: 1) collection of biomechanical data; 2) OpenSim model adaptation for subject-specific implementation; 3) model simulations to estimate JCF profile; and 4) validation of the model muscle activation output. Data were obtained from three elderly patients with symptomatic knee OA whom met the American College of Rheumatology classification criteria for knee OA [5] and demonstrated radiographic knee OA of at least grade II or higher according to the Kellgren and Lawrence radiographic severity rating scale [6]. Subjects were informed of all data collection procedures and signed an informed consent form approved by the University of Pittsburgh institutional review board.

Data Collection Subjects had their lower extremities outfitted with clusters of reflective markers (modified Cleveland Clinic Gait marker set) and virtual markers were calibrated at the hip, knee and ankle joint centers (The MotionMonitor, Chicago, IL, USA) for data collection. Subjects performed a single bout of walking on an instrumented, split-belt treadmill (Bertec Corp., Columbus, OH, USA) for a total of 45 minutes at a speed of 1.3m/s which is previously reported to be the average self-selected gait speed in older adults with knee OA [7]. Thirty seconds of passive marker trajectory data sampled at 100 Hz (Vicon motion Systems Ltd., Oxford, UK) and ground reaction force data sampled at 1000 Hz were collected at baseline and after 45 minutes of walking. Patients with knee OA were also asked to rate the pain of their affected knee on an 11-point numerical rating scale (NRS) while walking. All patients were attached to a ceiling mounted safety harness system to avoid accidental falls and injury.

OpenSim Model Adaptation A 3-dimensional computational musculoskeletal model was adapted from the Lower Limb Model [8] in OpenSim [4] to simulate knee JCFs. The adapted model contained 23 degrees of freedom and 96 muscles. Due to deprecation, all Schutte muscles in the model were replaced with Thelen muscles.

All 96 muscles were strengthened by 50% to avoid high residual errors. Muscle wrapping surfaces were deactivated to decrease overall computational time.

Given that data on bony landmarks above the superior iliac spine were not collected, lumbar movement was locked, forcing the spine into a neutral pose. Virtual markers and a marker representing the centroid of the Cleveland Clinic marker set were used as the final marker set applied to the adjusted model, as seen in Figure 1.

Figure 1. Cleveland Clinic marker set (left) with a virtual marker at the centroid of each cluster as they were applied to the adapted OpenSim model (right).

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Joint Contact Force Prediction Subject-specific models were anthropometrically scaled from the adapted model (Figure 2A) until the total root mean square (RMS) error between the predicted marker location and actual marker location was less than 0.01 m (Figure 2B). Joint angles were calculated using an inverse kinematics tool which derived the angles by minimizing weighted square errors of the predicted marker location to the actual marker location (Figure 2C). Muscle activity was estimated using the static optimization tool which uses the inverse kinematic results to further resolve the equations of motion for the unknown muscle forces (Figure 2D). Finally, a joint reaction analysis tool in OpenSim was used to predict the JCFs by combining the internal and external forces being applied to the

model. The knee JCFs were subsequently decomposed into orthogonal components for each child segment in the parent frame (distal to proximal). The knee JCFs during the stance phase of gait were averaged and reported as a percentage of body weight (BW).

Model Validation To qualitatively compare the output muscle activation timings and patterns from OpenSim, a young, healthy control subject with no history of lower extremity injury was recruited. Surface electromyography (EMG) muscle activity (DelSys, Inc., Natick, MA, USA) was collected at 2000 Hz and recorded muscle activity from six major leg muscles crossing the knee joint: medial/lateral gastrocnemius, medial/lateral quadriceps, and medial/lateral hamstrings.

Figure 2. Visual representations of the adapted model used in this study prior to subject-specific scaling (A), after anthropometrically scaling (B), walking resulting from inverse kinematic results (C) and walking including calculated muscle activity (D). The green arrows represent the ground reaction forces.

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Figure 3. Comparison of the predicted muscle activation levels and the corresponding recorded EMG signals normalized as a percentage of maximum values.

Results

Comparing the experimental EMG data with the predicted muscle activation from the musculoskeletal model for the healthy control patient, similar trends in the timings and patterns of muscle activity was found throughout the entire gait cycle (Figure 3).

The JCF profile consisted of a first and a second peak occurring at the early and late stance phases of the gait

cycle, respectively. The average magnitudes of the first and second peak JCF at baseline were approximately 2.7 BW and 2.5 BW, respectively. After 45 minutes of walking, the knee OA patients demonstrated a 56% BW increase in first peak JCF and a 24% BW increase in second peak JCF for the affected limb.

Additionally, there was a 28% BW increase in first peak JCF, but an 8% BW decrease in second peak JCF for the un-affected limb over time (Figure 4). The

Figure 4. Average weight-normalized tibiofemoral joint contact forces during gait at baseline (black) and 45 minutes (red) for the affected and un-affected limbs.

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Figure 5. Comparison of weight-normalized tibiofemoral joint contact forces at baseline and 45 minutes of gait between the affected limbs of a patient without self-reported pain while walking (black) and a patient with increased self-reported pain while walking (red).

subject who reported an increase in pain while walking demonstrated a decrease in first (52% BW) and second (55% BW) peak JCF between baseline and 45 minutes of walking compared to a patient without reports of pain (Figure 5).

Discussion

The purpose of this study was to develop and validate a modeling approach for estimating knee JCF. Overall, the range of JCF values (2.4 BW â&#x20AC;&#x201C; 3.4 BW) falls within previously reported values for patients with knee OA [2], crouch gait [9], skeletal reconstruction [10], and normal controls [11]. The increases in JCF over time for patients with knee OA demonstrate increasing loads to a compromised knee joint. Observed increases in JCF of 56% BW with every step in the affected joint during the early stance phase which is a high-impact portion of gait cycle could potentially lead to reports of symptoms with prolonged walking and expedite further joint degradation in this patient population.

An interesting finding was the large decrease in JCF between the knee OA patient with increased pain while walking and the knee OA patient without pain.

The average 53% BW reduction in net JCF of the knee OA patient with pain is indicative of a compensatory strategy to unload the painful knee joint which is consistent with previous reports of similar decreases in JCF in patients with severe radiographic knee OA [2].

The findings of our validation study is consistent with those previously reported by Taddei and colleagues [10] who performed a similar validation procedure of the muscle activity in a total femoral reconstruction patient population using OpenSim. Supported by other 52

studies [11-14], co-contraction of the quadriceps and hamstring muscle groups was observed in the early portion of the gait cycle, supporting the biomechanical construct that patients with knee OA exhibit a typical knee-stiffening gait strategy [15]. Although the results from this study suggest similar trends between experimental and predicted muscle activation timings and patterns, these patterns were for a healthy control, whereas it is well established that patients with knee OA exhibit increased muscular co-contraction with advanced disease severity [16-19]. However, a proof of concept in the form of the modelâ&#x20AC;&#x2122;s ability to predict muscle activation has been established.

There are several limitations from this study that should be addressed. First, the JCF profile predicted by the musculoskeletal model was not validated directly in vivo. However, the magnitude of JCF from our model is similar to a previous study looking at knee JCFs in a dataset of patients with implanted force-measuring prosthesis [20]. Furthermore, while experimental EMG and predicted muscle activity was comparable to prior studies, validation of additional subjects, especially patients with knee OA, would supplement the use of this model in the knee OA patient population. Additional limitations of the model development include potential variations of JCF due to de-activation of wrapped surfaces of the muscles as well as the simplification of the knee joint to a single degree of freedom. However, as this study was concerned with the net knee JCF, we feel the simplified knee joint has provided acceptable results.

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Conclusion

A musculoskeletal model was created for estimating knee JCF profile in patients with knee OA and validation of output muscle activations was performed. The similar trends between experimental and predicted EMG data provided confidence in the modelâ&#x20AC;&#x2122;s ability to predict the contribution of the major muscle groups crossing the knee joint to the net JCF. The ultimate goal for developing these subject-specific models is to gain a better understanding of the association between JCF profile and presence of symptoms and increased rates of disease progression in patients with knee OA. Further research will then be needed to determine if modification of knee joint muscle activity and JCF could be used as relevant targets for rehabilitation interventions to reduce pain and disease progression in patients with knee OA.

Acknowledgements

This work was supported by the University of Pittsburgh Medical Center (UPMC) Rehabilitation Institute and the Swanson School of Engineering. The authors acknowledge and thank Dr. Arash Mahboobin and Dr. Liying Zheng for their technical assistance.

References

[1] Creamer, P., M. Lethbridge-Cejku, and M. Hochberg, Factors associated with functional impairment in symptomatic knee osteoarthritis. Rheumatology, 2000.

39(5): p. 490-496.

[2] Richards, C. and J. Higginson, Knee contact force in subjects with symmetrical OA grades: differences between OA severities. Journal of biomechanics, 2010.

43(13): p. 2595-2600.

[3] Andriacchi, T.P., et al., A framework for the in vivo pathomechanics of osteoarthritis at the knee. Annals of biomedical engineering, 2004. 32(3): p. 447-457.

[4] Delp, S.L., et al., OpenSim: open-source software to create and analyze dynamic simulations of movement.

Biomedical Engineering, IEEE Transactions on, 2007.

54(11): p. 1940-1950.

[5] Altman, R., et al., Development of criteria for the classification and reporting of osteoarthritis: classification of osteoarthritis of the knee. Arthritis & Rheumatism, 1986. 29(8): p. 1039-1049.

[6] Kellgren, J. and J. Lawrence, Radiological assessment of osteo-arthrosis. Annals of the rheumatic diseases, 1957. 16(4): p. 494.

[7] Landry, S.C., et al., Knee biomechanics of moderate OA patients measured during gait at a self-selected and fast walking speed. Journal of biomechanics, 2007. 40(8): p. 1754-1761.

[8] Arnold, E.M., et al., A model of the lower limb for analysis of human movement. Annals of biomedical engineering, 2010. 38(2): p. 269-279.

[9] Steele, K.M., et al., Compressive tibiofemoral force during crouch gait. Gait & posture, 2012. 35(4): p. 556-560.

[10] Taddei, F., et al., Femoral loads during gait in a patient with massive skeletal reconstruction. Clinical Biomechanics, 2012. 27(3): p. 273-280.

[11] Sasaki, K. and R.R. Neptune, Individual muscle contributions to the axial knee joint contact force during normal walking. Journal of biomechanics, 2010. 43(14): p. 2780-2784.

[12] van der Krogt, M.M., S.L. Delp, and M.H. Schwartz, How robust is human gait to muscle weakness? Gait & posture, 2012. 36(1): p. 113-119.

[13] Valente, G., F. Taddei, and I. Jonkers, Influence of weak hip abductor muscles on joint contact forces during normal walking: probabilistic modeling analysis. Journal of biomechanics, 2013. 46(13): p. 2186-2193.

[14] Xiao, M. and J. Higginson, Sensitivity of estimated muscle force in forward simulation of normal walking.

Journal of applied biomechanics, 2010. 26(2): p. 142.

[15] Childs, J.D., et al., Alterations in lower extremity movement and muscle activation patterns in individuals with knee osteoarthritis. Clinical biomechanics, 2004.

19(1): p. 44-49.

[16] Hurley, M. and D.J. Newham, The influence of arthrogenous muscle inhibition on quadriceps rehabilitation of patients with early, unilateral osteoarthritic knees. Rheumatology, 1993. 32(2): p. 127-131.

[17] Grant, C. and A. Dixon, Joint distension and reflex muscle inhibition in the knee. The Journal of Bone & Joint Surgery, 1965. 47(2): p. 313-322.

[18] Oâ&#x20AC;&#x2122;Reilly, S.C., et al., Quadriceps weakness in knee osteoarthritis: the effect on pain and disability. Annals of the rheumatic diseases, 1998. 57(10): p. 588-594.

[19] Lewek, M.D., K.S. Rudolph, and L. SnyderMackler, Quadriceps femoris muscle weakness and activation failure in patients with symptomatic knee osteoarthritis. Journal of Orthopaedic Research, 2004.

22(1): p. 110-115.

[20] Fregly, B.J., et al., Grand challenge competition to predict in vivo knee loads. Journal of Orthopaedic Research, 2012. 30(4): p. 503-513.

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Domain-wall Memory Buffer for Low-energy Networks on Chip Donald Kline Jr.1, Haifeng Xu1, Fan Chen1, Rami Melhem2, and Alex K. Jones1 Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA 2 Department of Computer Science, Kenneth P. Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA, USA akjones@pitt.edu

1

Abstract

Networks-on-chip (NoCs) have become a leading energy consumer in modern multi-core processors, with a considerable portion of this energy originating from the virtual channel First-in-First-Out (FIFO) buffers.

While emerging memories have been considered for many architectural components, the asymmetric access properties and relatively small size of network-FIFOs compared to the required peripheral circuitry has led to few such replacements proposed for NoCs. In this paper, we propose control schemes that leverage the “shiftregister” nature of spintronic domain-wall memory (DWM) to replace conventional memory buffers for the NoC, in order to reduce the network power consumption.

Our results indicate that the best shift-based scheme utilizes a dual-nanowire approach to more effectively align with access ports for simultaneous access in the same cycle. Our approach provides a 2.93X speedup over a DWM buffer using a traditional FIFO memory control scheme with a 16% savings in energy. Compared to a SRAM (Static RAM)-FIFO it exhibits a 56% energy reduction with only an 8% message latency degradation.

The resulting approach achieves a 53% and 42% reduction in energy delay product compared to SRAM and STT-MRAM (Spin Torque Transfer Magnetic RAM), respectively.

Keywords: Domain-wall Memory, FIFOs, Networkson-Chip (NoCs)

Introduction

Sprintronic domain-wall “Racetrack” memory, recently proposed and demonstrated by IBM [1, 2], provides a potential solution for the size issues apparent in STTMRAM FIFOs. Specifically, the storage array size for such buffers is, unlike large MB size caches, often dominated by the peripheral circuitry, making the overall power benefit potentially limited by the FIFO size.

Domain wall memory (DWM) has a smaller peripheral 54

circuitry size and overall physical size due to the reduced access points, and is comprised of a ferromagnetic nanowire where multiple bits of data are stored in the different domains along the nanowire. To read/write the data in the DWM, the appropriate domain must shifted into alignment with an access point similar to the magnetic tunnel-junction (MTJ) of STT-MRAM. Additionally, DWM can also replace the current-based write of STTMRAM with a shift-based write [3] to improve the write time and energy to be more appropriate for a NoC FIFO buffer. The challenge of implementing an efficient DWM FIFO becomes how to organize the data placement and movement for best possible efficiency. We address this by providing a shift-register style FIFO buffer with creative placement of access points to maximize the performance and energy efficiency of the design while being sensitive to area requirements.

Background DWM comprises an array of magnetic nanowires, where each nanowire consists of many magnetic domains separated by domain walls (DWs). Each domain has its own magnetization direction used in a similar manner to STT-MRAM. For a horizontally-arranged planar strip (Figure 1), several domains share one access point for read and write operations [4]. The DW motion is controlled by applying a short current pulse on the head or tail of the nanowire in order to align different domains with the access point. Random access requires two steps to complete: Step 1-shift the target magnetic domain and align it to an access transistor; Step 2-apply an appropriate voltage/current to read or write the target bit. Intrinsically, the read operation from step 2 is the same as STT-MRAM, however the write can be a shift in the orthogonal dimension [3]. Thus, the tradeoff for DWM is further reduced leakage power over STTMRAM (fewer access transistors per bit) with increased dynamic power due to shifting domains.

