Texas Tech University

Seminar Series

Friday, February 17, 2023
10:00 a.m - 11:00 a.m - Electrical Engineering Bullen Room - 226

Opportunities and Support for the BME Research Community from NSF

Dr. Laurel Kuxhaus, Ph.D.
Program director of Biomechanics & Mechanobiology within the Division of Civil, Mechanical and Manufacturing Innovation at the National Science Foundation.

Abstract: The National Science Foundation (NSF) supports work in all fields of science and engineering, including biomedical engineering. That said, biomedical engineering researchers can face challenges in finding the right ‘home' and scope for their work at NSF. This presentation will provide a broad overview of the mission of NSF and how it relates to the biomedical engineering community, including emerging initiatives and responses to the current disruption of the research enterprise. Descriptions of select programs at the National Science Foundation that fund work relevant to the biomedical engineering community will be covered. Best practices in proposal preparation and practical tips to optimize interaction with your program director will also be discussed. Bring your questions along!

Biosketch: Laurel Kuxhaus, PhD, is the program director of Biomechanics & Mechanobiology within the Division of Civil, Mechanical and Manufacturing Innovation at the National Science Foundation. Concurrently, she is an Associate Professor of Mechanical & Aeronautical Engineering at Clarkson University, where she directs the Orthopaedic Biomechanics Laboratory. Her laboratory work spans the field of orthopaedic biomechanics including injury biomechanics of both hard and soft tissues and design of both orthopaedic implants and assistive technology devices. She holds B.S. (Engineering Mechanics) and B.A. (Music) degrees from Michigan State University, an M.S. (Mechanical Engineering) from Cornell University, and a Ph.D. (Bioengineering) from the University of Pittsburgh. In 2018, she was elected to Fellow status of the American Society of Mechanical Engineers (ASME) and has previously served as a member of the Executive Committee of the Bioengineering Division of ASME. More recently (2018-19), she spent a year on Capitol Hill working in science and technology policy as an ASME Congressional Fellow.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
Electrical Engineering Bullen Room - 226

Friday, February 10, 2023
2:00 p.m - 3:00 p.m - ME North 106

Physicochemical Properties of Advanced Electrolytes Using Molecular Simulations

Dr. Zhou (Joe) Yu, Ph.D.
Postdoc Research Associate in Theoretical Division at Los Alamos National Laboratory

Abstract: Li-ion battery, with essential components of electrodes, electrolytes, separators, and current collectors, is a critical technology that has revolutionized consumer electronics, is revolutionizing mobility, and is poised to revolutionize the electric grid. Compared to electrodes, electrolytes are frequently less studied and considered the bottleneck in the development of batteries. Molecular simulations, as one of the most crucial computing methods, can provide comprehensive insights into materials behavior and play an essential role in accelerating materials design. In this talk, I will first explain the emerging challenges and objectives of battery electrolytes and the applications of molecular simulations in battery electrolyte development. Then I will illustrate the computational battery electrolyte workflow through solvation-dynamics relationships study in bulk fluorinated ether electrolytes, solid electrolyte interphase formation mechanism exploration at the interface between Li metal electrodes and the newly designed fluorinated electrolytes, and reaction energy investigation on the discharging process in Li-S batteries with a new host material. This talk highlights the importance of molecular simulations in developing a fundamental understanding of advanced battery electrolytes' bulk and interfacial properties and presents an outlook for future studies.

Biosketch: Dr. Zhou (Joe) Yu has been a postdoc research associate in Theoretical Division at Los Alamos National Laboratory since November 2021. Before that, he took his first postdoc training in Materials Science Division at Argonne National Laboratory from February 2019 to November 2021. He got Ph.D. in Mechanical Engineering at Virginia Tech in December 2018. His research interests focus on understanding and predicting the physicochemical properties of advanced energy materials, especially battery electrolytes, using molecular simulations and theory-driven approaches in multiple spatial and time scales. His studies have been published in highly ranked journals, including Nature Nanotechnology, ACS Energy Letters, Energy & Environmental Materials, Journal of Materials Chemistry A, etc.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Wednesday, February 8, 2023
2:00 p.m - 3:00 p.m - ME North 106