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Figure 1. DWM Structure

Due to the growing percentage of power consumption in many-core architectures contributed by the NoC, it has been a significant concern of many research groups to find ways to reduce power consumption and use high-density memories. To the best of our knowledge, there is no prior work using DWM to implement variable length FIFO queues suitable for utilization in a NoC. However, a fixed-length shift register, realized by perpendicular magnetic anisotropy (PMA) technology, has been demonstrated [4, 5]. The leading scheme utilizing emerging memory which reduces energy in NoC buffers replaces a large percentage of the FIFO’s SRAM with STT-MRAM [6]. This approach writes into SRAM and then lazily migrates it to a reduced retention (i.e., a faster lower write effort) STT-MRAM [7, 8] when possible. An energy savings of 16% is demonstrated. In contrast, we demonstrate several DWM designs for NoC FIFOs that replace the entire SRAM buffer with spintronic memory with little or no SRAM buffering required.

Methods

FIFO Design Figure 2 (a) shows our proposed circular buffer (CB) implementation inspired by traditional FIFO architectures using DWM. We start with a single read/write port in the center. A FIFO write shifts the racetrack (if necessary) to align the tail domain with the access point (step 1) and then to write (step 2) using the orthogonal shift-write. For reading, the head domain is aligned with access point and then read by applying a current. Immediately, several undesirable characteristics become apparent. First, to align the leftmost or rightmost domain with the access point requires a nanowire that is essentially twice as long as the useful storage in the device [regions indicated by “overhead” in (a)]. This makes the nanowire larger and requires more shifting effort. Also, it may be necessary to shift the full logical length of the Racetrack between subsequent writes to the queue. Further, most FIFOs are assumed to be able to read and write simultaneously, which is not possible in most configurations.

Figure 2. FIFO queue structure with DWM. (a) traditional circular buffer. (b) shift-register approach

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To address these inefficiencies we consider three approaches, a linear buffer (LB) concept [Figure 2 (b)] that shifts data through the Racetrack like a shiftchain, an increase the number of access points, and the introduction of temporary SRAM storage to buffer reads or writes to move them off of the critical path.

It has been demonstrated that a multiple read port DWM does not detract significantly from the density achievable by the nanowire because of the small relative size of read ports [9, 10]. Thus, it is reasonable to add additional read access points to increase performance at the cost of some additional static power. These additional ports are displayed in Figure 2 with dashed lines. While the CB scheme uses traditional head/tail pointers, the LB scheme requires a finite state machine (FSM) for control as shown in Figure 3. To simplify the FSM, we present the case where read access ports are separated by one domain and assume that data must stay contiguous in the Racetrack (i.e., there are no gaps between flits). We also assume that writes and shifts require half a cycle, and reads require one cycle.

Thus, there are four states possible for the buffer (see Figure 4).

While LB significantly reduces write delays compared to CB, consecutive reads in either scheme continue to introduce additional read latency even when including a single-flit SRAM head buffer and prioritizing reads due to the longer operation time compared to shifting/writing. In order to mitigate this concern, we propose using Dual Linear Buffers (Dual). Each of the nanowires is half the length, but also has half the read access points of the LB, and has similar FSM control logic (each nanowire has a FSM control) with a small extension. However, the two Racetrack structure allows alternating reads and writes essentially creating the illusion of a dual port. For example, one Racetrack can shift to prepare for an access while the other is accessed. We include two additional control bits, “Read Owner” and “Write Owner” to manage which Racetrack is accessed in each cycle. For each access, the owner bit is flipped.

Figure 3. Linear Buffer finite state machine. R=Read, (R)=read align racetrack, W=Write, Idle=neither read nor write, <<RC and >>RC shift the read ports left and right, respectively

Figure 4. Example queues for states in Fig. 3

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Table 1. Buffer Power (uW) synthesized in NVSim

Table 2. Architectural Parameters

Network Simulation In order to evaluate the Racetrack FIFO schemes, we implemented CB, LB, and Dual schemes to serve as virtual channel buffers in a NoC using the HORNET multicore simulator [11] to compute both average flit latency and energy consumption. In addition, peripheral circuitry power calculations for SRAM, STT-MRAM and different Racetrack FIFO schemes were analyzed using a modified version of NVSim [12] (see Table 1).

Sniper [13] was used to generate workload traces of the PARSEC benchmark suite [14] for the modified HORNET simulator. In order to estimate the full system performance impact of the NoC, the HORNET generated latencies were then used in a second full system simulation in Sniper to determine performance impact in terms of IPC. CB, LB, and Dual were tested both with and without a single flit SRAM storage to buffer the queueâ&#x20AC;&#x2122;s head flit.

When running the simulations, we assumed there was no performance difference of SRAM and STT-MRAM as an optimistic implementation of the leading STTMRAM NoC buffer proposal [6]. Also, to compare the implementation of DWM technology with a more energy efficient, and for STT-MRAM area-equivalent counter parts, both SRAM and STT-MRAM were also

tested with half (4) the number of queues per channel (SRAMHalf and STTHalf).

We assume a NoC clock speed of 1GHz which allows SRAM, STT-MRAM, and Racetrack reads in a single cycle. SRAM and STT-MRAM writes are also assumed to be a single cycle, while Racetrack shifts and writes take half a cycle allowing a Racetrack to write and shift, or shift twice in a cycle. The detailed architecture parameters are shown in Table 2.

Results

To evaluate the impact of the proposed Racetrack buffer schemes we compared CB, LB, and Dual with an SRAM and STT-MRAM baseline for flit latency, overall system performance (IPC) and energy delay product based on the architectural parameters from Section 5. Figure 5 summarizes the average flit latency of the Racetrack buffer schemes normalized to SRAM. As expected, the translation of a head/ tail pointer FIFO concept (CB) performed poorly, resulting in a more than 3X increase in latency over SRAM. In contrast, LB was much more competitive than CB, but still increased latency by 73%. By adding a single flit SRAM storage element for the head flit to the Racetrack, the CB (CB+S) and LB (LB+S) latency

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overhead can be reduced to 13% and 10% respectively.

The Dual scheme without adding SRAM actually outperforms the CB+S and LB+S schemes but still requires an 8% latency overhead compared to the all SRAM FIFO. The impact of these latencies on full system performance is shown in Figure 6 as IPC normalized to all SRAM buffers. CB resulted in a dramatic 22% IPC degradation, while LB also had a significant 7.2% IPC reduction compared to SRAM. Adding the SRAM head flit storage, the LB+S and Dual scheme were nearly indistinguishable, requiring a nominal 1.7% overhead over SRAM. Dual+S was nearly indistinguishable from SRAM, only causing an extra 0.69% additional overhead.

Using the energy parameters synthesized from NVSim shown in Table [1], the total energy consumed by the different FIFO configurations was calculated, shown in Figure 7. On average, all of the DWM and STTMRAM FIFOs had an energy reduction over SRAM. LB had the greatest energy reduction, with a 57.5% reduction over SRAM. Dual was very close behind, with a 56.3% energy reduction over all of the FIFO buffers. Despite the increased execution time of CB, it still managed to save 51% over SRAM, due to its low static power overhead. When an SRAM buffer is added to CB, LB, and Dual, the average energy reduction over SRAM decreases to 20.2%, 20.5%, and 29.4%, respectively.

Figure 5. Average Per-Flit Network Latency

Figure 6. IPC normalized to SRAM

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Figure 7. Energy Consumption Normalized to SRAM

Figure 8. Energy-Delay Product Normalized to SRAM

The results of the energy delay product calculations in Figure 8 revealed that Dual yields a 53% reduction over SRAM. Also, the developed Linear Buffer scheme provides a 25.4% reduction over SRAM. Furthermore, the CB scheme with an additional SRAM buffer is slightly higher than the SRAMHalf configuration in terms of energy delay product, and the LB scheme with an SRAM buffer is also very similar to the SRAMHalf scheme in energy delay product. When compared to 0.5ns read and sub 1ns write STT-MRAM technologies, CB, CB with an SRAM buffer, and LB with an SRAM buffer yield an increased energy delay product. In contrast, the LB yields an energy delay product in between that of STT and STT-Half, the Dual scheme with a buffer provides a reduction over the STT-MRAM implementation by 11.7%, and the Dual scheme produces a 41.5% reduction from the baseline STT- MRAM.

Discussion

When observing the average message latencies of the different DWM buffers without additional SRAM storage, the difference is dramatic: CB has over 3x the message latency of SRAM, LB has nearly 1.73x the latency of SRAM, and Dual only has 1.08x the latency

of SRAM. The progressive improvement in message latency is largely due to an increased number of possible racetrack configurations which can accommodate cases of successive reads, writes, and concurrent reads and writes to be handled without introducing stalls. By using the Dual configuration, the buffer can naturally overcome the previous limitation of not being able to perform consecutive reads, by allowing one racetrack to move while the other one is reading. This also explains the considerable improvements in latency by adding SRAM storage for the leading flit, which allows the racetrack to move while the buffer is being read from.

When observing the energy-delay product in Figure 8, by far the best performing DWM scheme is Dual. Because of its low latency at the cost of little additional overhead in peripheral circuitry, Dual is able to significantly reduce energy-delay product from SRAM. The CB scheme, while reducing energy, actually results in an increase in energy-delay product over SRAM, due to its significant increase in message latency, making it a non-feasible replacement for SRAM.

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Conclusions

In order for racetrack memory to practically replace SRAM in virtual channel buffers without a drastic drop in performance, more advanced schemes are necessary than traditional FIFO controls. One such strategy that demonstrated its viability through this experiment was Dual, which on average across the Parsec benchmarks had a 41% improvement over the CB scheme in performance, as well as a 56% improvement over the SRAM scheme in power.

Due to the reduced number of read-write heads in DWM as compared to SRAM or STT-MRAM, 2X the number of DWM queues can occupy the same space as X STT-MRAM queues. At a 1 GHz clock frequency, the Dual scheme with a buffer outperforms and reduces energy compared to the equivalent area STT-MRAM and SRAM schemes. If the focus is more energy savings, the buffer can be removed in order to have a 46% and 36% energy-delay product reduction compared to the area-equivalent SRAM and STTMRAM queues, respectively.

Acknowledgements

I would like to thank Dr. Jones for his guidance and funding for the project, Fan Chen for the calculation of the peripheral circuitry numbers, Haifeng Xu for generating the Sniper traces and helping to determine the energy parameters, and Michael Moeng for advice on using Hornet.

References

[1] S. S. P. Parkin, et al., “Magnetic Domain-Wall Racetrack Memory,” Science, Vol. 320, No. 5874, pp.

190–194, Apr. 2008.

[2] S. Parkin, “Racetrack Memory: A Storage Class Memory Based on Current Controlled Magnetic Domain Wall Motion,” Device Research Conference, pp. 3–6 2009.

[3] R. Venkatesan, et al.,“DWM-TAPESTRI-an energy efficient all-spin cache using domain-wall shift based writes,” DATE 2013, pp. 1825–1830, EDA Consortium.

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[4] Y. Zhang, et al.,“Perpendicular-magneticanisotropy CoFeB racetrack memory,” Journal of Applied Physics, Vol. 111, No. 9, No. 9, p. 093925, 2012.

[5] L. Thomas, et al. “Racetrack Memory: A Highperformance, Low-cost, Non-volatile Memory based on Magnetic Domain Walls,” IEEE IEDM, pp. 24.2.1– 24.2.4, Dec. 2011.

[6] H. Jang, B. S. An, N. Kulkarni, K. H. Yum, and E. J. Kim, “A Hybrid Buffer Design with STTMRAM for On-Chip Interconnects,” Proceedings of the 2012 IEEE/ACM Sixth International Symposium on Networks-on-Chip, NOCS ’12, (Washington, DC, USA), pp. 193–200, IEEE Computer Society, 2012.

[7] C. Smullen, et al., “Relaxing non-volatility for fast and energy-efficient STT-RAM caches,” IEEE HPCA, 2011, pp. 50–61, Feb 2011.

[8] Z. Sun, et al., “Multi Retention Level STT-RAM Cache Designs with a Dynamic Refresh Scheme,” IEEE/ACM MICRO-44, (New York, NY), pp. 329– 338, ACM, 2011.

[9] R. Venkatesan, et al., “TapeCache: a high density, energy efficient cache based on domain wall memory,” ACM/IEEE ISLPED pp. 185–190, ACM, 2012.

[10] Z. Sun, et al. “Design exploration of racetrack lower-level caches,” ISLPED 2014, pp. 263–266, ACM, 2014.

[11] M. Lis, et al. “Scalable, accurate multicore simulation in the 1000-core era,” IEEE ISPASS 2011. pp. 175–185, April 2011.

[12] X. Dong, et al., “NVSim: A Circuit-Level Performance, Energy, and Area Model for Emerging Nonvolatile Memory,” IEEE TCAD 2012. Vol. 31,No. 7, pp. 994–1007.

[13] T. Carlson, et al., “Sniper: Exploring the level of abstraction for scalable and accurate parallel multicore simulation,” SC 2011, pp. 1–12, Nov 2011.

[14] C. Bienia, et al. “SPLASH-2: A Quantitative Comparison of Two Multithreaded Benchmark Suites on Chip-Multiprocessors,” Tech. Rep. TR-81808, Princeton University, Department of Computer Science, March 2008

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Body Segment Parameters in Normal Weight versus Obese Young Females Molly Knewtsona, Zachary Merrilla, Rakie Chama, and April Chambersa Human Movement and Balance Laboratory, Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

a

Abstract

Body segment parameters (BSPs) such as length, mass, center of mass, and radius of gyration are important in gait analysis and injury prevention. Previous predictive models for BSPs have been developed with normal weight subjects and thus do not accurately predict parameters of obese individuals. This study aims to compare BSPs of obese females against normal weight females, aged 21-39, using dual energy X-ray absorptiometry (DXA). Thirteen obese (BMI: 30-40 kg/m2) and thirteen normal weight (BMI: 18.525 kg/m2) females underwent a whole body DXA scan and body measurements. Bony landmarks and joint centers were used to define the boundaries between head, torso, thigh, shank, upper arm, and lower arm segments. Scans were analyzed for segment length as a percent of body height (SL), segment mass as a percent of body mass (SM), longitudinal distance from the proximal end to the center of mass of the segment as a percent of segment length (COM), and frontal plane radius of gyration as a percent of segment length (RG). Obesity was found to significantly affect RG, COM, and SM in various segments, indicating a need for anthropometric models that account for body mass index variations.

Keywords: Body segment parameters, dual energy X-ray absorptiometry (DXA), obesity, anthropometry Abbreviations: BMI-body mass index, DXA-dual energy X-ray absorptiometry, BSPs-body segment parameters, SL-segment length as a percent of body height, SM-segment mass as a percent of total body mass, COM-longitudinal distance from the proximal end to the center of mass of a segment as a percent of segment length, RG-frontal plane radius of gyration as a percent of segment length

Introduction/Background

In recent years, the percentage of workers in the United States classified as overweight or obese has risen to over 60% [1]. This rise in obesity yields health concerns as the obese population is more highly susceptible to medically attended injuries [2], the mechanisms of which are not fully understood [3]. In order to understand the cause of this difference in injury rates between susceptibility of obese and non-obese populations, additional research is required. Certain biomechanical research requires physical information about a subject not easily or affordably collected.

Therefore, researchers rely on anthropometric tables to give accurate approximations of subjectsâ&#x20AC;&#x2122; measurements. Specifically, anthropometric tables include predictions of body segment parameters (BSPs). Common examples of studied parameters include lengths, masses, center of mass locations, and radii of gyration of the bodyâ&#x20AC;&#x2122;s segments (head, torso, upper arm, lower arm, thigh, and shank).

Current anthropometric models were developed with subjects in a normal weight range [4] [5], so they do not accurately predict BSPs of obese individuals.

For example, Chambers et al. found a significant difference between trunk segmental mass, center of mass, and radius of gyration in obese versus non-obese elderly [6]. Matrangola et al. quantified the changes in segmental parameters over a period of weight loss, finding significant differences in many of the center of mass locations and radii of gyration of various segments [7]. The literature suggests a need for an accurate way to predict segmental specifications for the working population while accounting for obesity.

Many methods have been used to quantify BSPs to create predictive models and regression equations such as cadaver-based studies [8], imaging [4], computerized tomography [5], and biplanar radiography [9]. However, each of these methods has

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disadvantages such as time-intensiveness, monetary cost, and/or delivery of high doses of radiation to the participant. Dual energy X-ray absorptiometry (DXA) is a quick, inexpensive, low-radiation full body scan that can distinguish the difference between bone, muscle, and fat tissue, allowing it to calculate accurate densities and masses of the bodyâ&#x20AC;&#x2122;s segments [10]. It is a verified method of finding segmental mass, center of mass, and radius of gyration in the frontal plane [11].

Therefore, this study uses DXA to quantify BSPs of the participants in efforts to compare body segment parameters of young obese females against those of young normal weight females.

Methods

Data Collection A common way to classify obesity is with the use of body mass index (BMI). World Health Organization defines a BMI range of 18.5-25 kg/m2 as normal weight, 25-30 kg/m2 as overweight, and 30+ kg/m2as obese [12]. These categories were assumed, and the obese range was further separated into two categories: obese, corresponding to a BMI of 30-40 kg/m2 and

morbidly obese, corresponding to a BMI greater than 40 kg/m2. Thirteen obese females and thirteen normal weight females aged 21 to 39, who work full time, were recruited for participation in this study (Table 1).

Written informed consent approved by the University of Pittsburgh Institutional Review Board was obtained prior to participation. The subjects were screened for metal implants, broken bones, and osteoporosis, as these would all affect segment mass calculations.

Height and weight were collected to confirm the BMI category of each participant. Eighty-eight body measurements were taken including lengths, widths, and circumferences of the limbs as well as widths, depths, and circumferences of the torso and head.

Each participant underwent a full body DXA scan (Hologic Discovery DXA System) lying supine with legs internally rotated and maximum plantar flexion.

Data Processing In analysis of the DXA scan, bony landmarks and joint centers were used to define the boundaries between head, torso, thigh, shank, upper arm, and lower arm segments (Figure 1a) [4]. The head segment extended

Table 1. Mean and standard deviation of characteristics for the 13 normal weight and 13 obese participants

(a) (b)

Figure 1. (a) Whole body DXA scan with segmental boundaries indicated by solid red lines (b) Sub-region divisions of the thigh segment outlined in red boxes

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from the vertex to the base of the mandible. The torso was defined from the acromion to the greater trochanter. The torso was separated from the arms with a boundary through the medial acromion to the axilla, and it was separated from the thigh with a boundary just lateral to the anterior superior iliac spine and the ischial tuberosity of the pelvis. The thigh was defined from the greater trochanter to the center of the knee joint. The shank extended from the knee joint center to the lateral malleolus. The hands and feet were excluded from analysis. Each of these body segments was further divided into horizontal sub-regions with vertical height of 2 or 3 pixels, corresponding to 2.6 or 3.9 cm, respectively (Figure 1b). Masses were calculated for each sub-region and used to determine BSPs for the segment.

Scans were analyzed for segment length as a percent of body height (SL), segment mass as a percent of body

mass (SM), longitudinal distance from the proximal end to the center of mass of the segment as a percent of segment length (COM), and frontal plane radius of gyration as a percent of segment length (RG) [13]. A two-tailed t-test was run to compare the parameters for the obese subjects to those of the normal weight subjects. Statistical significance was set at 0.05.

Results

Means and standard deviations for all tested BSPs for the normal weight and obese subgroups are presented (Table 2). As expected, no significant differences were found in SL between the two subgroups, as greater BMI does not affect relative lengths of bones. There were no significant differences in any parameters of the thigh segment. However, obesity was found to significantly affect RG, COM, and SM in various other segments (Table 3).

Table 2. Mean and standard deviation of calculated body segment parameters of 13 normal weight and 13 obese subjects.

Table 3. Mean and standard deviation of statistically significant (p<0.05) body segment parameters of 13 obese and 13 normal weight females. * denotes p<0.01.

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Torso and lower arm RG of the obese population were found to be smaller than that of the non-obese population, indicating that subjects with a larger BMI had a higher distribution of mass in their torso and lower arm segments. Head RG was found to be larger for subjects with a greater BMI. Head and shank SMs were smaller for obese than normal weight subjects. Shank COM of obese subjects was found to be more proximal than that of normal weight subjects.

Additionally, obese participants had a significantly greater SM in the upper arm segment when compared against normal weight subjects.

Discussion

The objective of this study was to quantify the impact of obesity on BSPs. No significant differences were found between normal weight and obese thigh SM, COM, nor RG. However, there were significant differences between populations in at least one BSP for all other segments.

The decrease in RG of the torso in the obese population is an intuitive result as people with a greater BMI have more mass concentrated at the midsection of their torso, as discussed in Chambers et al. and Matrangola et al. [6,7].

Previous studies on a geriatric population have found a significantly higher SM in the torso segment of obese versus normal weight populations [6], but this result was not present in our findings. One potential reason for the difference in the findings of these two studies may be the distinct age ranges of analyzed subjects, but further testing on the effects of aging on BSPs would be needed. The variance in RG with no significant change in SM between the groups indicates the groups had a similar proportion of mass in their torso but a dissimilar distribution of that mass within the segment.

In accordance with current literature [6], head and shank SMs were found to be smaller for obese than normal weight subjects. These segments do not store a large portion of the bodyâ&#x20AC;&#x2122;s soft tissue, so they do not show much variation in overall mass between normal weight and obese subjects. However, since the obese subjects have a larger full body mass, the head and shank segments contain a significantly smaller percent of total body mass than they do for normal weight participants. Additional weight is not distributed evenly throughout the body, nor evenly throughout the segments themselves as the shank COM was found to 64

be more proximal for the obese than the normal weight subgroup, again in agreement with previous studies [6].

Muscle bellies are nearer to the superior end of the shank segment, when in anatomical position, moving the center of mass more proximally for subjects with a higher BMI. The use of current regression equations to predict segment parameters in the obese population would simply scale the parameters with respect to the increase in mass but would not account for the redistribution of this mass. Different distribution of segment masses and the mass within these segments changes the SM, COM and RG, making the use of regression equations developed with normal weight subjects inaccurate for predicting BSPs of obese subjects.

This analysis is based on BMI, a measure of the amount of mass in a personâ&#x20AC;&#x2122;s body per square meter, thus it does not account for the composition of this mass.

There are some instances in which a person with a low body fat percentage and high muscle content has a high enough BMI to be classified as obese. Because of this variation in body type within BMI categories, it would also be interesting to look at the correlations between body fat percentage and BSPs in future studies.

There were some limitations of this study which should be noted. The whole body DXA scan was performed in the frontal plane only, so analysis on COM and RG was limited to one plane of the body. Also, during the DXA scan, subjects were laying supine which may have contributed to soft tissue deformation compared to standing vertically. Though this should not change SL or SM parameters, the difference in direction of gravitational pull may have a slight effect on COM and RG. In addition to making comparisons between the two studied subgroups, it would be of interest to examine the similarity between these real values and current regression data. This would further analyze the effects of obesity on BSPs and help detect if new regression models that account for BMI are necessary.

This study, while informative, only encompassed a small subset of the overall population of interest: female young adults in two BMI categories. In order to better determine BSPs of the working population, age and gender must be taken into consideration along with BMI category. Race was not considered in this analysis though it may have an impact on BSPs as well and could be included in future studies as well [11].

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Conclusions

Over the past few years, there has been an increase in the percent of United States workers classified

by BMI as either overweight or obese. In order for researchers to study obese populations and develop preventative measures, they call upon anthropometric data. Current anthropometric sets are based on a normal weight population. However, many BSPs were found to be significantly different between the normal weight and obese subgroups, indicating that obesity impacts physical characteristics of mass distributions throughout the body. Additional mass is not distributed evenly throughout the body, nor evenly throughout the segments themselves, suggesting a need for new anthropometric prediction methods or models that account for BMI variations in the population to more accurately study this growing population.

Acknowledgements

NIOSH grant No. R01 OH010106, National Institutes of Health Grant Number UL1TR000005, Swanson School of Engineering Bioengineering Summer Research Support, Osteoporosis Prevention and Treatment Center, and Jenna Trout

References

[1] Singh GK, Siahpush M, Hiatt RA, Timsina LR, Dramatic increases in obesity and overweight prevalence and body mass index among ethnicimmigrant and social groups in the United States, 1976-2008. J Community Health, 2011. 36 p. 94-110.

[2] Finkelstein EA, C.H., Prabhu M, Trogdon JG, Corso PS, The relationship between obesity and injuries among U.S. adults. Am J Health Promot, 2007.

21(5): p. 460-80.

[3] Wearing SC, H.E., Byme NM, Steele JR, Hills AP, The biomechanics of restricted movement in adult obesity. Obesity Reviews, 2006. 7: p. 13-24.

[5] Pearsall DJ, R.J., Livingston LA, Segmental inertial parameters of the human trunk as determined from computed tomography. Ann Biomed Eng, 1996.

24(2): p. 198-210.

[6] Chambers AJ, S.A., McCrory JL, Cham R, The effect of obesity and gender on body segment parameters in older adults. Clin Biomech (Bristol, Avon), 2010. 25(2): p. 131-6.

[7] Matrangola SL, M.M., Nussbaum MA, Ross R, Davy KP, Changes in body segment inertial parameters of obese individuals with weight loss. J Biomech, 2008. 41(15): p. 3278-81.

[8] WT, D., Space Requirements of the Seated Operator: Geometrical, Kinematic, and Mechanical Aspects of the Body With Special Reference to the Limbs. WADC Technical Report, 1955: p. 55-159.

[9] Dumas R, A.R., Mitton D, Skalli W, de Guise JA, Personalized body segment parameters from biplanar low-dose radiography. IEEE Trans Biomed Eng, 2005.

52(10): p. 1756-63.

[10] Rossi M, Lyttle A, El-Sallam A, Benjanuvatra N, Blanksby B. Body Segment Inertial Parameters of elite swimmers Using DXA and indirect Methods. J Sports Sci Med, 2013. 12(4):p. 761-75.

[11] Durkin JL, J.J.D., Andrews DM, The measurement of body segment inertial parameters using dual energy X-ray absorptiometry. J Biomech, 2002. 35: p. 15751580.

[12] Obesity: Preventing and managing the global epidemic. (2000). WHO Technical Report Series, 894.

[13] Ganley KJ, Powers CM. Anthropometric parameters in children: a comparison of values obtained from dual energy X-ray absorptiometry and cadaver-based estimates. Gait Posture, 2004. 19: p.

133â&#x20AC;&#x201C;140.

[4] de Leva, P., Adjustments to Zatsiorsky-Seluyanovâ&#x20AC;&#x2122;s Segment Inertia Parameters. J Biomech, 1996. 29(9): p. 1223-1230.

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An Adipose Stem Cell Suspension in Keratin Hydrogel for Peripheral Nerve Injury Treatment Lindsey Marra1, Danielle Minteer1, and Kacey Marra1-3 1

Department of Bioengineering, 3McGowan Institute for Regenerative Medicine, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA 2 Department of Plastic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

Peripheral nerve injuries are prevalent in trauma cases and current treatments do not allow adequate recovery.

The Adipose Stem Cell Center has developed a nerve conduit for peripheral nerve repair and aims to provide treatment for cases in which the injury is greater than 2.5 centimeters in length.

An adipose-derived stem cell-suspension in keratin hydrogel that can be injected into the lumen of the nerve conduit may be a biomaterial to provide adequate support to a regenerating peripheral nerve. Necessary qualities of keratin gel are that it must be able to form a gel in solution, maintain its gel state in physiological conditions and allow adipose stem cells (ASCs) to integrate into the gel.

Testing these qualities will provide evidence of keratin gelâ&#x20AC;&#x2122;s biocompatibility and its role in peripheral nerve injury regeneration. The use of ASCs within the gel is verified by a co-culture experiment showing that PC12 cells (a rat nerve cell line) proliferate when grown concurrent with ASCs. Keratin was dissolved at different concentrations to show the optimal concentration for gelation and for biocompatibility. ASCs were seeded on top of the optimized gel. Fluorescence microscopy was performed and cell viability assessed.

ASCs and PC12 cells were plated together in cell culture wells for the co-culture experiment. This serves a preliminary study, as it begins to show that both cell types compliment the growth of the other.

The compilation of these results points to the validity of a biomaterial comprised of keratin hydrogel with ASCs embedded within as an application to peripheral nerve regeneration.

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Keywords: Peripheral nerve regeneration, adipose stem cell, biocompatibility, co-culture.

Abbreviations: PC12 cells: rat nerve cell line, ASCs: adipose stem cells.

Introduction

Peripheral nerve injuries occur in 1.64 percent of trauma patients.[1] The injuries cause a loss of motor and sensory function in the nervous tissue. The peripheral nervous system consists of all the nerves outside of the brain and spinal cord. Despite how widespread this type of injury is in trauma patients, only about 50% of them recover function in their injured nervous tissue.[2,3] The current clinical gold standard of treatment for this peripheral nerve repair is the nerve autograft. The autograft is limited by the available nerve tissue for the transplant, and there are complications associated with the procedure.[4] There are commercially available nerve guides but these fail to heal nerve tissue with the injury greater than 2.5 centimeters wide.[5] The Adipose Stem Cell Center has developed a polymer nerve conduit that targets the wider nerve gaps.6 The polymer conduits have shown evidence of their ability to heal these gaps in nerve tissue.[7,8,9] Engineering a keratin biomaterial to provide biochemical and mechanical support for regenerating peripheral nervous tissue will raise the standard of treatment for peripheral nerve injuries.[10,11,12] Keratin biomaterials are increasingly being applied to wound healing.[13,14] Nerve conduits are a currently developing treatment option for peripheral nerve injury.[15] An adipose-derived stem cell-suspension in keratin hydrogel that can be injected into the lumen of the nerve conduit may be a biomaterial to provide support to the regenerating peripheral nerve.[16,17,18] Keratin must be able to form a gel at a concentration

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that maintains its gel state in physiological conditions; 37 degree Celsius and within the ionic environment of extracellular fluid.[19] In addition, adipose stem cells (ASCs) and neurons must flourish in a shared environment.[20] The gelation procedure for keratin gel was optimized, and adipose stem cells was seeded on the gel to test cell compatibility. This leads to results that indicate whether keratin gel is biocompatible or not.

Materials and Methods

Keratin Gelation Experiment Keratin was extracted from human hair and lyophilized into a powder. A Labconco Free Zone 2.5, Kansas City, MO freeze dryer was used. Lyophilized keratin was dissolved in phosphate buffered saline (PBS) at room temperature. Concentrations of 20%, 22% and 25% by weight percentage of keratin protein are compared for time to gelation and ability to maintain structure when submerged in PBS. To test whether the keratin gel was fully formed, the vial in which it was made was inverted. If the gel is formed it maintains its form at the bottom of the vial when upside-down.

cells per square centimeter. ASCs were seeded on top of the gels. The ASC media consisted of DMEM and serum as well as antifungal and antibacterial agents.

The ASC media was replaced every 2 or 3 days for 7 days. On day 7 the ASCs were labeled with calcein AM (stains living cells) and ethD-1 (stains dead cells).

These were imaged using fluorescence microscopy and cell viability assessed. The microscope used was the Zeiss Axiovert 25, Jena, Germany.

Cytoviability Experiment ASCs were seeded on top of keratin gel or seeded directly in a well plate as a positive control at normal cell culture conditions. The cells were proliferated for 4 days without a media change. On day 4 the ASCs were labeled with calcein AM and ethD-1. Fluorescence was quantified in a Tecan Infinite M200 Pro plate reader, Maennedorf, Switzerland. The percentage of live and dead cells was calculated for both experimental groups (Equations 1and 2), having already subtracted the noise from keratin autofluorescence that was determined with a negative control. A two-tailed, independent t-test was used to determine statistical difference between experimental groups.

Vials of 2.5mL of keratin gel at each of these concentrations were placed at either room temperature or in an incubator at 37°C. Gelation was timed for how long it takes for the keratin to gel in solution.

A preliminary degradation test is performed on the 25% keratin gel. 0.5 mL sections of gel are submerged completely in vials of PBS. Sections of the gel were obtained by scooping sections of gel from a larger portion. Three samples were placed at 37 Ě&#x160;C for one week. This was performed in order to gain qualitative insight towards how long gel maintains its shape in solution.

Fluorescent Imaging Experiment Keratin gel and collagen gel were plated separately on glass-bottomed dishes at a plating density of 5,000

ASC & PC12 Co-Culture Experiment PC12 cells are a rat nerve cell line, and the model for human neurons. Varying ratios of PC12 cells to ASCs were plated on a 24 well plate. 1:3, 2:2, and 3:1 were compared based on morphology after 7 days of coculture. The ratios of the cells had the same ratio of the respective cell media used in each well. The same groups were repeated with the cells plated on top of keratin gel. Bright field microscopy was used to image the experimental groups to gain insight on cell morphology and confluency.

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Results

Keratin Gelation Experiment A 25% lyophilized keratin solution in PBS was studied due to its optimal gelation properties at physiological conditions highlighted in Figure 1. The 25% solution formed a gel within 30 minutes at both room temperature and at 37°C. The two solutions at lower concentrations did not form a gel at all. Temperature within this range does not seem to affect gelation time.

Dissolving lyophilized keratin in PBS proves to be a difficult process due to the large volume of dry keratin that is dissolved into a much smaller volume of PBS. The gel should be pushed into the PBS at the bottom of a container that is completely dry on the sides to prevent sticking. Once the dried keratin is inside this container and submerged in the PBS, it should not be stirred or shaken as this will trap air pockets within the solution in its gelled state. It appears that the solution does not gel until the dry keratin powder is completely dissolved. This process takes up to 30 minutes regardless of temperature.

Fluorescent Imaging Experiment Fluorescent microscopy indicated qualitatively that ASCs are viable within keratin gel over 7 days. The majority of the fluorescence in the keratin experimental group was green, indicating live staining with calcein AM. These images are shown in Figure 3. After trying plastic well plates, glass well plates, and small glass dishes, it is determined that the glass dishes allow for the best visibility of the cells. It is necessary to seed cells on top of the gel due to the dark coloring of the gel, shown in Figure 1.

Figure 1. An upturned vial with 2.5mL of keratin gel at the top. Keratin gel is completely gelled, and its dark coloring can be observed.

When sections of the gel are submerged in PBS, they maintain their size and shape for at least one week, and then begin to discolor the surrounding PBS as shown in Figure 2, indicating their dissolution or degradation Figure 3. Fluorescence microscopy of ASCs on keratin (A) and collagen (B) indicate results of live/dead assay. Green fluorescence represents live cells and red fluorescence dead cells.

Cytoviability Experiment Fluorescence was quantified after a four-day experiment of cells grown on keratin gel. No significant difference was observed in the percentage of live cells on keratin compared to live cells grown directly on the well plate (p= 0.1528, Îą=0.005). There is a significant increase in the percentage of dead cells in keratin compared Figure 2. Pieces of keratin hydrogel shown submersed in PBS. The pieces discolor the PBS surrounding them yet retain their shape after seven days.

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to the percentage of dead cells grown directly on the well plate (p= 0.0002158, Îą=0.001). These results are shown in Figure 4.

Figure 4. The results of the cytoviability experiment show that the percentage of live cells between groups is significantly the same (*) and the percentage of dead cells between groups is significantly higher in the keratin. (o)

ASC & PC12 Co-Culture Experiment In the co-culture experiment, every experimental group of cells in collagen reached 100% confluency as shown in Figure 5. Whether the cells reached confluency in the keratin gel is not apparent from the light microscopy. The morphology of the cells is not clearly defined from these images. The color of the keratin obscures the images of the cells despite the cells having been seeded on top of the gel rather than within.

Figure 5. ASC and PC12 co-culture shows that the cells can reach 100% confluency in a shared environment seeded in collagen (A) or keratin (B).

Discussion

Keratin Gelation Experiment Once the keratin gelation procedure was optimized, it was used in all of the experiments to determine how the substance affects the cells plated within. It is important to choose the gel that shows the correct properties at the lowest concentration to minimize issues with biocompatibility. The other concentrations tested did not gel, and concentrations higher than 25% may or may not have gelled at physiological conditions, indicating that this 25% gel is optimal. The keratin gel provides limitations. The brown color of the gel limits the visibility of cells when seeded on top of the gel.

The mechanical properties of the gel have not been quantified, but qualitative analysis gives evidence that this is an ideal gel to apply to nervous tissue regeneration. The gel is at a viscosity (even in its gelled state) that it can be injected into a nerve conduit with a syringe or 1000 ÂľL pipet, yet remains inside the conduit once placed.

The environment of the gel within a nerve conduit in vivo is not perfectly modeled when the sections of gel are submerged in PBS. When the conduit is placed within the peripheral limb tissue where a nerve injury has occurred, flowing blood and moving extracellular fluid will increase the rate of species transport in and around the conduit and the lumen filled with gel. This occurs via conductive and convective species transport.

In a vial sitting still within a 37°C incubator, there is no convective species transport out of the section of gel.

In the PBS, there is no protein concentration compared to the high protein concentrations within blood and extracellular fluid. The decreased concentration gradient out of the protein gel will slow species transport. A next step would be to do a quantitative degradation study that models the convective and diffusive forces of an in vivo environment. Quantitative rheological testing is a logical next step, as the ideal consistency for a hydrogel will allow the nervous tissue to grow through the conduit rather than act as a barrier to motion.

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Fluorescent Imaging Experiment In the qualitative study testing cell viability within keratin hydrogel, ASCs survive for at least 7 days.

It would be ideal to culture the cells longer, but the ASC media starts to dissolve the gel and refreshing the media becomes impossible without disturbing the cells plated in it.

The importance of the qualitative study of cell viability in the gel was the development of a method for visualizing the cells within the keratin gel. Glass dishes are ideal for imaging as the fluorescence is not distorted. The process of imaging cells within keratin gel was not optimized before this study. Seeding the cells on top of the gel allow for visualization when stained fluorescently. If the cells are seeded within the gel visibility is limited with the normally vibrant fluorescent staining.

Cytoviability Experiment The quantification of cell viability gave positive results, as the keratin was not toxic to the ASCs since there is no difference in the number of live cells in keratin compared to no keratin. There were significantly more dead cells in the keratin compared to the collagen group, and this may be because dead cells were not rinsed immediately from the keratin when they were plated.

This experiment shows the importance of determining a method of changing media for cells that are seeded on keratin gel because in this experiment it was not changed to not disturb the gel and allow for accurate results. Further experimentation using an MTT assay for cell metabolism would determine whether cells are more active in co-culture than if grown separately.

ASC & PC12 Co-Culture Experiment A co-culture of ASCs and PC12 cells proved that the cells proliferate when grown together. A next step is

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quantifying how fast each type of cell proliferates in different ratios with the others. It is the first step to determining whether ASCs will be a good addition to the keratin gel biomaterial for nervous tissue regeneration. The keratin gel shows great promise as a biomaterial for this application with or without the ASCs seeded within it in the nerve conduit. The co-culture experiment shows that the use of ASCs to increase nerve regeneration is promising since the cells were able to cohabitate successfully.

Conclusions

A need for a biomaterial within nerve conduits has presented itself, as the nervous tissue requires mechanical support similar to its own extracellular matrix for normal axonal protrusion and elongation.

The experiments performed demonstrate that ASCs are viable within our previously developed keratin hydrogel. This indicates the biocompatibility of a gel that also provides ideal properties at physiological conditions. The co-culture experiment further showed evidence that the combination of ASCs with nervous tissue could be beneficial to nervous tissue growth (and possibly regeneration). Both the qualitative and quantitative results from these experiments yield evidence that ASCs suspended in a keratin hydrogel may be a practical biomaterial for use in a nerve guide.

The addition of this biomaterial may raise the standard of treatment for peripheral nerve injury, improving the regeneration of injured nervous tissue.

Acknowledgements

I acknowledge the Department of Bioengineering and the Adipose Stem Cell Center summer grant 2014 allowing this experiment to be performed. I am grateful for the support from my graduate student mentor, Danielle Minteer, as well as the guidance from Dr. Kacey Marra and Dr. Peter Rubin.

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References

[1] Taylor, C. A., Dillingham, T., The incidence of peripheral nerve injury in extremity trauma, Am J of Physical Medicine & Rehabilitation 2008, Volume 87(5), Pages 381-388.

[2] Lee S. K., Wolfe S. W., Peripheral nerve injury and repair, J Am Acad Orthop Surg. 2000, Volume 4, Pages 243-252.

[3] Clarke, D., Richardson, P., Peripheral Nerve Injury, Current Opinion in Neurology 1994, Volume 7.

[4]Kim, Y., Bellamkonda, RV. The role of aligned polymer fiber-based constructs in the bridging of long peripheral nerve gaps, Biomaterials 2008, Volume 20, Pages 3117-3127.

[5] Bell. J. H. A., Haycock, J. W., Next generation nerve guides: materials, fabrication, growth factors, and drug delivery, Tissue Engineering Part B: Reviews 2012, Volume 18(2), Pages 116-128.

[6]Sivak, W. N., Marra, K. G., Delivery of chondroitinase ABC and glial cell line-derived neurotrophic factor from silk fibroin conduits enhances peripheral nerve regeneration.

[7] Davies, J. E., Archibald, S. J., Allan, C. E., Bioengineering for nerve repair in the future, J Am Soc for Surgery of the Hand 2004, Volume 3, Pages 134-142.

[8] Lin, Y., Marra, K., Injectable systems and implantable conduits for peripheral nerve repair, Biomedical Materials 2012, Volume 7(2): 1-9.

[11] Klymov, A., Walboomers, X. F., Nanogrooved surface-patterns induce cellular organization and axonal outgrowth in neuron-like PC12-cells, Hear Res 2015, Volume 320, Pages 11-17.

[12] Rouse, J. G., Van Dyke, M. E., A review of keratin-based biomaterials for biomedical applications, Materials 2010, Volume 3, Pages 999-1014.

[13] Fearing, B. V., Van Dyke, M. E., In vitro response of macrophage polarization to a keratin biomaterial, Acta Biomater 2014, Volume 10, Pages 3136-3144.

[14] Nectow, A.R., Marra, K., Biomaterials for the development of peripheral nerve guidance conduits, Tissue Eng, Part B, 2012, 18(1): 40-50.

[15] Rouse, J., Van Dyke, M. A review of keratin-based biomaterials for biomedical applications, Materials 2010, Volume 3, Pages 999- 1014.

[16] Lin, Y., Marra, K., Keritin gel filler for peripheral nerve repair in a rodent sciatic nerve injury model, Plastic Reconstructive Surg. 2012, Volume 1, Pages 67-72.

[17] Zhao, X., Loo, J. S., Calcium phosphate coated Keratin-PCL scaffolds for potential bone tissue regeneration, Mater Sci Eng C Mater Biol Appl 2015, Volume 49, Pages 746-753.

[18] Kakkar, P., Madhan, B., Development of keratinchitosan-gelatin composite scaffold for soft tissue engineering, Mater Sci Eng C Mater Biol Appl 2014, Volume 45, Pages 343-7.

[9] Pateman, C. J., Nerve guides manufactured from photocurable polymers to aid peripheral nerve repair, Biomaterials 2015, Volume 49, Pages 77-89.

[19] Nakata, R., Tanabe, T., Preparation of keratin and chemically modified keratin hydrogels and their evaluation as cell substrate with drug releasing ability, J Biosci Bioeng 2015.

[10] Babolmorad, G., Jodeiri, M., Enhanced PC12 cells proliferation with self-assembled S-layer proteins scaffolds, Appl Biochem Biotechnol 2015, Volume 1, Pages 223-231.

[20] Ostrovidov, S., Khademhosseini, A., Threedimensional co-culture of C2C12/PC12 cells improves skeletal muscle tissue formation and function, J Tissue Eng Regen Med 2014.

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Adipose-Derived Stem Cells from Diabetic Patients Display a Pro-Thrombogenic Phenotype Dominic J. Pezzonea,e, Jeffrey T. Krawieca,e, Justin S. Weinbauma,e, J. Peter Rubinc,e, and David A. Vorpa,b,d,e,f Corresponding author: David Vorp (vorp@pitt.edu) Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA b Department of Cardiothoracic Surgery, cDepartment of Plastic Surgery, dDepartment of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA e McGowan Institute for Regenerative Medicine, fCenter for Vascular Remodeling and Regeneration

a

Abstract

Current preclinical evaluations of tissue-engineered blood vessels (TEBVs) utilize a healthy patient’s own cells for treatment of vascular damage within the patient’s body. These models hold minimal relevance for clinical translation of TEBV therapy because they do not test cells from patients that will have a greater need for this therapy: patients at high cardiovascular risk, such as individuals with diabetes. Adiposederived stem cells (ADSCs) represent an ideal cell type for clinical translation, as these can be easily and plentifully harvested from high-risk patients.

Previously, in vivo evaluations of diabetic donor ADSC-seeded TEBVs in a rat model were performed at the University of Pittsburgh that displayed a markedly reduced patency rate compared to those from healthy donors (i.e., non-diabetics), and this was due to early (<1 week) thrombosis.

To probe mechanistically, this study analyzed diabetic donor ADSCs and assessed two critical components of thrombosis: platelet adhesion and fibrinolysis.

We hypothesized that diabetic donor ADSCs have an increased ability to bind platelets and/or a decreased fibrinolytic activity compared to healthy donor ADSCs, making them more prone to thrombosis when used in TEBVs. As diabetic patients are a key patient cohort that would require TEBV therapy, it is critically important for the clinical translation of TEBVs to investigate why diabetic donor ADSCs display a pro-thrombogenic phenotype. We found that human ADSCs from diabetic donors showed no difference in their ability to bind platelets compared to those from

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healthy donors. However, diabetic donor ADSCs displayed a reduced ability to degrade fibrin based on zymography of their secreted factors, providing a mechanistic explanation as to their pro-thrombogenic phenotype.

Keywords: thrombosis.

fibrinolysis,

Introduction

platelets,

stem

cells,

Thrombosis is a physiological phenomenon that occurs in blood vessels where, in response to an injury in the vessel wall, erythrocytes and platelets will bind together and form a complex held together by fibrin capable of blocking the injury site preventing fatal loss of blood. In most cases, thrombus formation is a healthy phenomenon needed to ensure survival after an injury to the vascular system. However, in certain instances, thrombosis can be the result of physiological malfunctions or unhealthy habits. In these instances, the formation of the blood clot can lead to life-threatening injuries or diseases. Thrombosis has two critical components: the adhesion of activated platelets to collagen exposed by vessel wall injury and the degradation of the fibrin mesh after the injury in the vessel wall has healed. An increase in thrombosis can be attributed to either an increase in the degree of platelet adhesion by the cell or a decrease in the degree of fibrin degradation by the cell’s secreted factors. If the body is unable to properly initiate or dispose of the blood clot, problems such as anemia, heart attacks and strokes can occur.

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One way of restoring normal physiological blood flow after thrombosis blocks flow through a vessel is through the use of vascular grafts, like the TEBV. The stem cell based TEBV used in the Vascular Bioengineering Laboratory at the University of Pittsburgh is a bilayered, degradable, polyurethane-based scaffold utilized in small-scale vascular graft treatments. This TEBV is seeded with autologous ADSCs harvested from the patient’s adipose tissue, cultured, and implanted into the patient for treatment. The problem with the clinical translation of stem cell based TEBV treatment is that pre-clinical testing does not test cells from patients that are at high cardiovascular risk.

One group of patients at high cardiovascular risk is diabetics. Diabetes is a disease characterized by high concentrations of glucose in the bloodstream.

Diabetics are at high cardiovascular risk due to high blood pressure and high levels of cholesterol [1] compared to healthy (i.e. non-diabetic) individuals.

High blood pressure causes more force to be exerted on the walls of the arteries, which can cause injuries in the vessel wall [2]. High levels of cholesterol in the blood can cause a buildup of cholesterol in the walls of the arteries, causing the vessel to stiffen and narrow [3]. Over 20 million people in the United States are diagnosed with diabetes [4], and this fact along with the symptoms makes diabetics a clinically relevant patient group.

Previously in the Vascular Bioengineering Laboratory at the University of Pittsburgh, experiments were conducted that seeded healthy and diabetic ADSCs into TEBVs and implanted the TEBVs into a rat model for 8 weeks [5]. The vessels were tested for patency, blood vessel-like composition and blood vessel-like mechanical properties. The results of this experiment showed that the healthy ADSC seeded TEBVs were all patent. However, the diabetic ADSC seeded TEBVs displayed a largely reduced patency rate. This posed the question of determining which area of thrombosis was affected in diabetic ADSCs, since the reason for the reduced patency rate was unknown. Thus, the objective of this study was to determine if diabetic ADSCs are more prone to the two critical areas of thrombosis, platelet adhesion and fibrin degradation.

Methods

Platelet Adhesion Human ADSCs were obtained from patients [6] who were classified into either healthy (non-diabetic, <45 years of age) or diabetic (diabetic, <45 years of age) cohorts (n=3 donors each). Human smooth muscle cells (SMC) were purchased from ATCC (PCS-100012). To test the ability of diabetic ADSCs to adhere platelets, monolayers were incubated with bovine platelet rich plasma anti-coagulated with a 7:1 vol/vol citrate dextrose for 30 minutes. This was followed by PBS washes to remove any unbound platelets. Platelets bound to ADSCs were labeled using a standard immunofluorescence protocol staining for CD41 (1:100, Kingfisher #CAPP2A) with counterstains for DAPI and F-actin (1:250, Sigma #P5282). Samples were imaged using NIS Elements software. Healthy ADSCs and SMCs were used as controls. Platelets bound to cell bodies and cell nuclei were manually counted in each image, which was then utilized to calculate the average number of platelets per cell.

This value was averaged between donors of the same group (i.e. healthy or diabetic) and compared utilizing a student’s t-test with a significant difference being defined at p<0.05.

Fibrin Degradation To test the fibrinolytic activity of diabetic ADSCs, conditioned media was obtained by replenishing culture media on near-confluent flasks of cells then collecting after two days in either normal (i.e. with serum) or serum-free conditions. Zymography, utilizing established methods [7] was performed using conditioned media in fibrin-based acrylamide gels (7.5% acrylamide, approximately 400 µg/mL fibrin) to provide a platform for fibrin degradation while also being able to identify active proteins (based on molecular weight) involved in this process. Following protein separation via electrophoresis, gels were incubated in a divalent cation reaction buffer (50 mM Tris HCl pH 7.4, 1 mM CaCl2, 1 mM MgCl2) for 1, 3, 5, and 7 days to allow for enzymatic degradation of the gel. Gels were then stained with Coomassie Blue to observe degradation bands. Healthy ADSCs were used as controls. The zymograms of healthy and diabetic ADSCs were analyzed qualitatively to determine if diabetic ADSCs displayed a reduced fibrinolytic activity. Presence or absence of bands in zymograms was determined visually.

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Results

ADSCs from healthy and diabetic patients showed no significant difference in degree of platelet adhesion.

ADSCs obtained from healthy and diabetic donors showed no difference in the number of bound platelets per cell (Figure 1) and in either case the platelet binding was lower than when human SMCs (i.e. positive control; data not shown) were used.

ADSCs from healthy and diabetic patients showed a difference in degree of fibrinolytic activity.

ADSCs obtained from diabetic donors displayed a

reduced fibrinolytic ability compared to those obtained from healthy donors, with a 1 week time point being determined as optimal. Earlier time points (i.e. 1, 3, and 5 days) showed similar results but with less prominent bands. The decreased diabetic fibrinolytic activity is particularly shown by a 31 kDa band (likely urokinase plasminogen activator [8]) and a 38 kDa band (likely plasminogen [9]) developing on zymograms from healthy ADSC but not from diabetic ADSC conditioned media (Figure 2).

Figure 1. No significant difference between healthy and diabetic ADSCs in platelet adhesion. (A) Example image showing platelets (red) bound to cell bodies (body: green, nuclei: blue). (B) The number of bound platelets per cell was not statistically significant (N.S.) between healthy and diabetic ADSCs.

Figure 2. Less intense bands present in zymogram of diabetic ADSC samples compared to healthy ADSC samples.

Zymogram showing degradation bands from healthy ADSC conditioned media (red) at 38 and 31 kDa that are less present with diabetic ADSCs (blue).

Secreted factors are from cells that were grown in solution with serum.

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Figure 3. Less intense bands present in zymogram of diabetic ADSC samples compared to healthy ADSC samples.

Zymogram showing degradation bands from healthy ADSC conditioned media (red) at 38 kDa that are less present with diabetic ADSCs (blue). Secreted factors are from cells that were grown in solution without serum. The use of serum-free media instead of normal ADSC growth media with serum showed that the secreted factors of the cells were degrading fibrin.

Diabetic ADSCs do possess some fibrinolytic activity noted by bands present at 83 and 88 kDa (likely plasminogen; data not shown [9]) similar to healthy ADMSCs. Bands were confirmed to not be due to the presence of serum within conditioned media, which can contain fibrinolytic factors, by utilizing serum-free media (Figure 3). The use of serum-free media does not resemble in vivo conditions, as in a normal blood vessel, serum is constantly flowing through the vessel.

However, this study was concerned primarily with the ADSCs themselves and whether the clotting observed in previous experiments was due to cellular properties only. This study did not analyze environmental conditions and their affect on the patency of the vessel.

Discussion

Human ADSCs from healthy and diabetic patients were compared based on their ability to adhere platelets and the ability of their secreted factors to degrade fibrin.

No statistical significance was found in the number of platelets bound to diabetic and healthy ADSCs. However, healthy and diabetic ADSCs showed a difference in the degree of fibrin degraded by their respective secreted factors. Previous experiments showed that TEBVs seeded with diabetic ADSCs display a decreased patency rate compared to those seeded with healthy ADSCs. This study served to expand upon these findings and to determine what caused diabetic ADSCs to cause clotting in TEBVs. The platelet adhesion experiment may have yielded

different results if the experiment was performed in a setup incorporating flow in order to more realistically model the environment in blood vessel. The number of platelets bound to ADSCs would most likely be higher than the values found in this study due to fluid dynamics and the mechanisms of platelet activation [10]. Similarly, the fibrin degradation experiment may have yielded different results if the degradation was measured in a three-dimensional setup similar to a clot formation in a blood vessel. In this setup, the amount of fibrin degraded by the secreted factors of the ADSCs would most likely be increased due to an up-regulation of secreted products of these cells [11].

These experiments, if performed, could give a better understanding of thrombosis in vivo.

The lack of statistical significance in the number of platelets bound to diabetic and healthy ADSCs but apparent higher fibrinolytic activity in healthy ADSCs offers a potential mechanistic explanation as to the reduced patency seen during in vivo testing with diabetic ADSC-based TEBVs. Cellular incorporation within TEBVs is often utilized to increased patency [12], and based on this study, seems to occur through the ability of those cells to efficiently break down thrombogenic material, such as fibrin. As diabetic ADSCs are unable to successfully perform this task, further exploration into mechanisms to enhance or supplement diabetic ADSCs will be imperative for the clinical translation of TEBVs.

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Conclusions

Human ADSCs from healthy and diabetic donors show no significant difference in platelet adhesion. Both of these values were lower than what would be expected using SMCs or an empty well. Diabetic donor ADSCs have a reduced fibrinolytic ability based on zymography of their secreted factors. Bands of lesser intensity were present in the diabetic ADSC samples.

Acknowledgements

This work was supported by the NIH (R21 #EB016138 to DAV), the AHA (#12PRE12050163 to JTK), and the Swanson School of Engineering at the University of Pittsburgh.

[7] Ahmann et al. “Fibrin degradation enhances vascular smooth muscle cell proliferation and matrix deposition in fibrin-based tissue constructs fabricated in vitro.” Tissue Engineering Part A 16.10 (2010): 3261-3270.

[8] Barlow et al. “Molecular weight studies on human plasminogen and plasmin at the microgram level.” The Journal of Biological Chemistry 10.3 (1969): 11381141.

[9] http://www.haemtech.com/Zymogens/Plasminogen.htm. Accessed 11/30/2014.

References

[10] Maalej et al. “Increased Shear Stress Overcomes the Antithrombotic Platelet Inhibitory Effect of Aspirin in Stenosed Dog Coronary Arteries.” Circulation (1996) http://circ.ahajournals.org/content/93/6/1201. full. Accessed 11/30/2014.

[2] http://www.heart.org/HEARTORG/Conditions/ HighBloodPressure/WhyBloodPressureMatters/ Heart-and-Artery-Damage-and-High-Blood-Pressure_UCM_301823_Article.jsp. Accessed 11/29/2014.

[11] Hong et al. “2D and 3D collagen and fibrin biopolymers promote specific ECM and integrin gene expression by vascular smooth muscle cells.” Journal of Biomaterials – Polymer Edition 10 (2007): 12791293.

[1] http://www.heart.org/HEARTORG/Conditions/ Diabetes/WhyDiabetesMatters/Cardiovascular-Disease-Diabetes_UCM_313865_Article.jsp. Accessed 11/29/2014.

[3] https://www.nhlbi.nih.gov/health/resources/heart/ heart-cholesterol-hbc-what-html. Accessed 11/29/2014.

[4] http://www.cdc.gov/diabetes/data/statistics/ 2014StatisticsReport.html. Accessed 11/22/2014.

[5] Krawiec et al. “Defective Smooth Muscle Cell Recruitment by Adipose-Derived Mesenchymal Stem Cells from Elderly Patients: A Cautionary Tale for Autologous Vascular Tissue Engineering,” International Society of Applied Cardiovascular Biology (ISACB) 14th Biennial Meeting, Cleveland OH, April 2014 (oral presentation)

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[6] Krawiec et al. “Adult stem cell-based tissue engineered blood vessels: A review.” Biomaterials April (2012): 3388-3400.

[12] Nieponice et al. “In vivo assessment of a tissueengineered vascular graft combining a biodegradable elastomeric scaffold and muscle-derived stem cells in a rat model.” Tissue Engineering Part A 16.4 (2010): 1215-1223.

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Open-Hole Strength of Bamboo Laminate for Low-Impact Timber Repair Shawn L. Platta and Kent A. Harriesb Department of Civil and Environmental Engineering, Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA a slp71@pitt.edu, bkharries@pitt.edu

Abstract

This experimental program investigated the open-hole tension capacity and the effects of staggered open-holes on the capacity of engineered bamboo strip product.

The strip is being developed as an alternative means of repairing or retrofitting damaged timber structural members. The strips displayed reliable patterns of material behavior. Net section reduction factors accounting for the stress-raising effect of the holes were identified. The impact of staggering the holes was observed to depend on the spacing between holes.

Keywords: Bamboo, open-hole, repair, strength

Introduction

Construction methods and materials is an ever evolving field. As new materials are introduced, or old materials re-introduced, the need for a complete understanding of the behavior of that material becomes exceedingly important. With an aging infrastructure, comes a greater need for repairs and even greater need for appropriate materials, means and methods for those repairs.

Additionally, recent interest has been redirected from traditional products to a focus of environmental concerns and sustainability. In the case of a timberframed building or bridge, replacing a damaged timber may be impractical, costly or aesthetically unpleasing; particularly in the case of historical preservation, replacement may be prohibited. With repair being the most cost effective option in many cases, the question becomes what kind of repair and with what kind of material? Traditionally repairs would often be completed using a steel plate bolted and/or adhered to the damaged timber. Such a repair has some limitations and potentially adverse effects on both the fabric and performance of the structure. For a historical repair, the use of adhesives may be prohibited as the repair must be able to be reversed at some time in the future [1].

Secondly, the introduction of a material with properties that significantly differ from the parent material may result in changes in the performance of the system that could, for instance, promote or magnify damage in other areas of the system as a result of altered load paths.

In many areas, fiber reinforced polymer (FRP) composites are at the forefront of repair technology [2]. Bamboo, an old material being ‘rediscovered’ due largely to its sustainable ‘credentials’, has been used in construction for millennia and could be considered nature’s original FRP. Bamboo is composed of vascular bundles consisting of longitudinal fibers bound together with a lignin matrix. The fibers are the source of Bamboo’s superior mechanical properties (including tensile capacity and toughness) but also make designing with bamboo unlike designing with most conventional materials [3].

There have been various investigations of the properties of full-culm bamboo (e.g., [4], [5] and [6]). But the use of the full-culm bamboo in construction is limited and its use as a potential repair material impractical, despite its favorable mechanical properties. Taking advantage of its superior mechanical properties, bamboo has been incorporated into many engineered products as diverse as flooring and “oriented strand board” (both commercially available today); gluelaminated members or “glubam” [7]; reinforcement for concrete and masonry [8], and; reinforcing fibers for mortars and polymers [9]. Nonetheless, the FRPlike aspect of bamboo materials (superior, although highly anisotropic mechanical properties) has not been leveraged in many cases; this has led to our interest in the repair field.

The focus of this study is the application of manufactured bamboo strips for structural repair used in a manner similar to modern FRP methods [2]. The

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application envisioned is the repair of timber structures for which bamboo, it is proposed, offers an aesthetically similar or virtually invisible alternative. The comparable stiffness of bamboo and timber results in a more natural interface with an existing system mitigating induced stress raisers often associated with repair methods. The tensile strength of bamboo is generally superior to that of most species of timber, thereby not only repairing but potentially strengthening the original structure without compromising aesthetics or the historic fabric of the structure. With an emphasis on repair of historic or architecturally sensitive structures, bolted external repairs, rather than adhesively bonded, are preferred [1].

Background When compared to isotropic materials such as steel, bolted connections in an orthotropic material such as most FRP materials and bamboo, will redistribute stresses markedly differently. The anisotropic and relatively brittle nature of bamboo and manufactured bamboo strips render conventional assumptions of net section design inappropriate [10]. Furthermore, even adopting guidance for orthotropic materials is likely inappropriate since the degree of anisotropy—the ratio of longitudinal to transverse material properties—is typically much greater for bamboo.

Studies have been conducted on the engineering properties of bamboo, specifically as they apply to fullculm behavior. Janssen [4] investigated mechanical properties of bamboo culms including bending, shear, tension, and compression behavior, relating most properties to bamboo density and limiting behavior based on transverse strain limits. Shear stress, Janssen concluded, was the primary cause of failure and he developed a four-plane shear test method which was later adopted by the ISO guideline [11]. This work was expanded upon by Arce-Villalobos [12] in which he examined the tensile properties of bamboo. This was done both in the longitudinal and transverse directions.

His conclusions indicated that transverse tension capacity and bamboo density were not correlated although longitudinal tension capacity and density were.

Additionally, Arce-Villalobos found the transverse tensile modulus of elasticity to be approximately one eighth of that measured in the longitudinal direction.

He ultimately concluded that “the majority of fittings based on some sort of penetration normally used in construction (nails, bolts, pegs) are not suitable for 78

bamboo because they create high tangential stresses.”

This promoted the investigation into the need for a reliable splitting test in which a pin test was devised to study this behavior within the confines of the full culm section [13]. Sharma [5] extended this work to include a varying angle bolt shear-out test based on earlier work by Janssen [4]. These previous studies have explored the properties of bamboo in culm form, however, there remains a need to address the properties of new engineered products. There is no previous research known that proposes the use of engineering bamboo as a potential repair material.

Objective Using a limit states approach, it is necessary to address all manners by which a structure or element may fail and design for these. The focus of the current work is on bolted connections for bamboo-strip repairs of timber members. The limits states of the connection include bolt shear; bearing/splitting of bamboo; shearout of bamboo, and net section failure of bamboo.

While all will eventually be addressed, the focus of the present work is the open-hole capacity of bamboo in tension thereby defining the net section capacity of the member. If the net tension limit state cannot be addressed satisfactorily, there is little reason to address the others, since a bolted connection will prove impractical.

Experimental Program

Engineered bamboo strip material obtained from China was used in the present study. The strips were fabricated of laminated radial-cut bamboo having a nominal thickness of t = 6.1 mm. Two types of strip were used: natural and caramelized. Caramelized strips are natural strips that have been subject to high heat in order to caramelize their lignin, thereby darkening the color of the strip. Specimens approximately 89 mm wide and 406 mm long were cut from the 203 mm wide strips. The 89 mm width (w) was chosen based on the closest size dimensional lumber (2 x 4) that would fit into the grips of the universal testing machine for future connection testing. Holes having a diameter of 13 mm and 25 mm were drilled varying the transverse gage (g) and longitudinal spacing (s) as shown in Figure 1.

Strain gauges were installed along the mid-line of the specimen at a location 51 mm from the hole furthest

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Figure 1. Specimen geometry and open-hole tension specimen prior to testing.

from the center of the specimen (Figure 1, photo).

This was done to investigate the strain redistribution in the specimen. A mechanical clip gauge was placed, centered vertically, on the edge of the specimens during testing (Figure 1, photo). Tests were conducted using a 600 kN capacity universal test machine.

Thirteen geometries (A-M) were tested (Figure 1) each consisting of 3 to 5 specimens (sample size, n). Two materials were considered, natural and caramelized, although due to material availability, not every geometry was tested with both materials. Specimens contained from 0 to 3 holes (N) having a diameter (h) of 13 or 25 mm with spacing (s) and gages (g) ranging from 0 to 51 mm. A typical specimen prior to testing having 3-13 mm holes at a gage of 25 mm is shown in Figure 1.

Test Results

Control specimens having no holes were tested both in their longitudinal (L) and traverse (T) orientations; results are shown in Table 1. Little difference was observed between the natural and caramelized

bamboo products with the exception of the transverse tension strength (FuT). This is an indication that the caramelization process adversely affects the lignin matrix but not the bamboo fibers. As can be seen, the degree of anisotropy in terms of strength and modulus (i.e., L/T) is significant.

Specimens tested having only a single row of bolts (i.e. s = 0) demonstrated some reduction in open-hole tensile capacity (Tn) beyond the calculated effect of net section area (An) as described by the factor k in Eq. 1.

This is an indication of the stress-concentrating effect of the holes.

Eq. 1 â&#x20AC;&#x192;Tn = kFuLAn In which FuL is the nominal (no-hole) longitudinal tensile capacity given in Table 1 and An = Ag â&#x20AC;&#x201C; Nht, in which Ag is the gross sections area and Nht is the area represented by N holes of diameter h through the strip thickness t.

Table 1. Average bamboo strip material properties

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As shown in Table 2, for the natural bamboo material, the observed open-hole strength reduction was marginal for 13 mm diameter holes and k â&#x2030;&#x2C6; 0.8 for 25 mm holes. The value of k â&#x2030;&#x2C6; 0.9 for 13 mm holes in the caramelized material. These values of k are greater than comparable values observed in GFRP materials [10] as should be expected due to the greater degree of anisotropy in the bamboo.

For connections requiring multiple bolts, a staggered geometry will generally a) be more compact; b) help to better engage adjacent bolts by reducing the shadowing effect along the direction of the applied load; and, c) results in an effectively larger net section in isotropic materials and therefore is a common detail. Table 3 shows results from cases in which staggered hole lines were tested. Only 13 mm diameter holes were considered and, due to limited material availability, only caramelized materials were tested. For the case of a

staggered connection, the stress, Fu, is calculated based on a plane net section accounting for all holes across the section (i.e., Ag â&#x20AC;&#x201C; Nht) regardless of stagger spacing (s).

In isotropic materials, the effect of staggering bolts is to increase the net section tensile capacity since the failure path between adjacent staggered bolts is longer than the path across the plane net section. While the results of this pilot study are not conclusive, providing a stagger is observed to increase the open-hole capacity marginally provided adequate spacing between the holes is provided.

Providing a center-to-center distance (c-to-c, in Table 3) of more than 51 mm (4 hole diameters) resulted in an increase in net section strength. Below 51 mm, interaction between stress concentrations developed at the holes is believed to occur resulting in a reduction in net section capacity. Further study is required to verify and quantify this effect.

Table 2. Average longitudinal open-hole strength of bamboo strip having single row of holes

Table 3. Average open-hole capacity and observed effect of hole stagger

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Conclusions

This experimental program investigated the openhole tension capacity of an engineered bamboo strip product and further considered any potential benefit from staggering the holes. The strips displayed reliable patterns of material behavior. Net section reduction factors accounting for the stress-raising effect of the holes were identified. The impact of staggering the holes was observed to depend on the spacing between holes.

Additional study is necessary to quantify these effects since the effect of introducing the hole is detrimental (Table 2), while the effect of staggering the holes may counteract this effect (Table 3). Continued study using digital image correlation is planned and should help to address the apparent interaction observed.

Apparently detrimental effects on transverse properties resulting from the caramelization process were identified. For this reason, future work will use only the natural material.

[3] Harries, K.A., Sharma, B. and Richard, M.J. (2012) Structural Use of Full Culm Bamboo: The Path to Standardization, International Journal of Architecture, Engineering and Construction. Vol. 1, No. 2. pp 66-75.

[4] Janssen, J. (1981). Bamboo in building structures.

PhD Thesis, Eindhoven University, Eindhoven, The Netherlands.

[5] Sharma, B. (2010) Seismic Performance of Bamboo Structures. PhD Thesis, University of Pittsburgh.

[6] Richard, M.J. (2013) Assessing the Performance of Bamboo Structural Components. PhD Thesis, University of Pittsburgh.

[7] Xiao, Y., Shan, B., Chen, G., Zhou, Q., and She L.Y. (2008) Development of a new type of Glulam – Glubam. Modern Bamboo Structures, Xiao, Y., Inoue, M., and Paudel S.K., eds., London, UK, 41-47.

[8] Ghavami, K., (2005) Bamboo as reinforcement in structural concrete elements. Cement and Concrete Composites, Vol. 27, pp 637-649.

This study will continue to investigate all limit states associated with bolted connections in an effort to develop a practical external retrofit system suitable for timber structures.

[9] Li, F., Liu, Y.F., Gou, M., Zhang, R. and Du, J. (2011) Research on Strengthening Mechanism of Bamboo Fiber Concrete under Splitting Tensile Load, Advanced Materials Research, Vol. 374-377, pp 14551461.

Acknowledgements

[10] Cunningham, D., Harries, K.A. and Bell, A.J., (2014) Open-Hole Tension Capacity of Pultruded GFRP Having Staggered Hole Arrangement, submitted to Composite Structures

Testing was conducted in the University of Pittsburgh’s Watkins-Haggart Structural Engineering Laboratory (WHSEL). Materials were provided by Dr. Qingfeng Xu of the Shanghai Research Institute of Building Sciences. Funding was provided by WHSEL, the Swanson School of Engineering, and the Office of the Provost.

References

[1] United States Department of the Interior (1995) The Secretary of the interior’s Standards for the Treatment of Historic Properties with Guidelines for Preserving, Rehabilitating, Restoring and Reconstructing Historic Buildings, National Park Service, Washington, DC.188 pp.

[11] International Standards Organization (ISO) (2004) ISO 22157-1 Bamboo - Determination of physical and mechanical properties—Part 1: Requirements, Geneva, Switzerland.

[12] Arce-Villalobos, O. (1993) Fundamentals of the design of bamboo structures. PhD Thesis, Eindhoven University, Eindhoven, The Netherlands.

[13] Mitch, D., Harries, K. and Sharma, B. (2010).

Characterization of splitting behavior of bamboo culms.

ASCE Journal of Materials in Civil Engineering, Vol. 22, No. 11, pp.1195-1199.

[2] American Concrete Institute (ACI) (2008) ACI 440.2R-08 Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening of Concrete Structures, American Concrete Institute, Farmington Hills, MI, USA.

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Surge Generator Design for Electric Power Systems Lab Zachary T. Smith1,a, Michael R. Doucettea, James D. Freemana, Ansel Barchowskya, Brandon Grainger2,a, Gregory F. Reeda, and Daniel J. Carnovaleb Electric Power Systems Laboratory Eaton Power Systems Experience Center Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA Email: 1zachary.t.g.smith@gmail.com, 2bmg10@pitt.edu a

b

Abstract

This document gives a cost-effective alternative to purchasing a surge generator from a vendor by designing and prototyping one within the university.

The generator is designed to deliver a simulated lightning strike with surge characteristics as defined by electrical standards. MATLAB/Simulink was used to simulate the operation of the surge generator. A prototype was constructed, surge characteristics were measured using equipment capable of sampling at 6 MHz in order to validate the design, and met IEC 61000-4-5 and IEEE C62.41-1991.

Keywords: Instrumentation, Lightning Surge, Power Quality, Transient

Introduction

Purpose The purpose of this project was to design and construct a surge generator capable of delivering a representative lightning strike to a load. The surge generator was designed for research and demonstrative purposes in the University of Pittsburghâ&#x20AC;&#x2122;s Electric Power Systems Lab (EPSL). Current progress of the design and construction of the surge generator has been performed

by two undergraduate research groups over an eight month period.

Project Goal The main objective of this project was to provide a cost-effective alternative to purchasing surge generating equipment from a vendor. At the time of the projectâ&#x20AC;&#x2122;s initiation, the cost of a surge generator with the desired capacity ranged from $30,000 to $100,000.

This project was expected to produce a similar surge generator with a budget of around $5,000. Besides the cost savings, this surge generator design has the capability of being reconfigured to obtain any desired surge waveshape by simply changing the front and tail resistances.

Surge Generator Design and Operation The surge generator design was selected to match standard requirements as defined by the International Electrotechnical Commission (IEC) and the Institute of Electrical and Electronic Engineers (IEEE). The design was based on a combination wave generator as defined by IEC 61000-4-5, [1], which is shown in Figure 1.

Figure 1. Generalized form of the combination wave generator [1]

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The surge generator operates by charging the energy storage capacitor from the high-voltage source. When the switch closes, the stored energy in the capacitor travels through the resistor and inductor network and into the load. The output surge waveform varies depending on the values of the resistors and inductors downstream of the switch.

Open Circuit Voltage and Short Circuit Requirements Power surges have two characteristic waveforms: the open circuit voltage and the short circuit current.

These waveforms define the surge itself, since there is no load associated with open circuit voltage or short circuit current. Each waveform has a front time and duration (or tail time), each of which can be calculated per standard IEC 61000-4-5 [1]. The front time defines the period of time to reach the peak and the tail time defines the time to fall to half of the peak as shown in Figure 2. According to these standards set by IEC and IEEE, a typical lightning strike is a 6kV ± 300V voltage surge with voltage and current/time waveforms as

follows (see Figure 2 for visual placements of defined times): • 1.2µs ± 0.36µs x 50µs ± 10µs open circuit voltage • 8µs ± 1.6µs x 20µs ± 4µs short circuit current These required surge properties are defined by IEC 61000-4-5 and IEEE C62.41-1991. The surge generator was designed to meet these standards.

Decoupling Network In order to superimpose the voltage transient waveform on the normal operating voltage of the equipment under test (EUT), a decoupling network was required. A decoupling circuit was necessary to isolate the normal 120VAC source from the 6kV surge and to couple the surge to the load. The design for these networks can be found in IEC 61000-4-5. The general form of these circuits is shown in Figure 3. The coupling capacitor for the design was set at 18µF, but the other component values in the decoupling network were taken from a previous design performed by Advanced Energy [2].

Figure 2. Required voltage/time waveform for an open circuit load (Left). Required current/time waveform for a short circuit load (Right). [1]

Figure 3. General circuit diagram for coupling and decoupling networks for a surge generator [1]

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Methods

Surge Generator Component Values and Design The first task was to design the surge generator circuit.

Shown in Figure 4 is the schematic of the entire surge generator circuit based on the design provided by Advanced Energy [2]. The design includes a boost converter, which charges a high power capacitor to 6kV. Once the capacitor is fully charged, the user can send a signal to a high voltage â&#x20AC;&#x153;fireâ&#x20AC;? relay, which will discharge the capacitor. This discharge is coupled to a load and mimics the high voltage caused by a lightning strike across the load. Any load can be coupled to the surge generator; but for demonstrative purposes, a light bulb is considered the easiest and most costeffective way to demonstrate the effects of a surge on a load showing the normal operating condition at 120V and the dramatic effect exploding the light bulb with the 6 kV surge. Resistors, capacitors, and inductors are used to tune the front and tail of the surge to meet

the standards as mentioned in Section 1.4. Capacitors and inductors are used to decouple the load from the 120VAC power supply, which prevents the surge from propagating to the source.

Simulation of Surge Generator The design shown in Figure 4 was simulated per IEC and IEEE standards by using MATLAB/ Simulink.

The two design criteria (open circuit voltage and short circuit current) were measured by setting up two different models. The Simulink models were used to simulate the open circuit voltage waveform and the short circuit current waveform. Adjustments were made to component values until the models satisfied the design criteria. A decoupling network was then added to the Simulink model to simulate the entire circuit as seen in Figure 5. The decoupling network was verified to isolate the surge from the source.

Figure 4. High level schematic of surge generator with component values

Figure 5. Final Simulink model of surge generator

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Figure 7. Prototype surge generator circuit

Results

Figure 6. Surge Generator Circuit Schematic

Construction and Testing of Prototype After receiving satisfactory simulation results, a prototype circuit design schematic was developed.

This schematic includes all components that are necessary for the construction of the prototype and is shown in Figure 6. A picture of the physical prototype circuit is shown in Figure 7. All voltage and current measurements for the surge generator were measured at Eaton’s Power Systems Experience Center with an Eaton Power Xpert Meter capable of measuring high current / voltage transients at a 6 MHz sampling rate.

MATLAB Simulink Test Results The surge generator simulation model was first tested with a source of 6.5kVdc. The results of the first simulation can be found in Table 1. A point to note is that the open circuit voltage surge reached 5.97kV, which meets the expected 6kV requirement. Also, the front time and tail time parameters were within the tolerances set by IEC 61000-4-5 and IEEE C62.41-1991.

Next, the surge generator simulation model was tested to meet the short circuit current waveform standards with the same 6.5kVdc source. The results of this test can be found in Table 2. Note that the current surge reached 2.96kA, which met the expected 3kA requirement. Like the open circuit voltage waveform, the short circuit current waveform also met the front time and tail time parameters.

Table 1. Simulink results of open circuit voltage waveform analysis – 6500V source

Table 2. Simulink results of short circuit current waveform analysis – 6500V source

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Table 3. Results of Open Circuit Surge Test on the Prototype Circuit

The complete simulation model shown in Figure 5 was tested to verify that the power surge did not propagate back to the source. This model also was used to get an estimate of the surge characteristics that the load will experience. When performing experiments on the prototype, the voltage waveform seen by the load during a surge was measured and shown in Figure 10. In this figure, the light bulb exploded at the initial peak. An electrical arc established across the air gap after the rupture occurred introduced nonlinear resistance into the current path impacting the voltage waveform until the arc was extinguished approximately 25 uS after the initial peak.

Prototype Test Results The prototypeâ&#x20AC;&#x2122;s open circuit voltage waveform was captured with an Eaton Power Xpert meter. The results of the open circuit test are provided in Table 3. The waveforms captured by the Power Xpert meter are shown in Figure 8 and Figure 9. The surge generator did not provide the full 6.5kV source to charge the capacitor due to operational issues with the boost converter. The generatorâ&#x20AC;&#x2122;s output voltage was therefore reduced as well. The front time and tail time parameters were within the tolerances expected by the MATLAB Simulink simulation.

Figure 8. Open Circuit Full-Voltage Surge Waveform Showing Rise Time (Volts on y-axis)

Figure 9. Open Circuit Full-Voltage Surge Waveform with Undershoot (Volts on y-axis)

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Figure 10. Full-Voltage Surge Waveform as seen by a Light Bulb (Volts on y-axis)

Discussion

A full-voltage surge short circuit test was not performed due to time restraints. In order to fully validate the design, future work on a short circuit test will need to be completed. In addition, the surge generator prototype did not produce the full 6.5kV, but instead had a maximum charging voltage of 4.5kV. Future work will include changing the storage capacitor and boost converter to allow the surge generator to operate properly at the full 6.5kV. The high voltage relay did not operate as expected; therefore, a spark gap was used to pass the high voltage surge. The spark gap uses an air gap to pass the surge when the voltage reaches a critical value, which explains why the peak voltage was 3288V instead of 4500V. Spark gap issue corrections or fine tuning the length of the air gap will yield a 4500V surge.

The short circuit current tests are left to be performed on the prototype and measured. Once the full voltage and current tests abide by the electrical standards mentioned, the prototype will be neatly packaged and brought to the EPSL to be used as a future demonstration piece for visitors and used in graduate level transients courses.

Conclusions

The paper presented an initial prototype of a high voltage surge generator design benchmarked against leading standards in the industry and well suited for a university laboratory to meet research needs. The total cost of the prototype surge generator design without hardware enclosure is $5,247.

Acknowledgements

This work was supported by funding and equipment donations from Eaton.

References

[1] International Standard. IEC 61000-4-5. [Available Online]:http://www.sanki-e.com/uploadimg/ contents/20100722110236817.pdf.

[2] Powell, D. E., Hesterman, B. â&#x20AC;&#x153;Introduction to Voltage Surge Immunity Testingâ&#x20AC;? IEEE Power Electronics Society. September 18, 2007. [Available Online]: http://www.denverpels.org/Downloads/ Denver_PELS_20070918_Hesterman_Voltage_ Surge_Immunity.pdf.

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Regenerating Composite Layers from Severed Nanorod-Filled Gels Stephen C. Snow, Xin Yong, Olga Kuksenok, and Anna C. Balazs Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

Building upon previous computational efforts to design a self-regenerating polymer gel utilizing interfaciallyactive nanorods, we explore a method to regrow a composite polymer matrix with embedded nanorods which better resembles the uncut material. When the regenerated gel layer reaches certain heights, nanorods localized at the interface escape and consequently diffuse into the regrowing gel. With these nanorods dispersed in the new gel, a nanocomposite polymer matrix forms that resembles the original composite.

In order to compensate for the loss of the interfacial nanorods that served as anchors, we introduce a new cross-linker into the original gel that forms bonds with the active chain ends of the regrowing gel. These covalent bonds bridge the cut gel and regenerated layer, creating a coherent system. The strength of the interface can be tuned by varying system parameters, and a uniform degree of cross-linking throughout the entire gel can be achieved with optimal parameters.

Keywords: Self-regeneration, polymer nanocomposites, atom transfer radical polymerization, dissipative particle dynamics

Introduction

The development of self-regenerating materials has been a focus in materials science for several decades, owing to the impact of such a breakthrough. The utility of self-regeneration would extend the lifetime of materials, ranging from the small scale, such as device screens, to a much larger scale, for example aircraft hulls. An ideal material would be developed to be both self-regenerating and self-replenishing [1].

The primary objective of this project is to devise the regeneration of a truly composite polymer matrix with embedded nanorods. This is to be accomplished through computational modeling. Based on our previously developed model of a self-regenerating material [2], we develop a means for strengthening 88

the interface between the two gels. This proves to be a challenge because the interactions between the separate moieties are repulsive, which creates a natural gap at the interface as the nanorods diffuse upward into the outer solvent and lose their anchoring effect.

Although it is possible to keep the nanorods anchored at the interface by tuning monomer concentrations, which yields a coherent interface, the resulting composite is not ideal due to the lack of embedded nanorods in the regenerated layer. It is important for the nanorods to disperse throughout the two layers so that the characteristics of the uncut gel are replicated as closely as possible. The presence of nanorods in the regenerated layer also adds strength to the new gel [3,4].

To solve this issue, we introduce a new cross-linker to the system, which bonds with both the original gel and the regrown gel. This improvement allows us to replicate a â&#x20AC;&#x153;carpentry trickâ&#x20AC;? for joining two wood pieces. This approach utilizes two different fasteners at a joint, nail and glue, to form temporary and longterm bonds, respectively. In our system, the nanorods initially act as nails to hold the two gels together. Next, the newly introduced reactive species will form intergel cross-links at the interface while the nanorods are moving out, fulfilling our main objective to create a strong interface.

Methods

Our system is modeled using a dissipative particle dynamics (DPD) approach, a coarse-grained, particlebased method that allows us to reach larger length and time scales than a full-atom molecular dynamics approach [5]. The system contains a chemically cross-linked gel network arranged in a diamondlattice structure, which has polymer chains modeled by the coarse-grained, bead-spring model with bond and angle potentials. The original gel in the system is attached to a substrate and swollen in a good solvent.

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The simulation box is periodic in lateral directions.

The temperature of the system is 28 degrees Celsius and at this temperature, the polymer concentration of the gel is 0.27. The outer solvent in the system contains monomer and cross-linker and is weakly incompatible with the inner solvent.

We use amphiphilic nanorods, where the rod portion is compatible with the outer solvent and the end-grafted chains are compatible with the inner solvent. The polymer gel is regrown through an atom transfer radical polymerization (ATRP) living copolymerization process of monomer and crosslinker [6,7]. ATRP was first integrated into the DPD simulations in our previous work [2], and in this

simulation we adopt the same approach. The model is expanded to incorporate inter-gel cross-linking, which occurs when polymerization is nearly complete.

The additional cross-linker is introduced to the gel by replacing a fraction of the chain beads in the original gel layer (Figure 1). At the start of simulations, the nanorods are vertically anchored at the interface with the end-grafted chains within the original layer of gel.

For these simulations, the simulation box height is increased so that the nanorods have enough space to move within the regrown gel layer. Additional details about all other simulation parameters can be found in the supporting information for our previous model (Sections S1-S3) [2].

Figure 1. Inter-gel cross-linker within the system. Left: System contents. Nanorod has yellow end-grafted chains and purple initiator sites. Original gel is green and contains chain beads. Wall beads are brown. Red additional cross-linker replaces a fraction of chain beads at start. Right: Formation of an inter-gel cross-link. The radical from an active end (indicated by an asterisk) is first transferred to the additional cross-linker. At this point, the additional cross-linker with the radical may now form another bond. An open circle indicates that the particle has not yet fully reacted. Prp,S (set to 0.075) is the reaction probability between a monomer with the radical and an additional cross-linker.

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the additional cross-linker results in notable changes at the interface. With no additional cross-linking, there is a decrease in cross-linking at the interface, but introducing the additional cross-linker to the system eliminates this drop.

Figure 2. Position of center of mass of nanorods. Left: The z position of the center of mass of the four nanorods is measured over time for different initial monomer concentrations in the solvent. Units for time, position, and subsequent figure variables are given in dimensionless simulation units. For the relationship between dimensionless simulation units and experimental values see Supporting Information (S1-S3) of original system [1].

Right: system snapshot for [M]0 = 40% (top) and [M]0 = 10% (bottom) at end of simulation. Regrown gel is blue.

Variations in Amount of Additional Cross-Linker We then varied the initial concentration of additional cross-linker to compare the effects on gel density.

The gel density graph (Figure 4) shows that for high concentrations of additional cross-linker, 10% or higher, there is an increase in density below the interface. Conversely, with 5% additional cross-linker, the density remains roughly uniform throughout the interface. We note that using 2% additional crosslinker is not sufficient to increase the interface density to original gel levels (see discussion in section 4.3).

Results

Nanorod Diffusion In order to better quantify the relationship between initial monomer concentration and average position of the nanorods, we first conduct the simulation without any additional cross-linker, while monitoring the position of the center of masses of the nanorods (Figure 2). For higher concentrations of monomer, the nanorods, initially anchored at the interface, move upward into the blue gel.

Effect on Interface Next, a simulation is performed with the additional cross-linker constituting a fraction of the original gel chain beads. The modified gel is essentially a copolymer gel with one monomer containing reactive groups that remain inert during the polymerization reaction, resembling those used in click chemistry [8]. After running new simulations with the additional cross-linker, a clear change in the interface is evident, as seen in Figure 3a. Where there had once been a separation at the interface, the additional cross-linker holds the two gels together neatly through covalent bonds.

By counting the amount of reacted cross-linkers at various layers (Figure 3b), we observe that introducing 90

Figure 3. Effect of additional cross-linker on interface. (a) Comparison of system with no additional cross-linker (left) and with 5% additional cross-linker (right). Reacted additional cross-linkers are red beads. (b) Vertical profile of number of reacted cross-linkers. Reacted cross-linker is defined as a cross-linker that has formed at least 3 total bonds. The interface is located at z=30 as is shown.

The results are averaged across four independent trials.

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The total and inter-gel cross-linking for the different concentrations of additional cross-linker were then measured and compared (Figure 5). With 10% or greater additional cross-linker, there are massive spikes in cross-linking density near the interface, with only a small portion of those being inter-gel cross-links. In contrast, for both 2% and 5% additional cross-linker, total cross-linking at the interface remains roughly homogeneous (see discussion in section 4.3).

Discussion

Nanorod Diffusion The clear relationship between initial monomer concentration and the position of the nanorod center of mass over time can be attributed to the nanorodâ&#x20AC;&#x2122;s connection to the gel. This is a result of the regenerated gel, which is connected to the nanorods by the initiator sites, pushing down on the green gel during growth, causing the nanorods to rise in return. This leads to dispersion of nanorods in the regenerated layer, but it also results in a gradual separation between the gels as the regenerated layer moves away from the interface.

Figure 4. Effect of cross-linking concentration on gel density. Vertical density profile of the bottom half of the system for various additional cross-linker concentrations.

The original layer of the gel corresponds to z<30, the interface is at z=30, and the regrown layer of the gel corresponds to z>30. For each cross-linker concentration, the results are averaged across four independent trials.

Figure 5. Inter-gel cross-linking at interface. (a) 2% additional cross-linker (b) 5% (c) 10% (d) 15%. An inter-gel cross-linker is defined as an additional cross-linker that has formed at least one bond with a particle in the regrown gel. The original layer of the gel is corresponds to z<30, the interface is at z=30, and the regrown layer of the gel corresponds to z>30. For each cross-linker concentration, the results are averaged across four independent trials.

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Effect on Interface The additional cross-linker creates a covalent bond between the original gel layer and the regenerated layer. The distinct decrease in density mentioned in Section 3.2 represents the lack of cross-linking at the interface. By introducing the additional crosslinker, the drop disappears because the interface is strengthened. Although the cross-linking at this point is not fully uniform throughout the layers, the interface is no longer the weak point in the composite.

causing the green gel to become more heterogeneous.

These insights aid in the determination of methods to synthesize a self-regenerating material that can duplicate its original strength after damage.

Variations in Amount of Additional Cross-Linker The results from Section 3.3 indicate that using an additional cross-linker concentration above 5% alters the density of the original gel layer. This effect is not desired, as it creates a more heterogeneous composite. An additional cross-linker concentration of 5% produces the desired effect of increasing the cross-linking at the interface, while still maintaining a roughly homogeneous composite. Using excessively low levels of cross-linker fails to establish a uniform density throughout the interface, as seen with 2% additional cross-linker in Figure 4 where the gel density exhibits a decrease around the interface.

References

This density phenomenon can be explained by the graphs measuring the total cross-linking for various concentrations of additional cross-linker (Figure 5).

The massive spikes in cross-linking density for 10% or greater additional cross-linker indicate that with those concentrations, the majority of reacted cross-linkers form intra-gel links, causing clumping to occur below the interface. In other words, the partially reacted additional cross-linkers near the interface form many bonds with additional cross-linkers in the rest of the original gel layer, resulting in a density gradient at this layer.

[4] Y. Kojma et al., Mechanical properties of nylon 6-clay hybrid, J. Mater. Res. 8 (1993) 1185-1189.

Conclusions

By using a concentration of 5% additional cross-linker, a strong interface can be achieved without sacrificing the goal of maintaining a roughly homogenous composite. Through the simulations, issues with using an excessive amount of this cross-linker were also discovered. Many of these reacted cross-linkers do not contribute to the strength of the interface, instead only

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Acknowledgements

The authors gratefully acknowledge the National Science Foundation (grant EEC-1359308), Swanson School of Engineering, and Office of the Provost for financial support of this research.

[1] A. C. C. Esteves, Y. Luo, M. W. P. van de Put, C. C. M. Carcouët, G. de With, Self-Replenishing Dual Structured Superhydrophobic Coatings Prepared by Drop-Casting of an All-In-One Dispersion, Advanced Functional Materials 24 (2014) 986-992.

[2] X. Yong, O. Kuksenok, K. Matyjaszewski, A. C. Balazs, Harnessing Interfacially-Active Nanorods to Regenerate Severed Polymer Gels, Nano Letters 13 (2013) 6269-6274.

[3] A. Usuki, M. Kojima, A. Okada, Y. Fukushima, O. Kamigaito, Synthesis of nylon 6-clay hybrid, J. Mater.

Res. 8 (1993) 1179-1184.

[5] R. D. Groot, P. B. Warren, Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation, J. Chem. Phys. 107 (1997) 4423– 4435.

[6] K. Matyjaszewski, J. H. Xia, Atom Transfer Radical Polymerization, Chem. Rev. 101 (2001) 2921– 2990.

[7] H. F. Gao, P. Polanowski, K. Matyjaszewski, Gelation in Living Copolymerization of Monomer and Divinyl Cross-Linker: Comparison of ATRP Experiments with Monte Carlo Simulations, Macromolecules 42 (2009) 5925– 5932.

[8] J. Xu, J. Ye, S. Liu, Synthesis of Well-Defined Cyclic Poly(N-isopropylacrylamide) via Click Chemistry and Its Unique Thermal Phase Transition Behavior, Macromolecules 40 (2007) 9103-9110.

Undergraduate Research at the Swanson School of Engineering

Ingenium 2015

Pore Distribution, Mechanical and Compositional Characterization of Inconel 718 Manufactured

by Laser Engineered Net Shaping Erica Stevens, Jakub Toman, Pu Zhang, Albert To, and Markus Chmielus Department of Mechanical Engineering and Materials Science, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

The structure and properties of Inconel 718 samples created using laser engineered net shaping were explored. Inconel 718 powder was used to produce prisms with an Optomec LENS 450, a direct metal deposition system. The prisms were examined as deposited with optical microscopy, then ground and polished for further analysis. After polishing the top face, the scanning direction of the laser was revealed in the hatching due to the distribution of small dots in the sample. In contrast, the contour contained an increased density of randomly distributed dots. Upon further polishing, the scan direction was no longer discernible and the dots became randomly distributed. There is therefore a variation in dot distribution within and between layers, as well as between the hatching and the contour. When analyzed using energy dispersive x-ray spectroscopy, the dots proved to contain trapped polishing material, leading to the conclusion that they were pores. Further micrographs parallel and perpendicular to the substrate showed that the pore density increased between melt pools. Hardness testing revealed that the hardness is higher in the hatching (less pores) than in the contour (more pores), linking the mechanical properties to the structure created by the manufacturing method.

Keywords: Additive Manufacturing, Inconel, pores, hardness

Introduction

Additive Manufacturing Additive manufacturing (AM) is a method of building parts layer by layer from a computer-aided design (CAD) model. AM has several benefits over traditional manufacturing. Since AM is a selectively additive and not a subtractive technique, it produces less waste material. It is also possible to fabricate more

complicated geometries that exceed the capabilities of traditional subtractive manufacturing [1]. Despite these benefits, certain AM techniques are not able to be used without reserve, because the effects of these processes on the structure and consequent properties of materials still requires further exploration. In particular, there is often porosity in AM parts due to incomplete fusion and gas entrapment, causing a detrimental effect on mechanical behavior [2]. This paper focuses on Inconel 718 (IN718) made by Laser Engineered Net Shaping (LENS), to gain knowledge about the microstructure and properties of LENS-manufactured IN718 parts, in order to better understand and eliminate defects in industrial and structural applications.

LENS Manufacturing LENS is an AM process that uses a laser to selectively melt powders introduced through a powder feed system [2,3,4]. A laser beam is first focused on a small area of solid metal, creating a melt pool. Then metal powder is sprayed through nozzles towards the melt pool. Some powder particles deposit on the melt pool, becoming part of it and adding to its volume. The deposition nozzles and laser scan over each layer in a manner prescribed by a CAD model, then proceed on to the next layer. In this way, a 3D part is created using layered 2D slices [2].

Due to the laser melting, LENS production creates intense thermal profiles with several cycles of quick heating and cooling, which strongly influences the microstructure. Some possible effects are the formation of dendrites and microsegregation [5]. Though LENS is an attractive method for parts of complicated geometries and repair of parts, the structure and properties based on thermal cycles imposed on the final parts are still being evaluated.

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Inconel 718 IN718 is a high-temperature nickel-based superalloy which has iron, chromium, and niobium as its main alloying elements. IN718 has a nickel-based FCC matrix phase and an ordered strengthening phase that is BCT and is composed of Ni3Nb. It is used frequently in engineering applications for its high strength and corrosion resistance. IN718 is commonly produced by powder metallurgy (PM) due to complicated part geometries [6]. AM could also be a solution to creating these and even more complex geometries. Currently, the LENS process is preferred for repairing damaged superalloy parts in the aerospace industry, due to several benefits over welding, including the small heat-affected zone that it produces [4].

There are several phases found in IN718: γ, γʹ, γʺ, and carbides and borides. γ is the nickel-rich FCC matrix phase. γʹ and γʺ are coherent with the matrix phase, and γʺ is preferred over γʹ in nickel-iron superalloys like IN718. γʺ is an ordered BCT phase that (in IN718) is the strengthening phase and is composed approximately of Ni3Nb and appears disc-shaped within the microstructure. Though preferred, γʺ is much more difficult to observe in the microstructure, due to the fact that it is nano-sized [6,7].

LENS fabrication of IN718 produces a unique thermal profile due to the focused laser scanning across the surface of the material. Any given area of material will experience thermal cycling: hottest when it is the melt pool, cooling as the laser moves away, and heating up again as the next layer is built on top of it. This cycling continues with subsequent layers, with a general decreasing trend [8]. Since the LENS process produces a very different thermal

profile than traditional manufacturing, it necessarily produces a very different solidification microstructure [9]. The microstructure of materials directly affects their mechanical properties. It has been reported that the properties of LENS-fabricated materials are generally comparable or superior to those produced by traditional manufacturing [3,5,9,10]. This is due to the fine microstructure created during rapid solidification.

However, since the thermal profile of each section of the part is not identical, the mechanical properties will vary throughout the part.

Methods

Materials and Sample Details Using an Optomec LENS 450 system, two samples (labelled A and B) were built on an IN718 substrate with IN718 plasma rotating electrode process (PREP) powder of 44-150 µm diameter. For both samples, a laser power of 250 W and a hatch distance and layer height of 0.254 mm were used. Sample A used a laser scanning speed of 2 mm/s, and sample B used a laser scanning speed 2.27 mm/s. Each sample was a prism of approximately 10 mm x 10 mm x 5 mm with 10 layers.

Each layer was printed by first building the contour, the outside edge. After this square frame was created, the interior was filled in by the laser rastering back and forth in parallel lines to create what is referred to as the hatching. These lines were made at 0° in the first layer, 45° in the second layer, 90° in the third, 135° in the fourth, and so on. The hatching overlapped the contour slightly, meaning that the contour was overbuilt and became higher than the hatching of the same layer. A schematic of the build process and terminology can be seen in Fig. 1. Neither of the samples was heat treated after being fabricated.

Figure 1. Schematic of build process and terminology on a (a) cross section and (b) top view.

The contour was built first in each piece (green), followed by the hatching at varying angles (white), using the tabulated build parameters. The sample in each image is 10 mm across.

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Imaging and Sample Preparation Each sample was imaged in the as-printed condition with a Keyence digital optical microscope (DOM) with a dark field Z20 lens and a multi-diffused adapter at varying magnifications. After the initial structure was documented, the samples were separated with a metallographic saw, cutting the substrate around each prism. This resulted in two samples: small pieces of substrate, each topped with one of the prisms. Each of these pieces was then hot compression mounted, ground, and polished according to the guidelines given in the ASM Handbook, Volume 9 [11]. Phenolic resin was used as the mounting material. The progression of SiC grinding paper used was 400-, 600-, 800-, then 1200-grit. Final polishing was accomplished using a 0.5 µm Al2O3 suspension and 0.05 µm Al2O3 suspension.

After polishing, each sample was sonicated in ethanol for 15 minutes. Additional micrographs were created using the DOM in bright field and dark field, with both Z20 and Z100 lenses.

Sample A was cut in half using a metallographic saw in order to access the cross-section. The cross-section was polished using the same procedure discussed above.

It was then swabbed for 10 seconds with Waterless Kalling’s Etchant (Number 94 in ASTM E 407 [12]).

Each polished sample was also examined using a JEOL JSM6510 scanning electron microscope (SEM).

Images were taken using both secondary electrons and backscatter electrons with a compositional contrast. The SEM was also used to collect surface compositional data using energy dispersive X-ray spectrometry (EDS).

Hardness Mechanical properties were assessed with a Leco hardness tester. Vickers microhardness testing was performed on Sample B with a load of 1000 g and a dwell time of 5 s. A grid of 5x4 indents was made at the edge of the face.

Results

Dot Distribution Fig. 2 shows the top view of sample A in the as-printed, after-polishing, and after-repolishing condition. There were two types of dots visible: large (20-30 µm) and small (<10 µm). The distribution of the large dots was not affected by location in the sample. The distribution of the small dots depended on the location in the sample. In the intralayer region, the dot distribution in the hatching followed the scan direction of the laser; whereas, in the interlayer region, the dots were more densely packed and randomly distributed. In addition, the dots in the contour region were consistently densely packed and randomly distributed. The dots appear in the SEM image in Fig. 3 as black spots.

Figure 2. Dot distribution.

Top view of IN718 sample, (a) in the as-printed, (b) polished, and (c) re-polished conditions all 10 mm across. With polishing, the scan direction is visible in the hatching by way of bright dots. The lines of dots disappear in the re-polished sample.

Figure 3. SEM micrographs.

The black spots are the same features that appear as bright dots in the dark field DOM image. Numbered spots correspond to data points in Table 2.

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Compositional Changes Chemical composition of the spots and regions of different contrast was identified with EDS (see Fig. 2).

Examples of spectra are given in Table 1 and compared to the expected nominal composition of IN718. As compared to the expected compositions of 0 wt.-% O and 0.5Â wt.-% Al, the spots showed an increase to between 9 and 19 wt.-% O and between 1 and 5Â wt.% Al. The bright regions showed an increased Nb content.

Hardness Distribution Hardness testing was performed on the face of the polished prism from the hatch into the contour. There are two clear regions of indents shown in Fig. 4: the first with higher hardness (234-255 HV); the second with lower (208-225 HV). The first region of the indents was in the hatching, and the second region progressed into the contour.

Discussion

Identification of Dots and Other Compositional Changes As there are numerous phases and the possibility for inclusions within the sample, the dots were not immediately identified as pores, though they appeared as such based on the reflection of light in DOM. To confirm that the dots were pores, sonication of the samples after polishing was performed for only a short amount of time so that results from EDS could be examined for trapped polishing slurry. The dots showed elevated amounts of Al and O compared to the nominal composition, though the other elements in IN718 were still detectable. This information led to the conclusion that each dot was a hole, or pore, and that the interaction area of the electrons in EDS allowed the elements within the bulk IN718 material to be seen. Within the pore was remnants of polishing slurry consisting of Al2O3, explaining the presence of anomalous levels of Al and O.

Table 1. EDS data for black spots. These contained more Al and O when compared to the nominal composition (wt.-%) of IN718. This table corresponds to Fig. 5.

Figure 4. Hardness data for IN718. The hardness data was taken from the hatching (the higher hardness portion) and the contour (the lower hardness portion).

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Figure 5. Part of the cross-section of sample A. Etching revealed melt pool interfaces.

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The increased levels of Nb indicate regions in which the nanoscale hardening phase of IN728, γʺ, is present as described by Tian et al. [7].

Pore Distribution Changes The large pores were 20-30 µm and their distribution was not affected by location in the sample. It is possible that these pores are caused by hollow powder particles with trapped gas that would remain trapped in the sample without heat treatment for densification.

Since the small pores were less than 10 µm and their distribution depended on location in the sample, it is more likely that they are a product of the process than the powder. When the sample was first polished, the face was a part of the top layer deposited. After being ground and polished again, enough material was removed to expose the interface between the top layer and the one directly beneath it. This can be determined from the view of the cross-section of sample A (Fig. 5), which was not polished down as much. In Fig. 5, the top face is clearly still within the top layer, but could feasibly be polished down enough to expose the interface.

The scanning lines that are seen in Fig. 2 were a result of the edges of melt pools, which can also be identified in Fig. 5, the cross-section. The edges of the melt pools have an increased number of pores. In the top view and in the middle of a layer, these edges and their contained pores trace out the scan direction, though they do not exactly define the laser’s path since they overlap each other. When the sample was polished to a depth which exposed an intralayer surface, the bottom of the upper melt pools met with the top of the lower melt pools, and caused a much denser and more random distribution of pores.

The difference between the distribution in the contour and the hatching can be explained in a similar way.

Since the contour was built first in every layer and then the hatching was built with some overlap, there is more re-melting and more interaction between edges of melt pools in the contour than in the hatching.

Therefore, there is expected to be a denser and more random distribution of pores in the contour throughout the height of the sample.

Pores Decrease Hardness Hardness testing revealed two distinct regions in hardness: the higher hardness hatching and the lower hardness contour. Since the sample used (sample B) had not been polished down enough to expose the interface between layers, the hatching had less pores than the contour. Since pores are empty space which is detrimental to hardness, the lower hardness can be linked to the presence of more densely-packed pores.

The hardness decreased by approximately 10% due to the increased pore density.

Conclusions

A combination of the images and EDS data led to the conclusion that the small dots are holes, or pores. EDS showed that the pores contained high amounts of Al and O, which are not abundant in IN718. Therefore, the elements must have been introduced through the final polishing step, which was performed using Al2O3, and been trapped.

The comparison between the polished and re-polished samples shows that the scan direction can no longer be seen after some amount of material is removed.

This is evidence that the distribution of pores changes throughout the layers of the sample. As they are defects, the pores affect the mechanical properties of the material. It was observed that the contour had an increased number of pores, and that the hardness of the contour was less than that of the hatching, where there were less pores. Based on these results, we will focus on the influence of printing and processing parameters on the defects in future works, which may help to eliminate these defects in repairs and other industrial applications.

Acknowledgements

This research was funded jointly by Markus Chmielus, the Swanson School of Engineering, Mascaro Center for Sustainable Innovations (MCSI), and the Office of the Provost of the University of Pittsburgh. Meredith Meyer from the University of Pittsburgh is also gratefully acknowledged for her assistance with the collection of hardness data.

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References

[1] A. Gebhardt, Understanding Additive Manufacturing – Rapid Prototyping, Rapid Tooling, Rapid Manufacturing, Hanser Publishers, 2011.

[2] W. E. Frazier, Metal Additive Manufacturing: A Review, J. Mater. Eng. Perform. 23 (2014) 1917–1928.

[3] R. Grylls, Laser Engineered Net Shapes, 45–46 (2003), 86.

[4] I. Palčič, M. Balažic, M. Milfelner, B. Buchmeister, Potential of Laser Engineered Net Shaping (LENS) Technology. Mater. Manuf. Process., 24 (2009), 750– 753.

[5] X. Zhao, J. Chen, X. Lin W. Huang, Study on microstructure and mechanical properties of laser rapid forming Inconel 718. Mater. Sci. Eng., A, 478 (2008), 119–124.

[6] M. J. Donachie, S. J. Donachie, Superalloys : A Technical Guide, second ed., ASM International, 2002.

[7] Y. Tian, D. McAllister, H. Colijn, M. Mills, D. Farson, M. Nordin, S. Babu, Rationalization of Microstructure Heterogeneity in INCONEL 718 Builds Made by the Direct Laser Additive Manufacturing Process. Metall. Mater. Trans., A, 45 (2014), 4470-4483.

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[8] V. D. Manvatkar, A. A. Gokhale, G. Jagan Reddy, A. Venkataramana, A. De, Estimation of Melt Pool Dimensions, Thermal Cycle, and Hardness Distribution in the Laser-Engineered Net Shaping Process of Austenitic Stainless Steel. Metall. Mater. Trans., A, 42 (2011), 4080–4087.

[9] W. Hofmeister, M. Griffith, (2001). Solidification in direct metal deposition by LENS processing. JOM, 53 (2001), 30-34.

[10] H. Yin, S. D. Felicelli, Dendrite growth simulation during solidification in the LENS process. Acta Mater., 58 (2008), 1455–1465.

[11] G. F. Vander Voort, S. R. Lampman, B. R. Sanders, G. J. Anton, C. Polakowski, (2004). ASM Handbook, Volume 9: Metallography and Microstructures, Fort Lauderdale: ASM International, Incorporated, 2004.

[12] ASTM E407-07e1, Standard Practice for Microetching Metals and Alloys, ASTM International, West Conshohocken, PA, 2007.

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Notes