Machine learning and mechanisms for high-entropy alloys

Dr. Zongrui Pei, Ph.D.
Senior staff at the supercomputing center of New York University

Abstract: High-entropy alloys have received extensive attention in the last decade due to their exceptional mechanical properties [1,2]. I will present our machine-learning and mechanistic studies of these novel alloys, including their mechanical and thermodynamic properties. The machine-learning examples include (i) a new order parameter based on a neural network [2]; (ii) new machine-learning informed rules beyond the traditional Hume-Rothery ones [3]; (iii) machine-learning microstructure for inverse materials design [4]; (iv) design of ultrahigh-entropy alloys via mining six million texts [5]. Mechanistic studies aim to reveal the origins of their exceptional mechanical properties. This part covers (i) the polymorphism of line defects [6], (ii) the decoupling of defects [7], and (iii) a new alloy-strengthening theory developed recently.

Biosketch: Zongrui Pei completed his Ph.D. study at Max-Planck Institut für Eisenforschung GmbH, Duesseldorf, Germany, supervised by Profs. Dierk Raabe and Jörg Neugebauer. During his Ph.D. time, he studied the mechanisms that control the mechanical properties of magnesium alloys and then used them to design ductile alloys, assisted by multi-scale methods. Then he moved to Oak Ridge National Laboratory as a postdoc for two years. There he used first-principles and machine-learning methods to study lightweight and high-entropy alloys. Afterward, he continued these topics as a research associate at National Energy Technology Laboratory. He is now a senior staff at the supercomputing center of New York University, focusing on various computational materials topics, particularly those related to first-principles calculations and machine learning.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Monday, February 6, 2023
2:00 p.m - 3:00 p.m - ME North 106

Discovery of New Materials, Structures, and Devices for Renewable Energies

Dr. Amin Nozariasbmarz P.hD.
Assistant Research Professor at the Department of Materials Science and Engineering at the Pennsylvania State University.

Abstract: Ever-growing energy demands, costs, and environmental concerns compel us to search for more sustainable energy resources and discover new materials and methodologies for clean energy generation. Annually, ~70% of global energy consumption wastes as heat. Recovering a fraction of the waste heat and converting it into electricity provides a transformative impact on the overall energy recovery scenario and suppression of global warming by reducing fossil fuel consumption. Therefore, thermal energy harvesting and management techniques are currently among the leading research topics. Materials that directly convert heat to electricity mainly work based on thermoelectric, thermomagnetic, and shape-memory effects. There is a broad application for these materials from body heat harvesting for self-powered wearable electronics to waste heat recovery in industrial processes.
This talk includes materials discovery and manufacturing, device design, and applications for thermal energy harvesting. Electromagnetic wave interaction with materials is introduced as a new technique for the discovery and design of far-from-equilibrium materials that offer novel materials, structures, manufacturing, and transport properties for thermal energy harvesting and thermal management applications. The interaction of the electromagnetic wave with materials classifies into thermal and non-thermal effects. The thermal effect includes characteristics of dielectric heating, such as overheating, hot spots, and selective heating. The non-thermal effect is more profound and includes different material responses such as electromagnetic field-induced alloy decomposition, de-crystallization, enhanced solid-state reactions, and defect generation. This method is especially interesting as it is extendable to various materials and can produce bulk and thin film materials with desired properties for renewable energy technologies. I will explain the materials and device fabrication, testing, and application of thermoelectric materials to convert heat to electricity.

Biosketch: Dr. Amin Nozariasbmarz is currently an Assistant Research Professor at the Department of Materials Science and Engineering at the Pennsylvania State University. He received his B.Sc. and M.Sc. in Materials Science and Engineering from Sharif University of Technology and University of Tehran, respectively. He completed his Ph.D. in Electrical Engineering (Nanoelectronics) with a minor in Materials Science at North Carolina State University in 2017. He continued his research as a postdoctoral scholar at the Department of Materials Science and Engineering at the Pennsylvania State University. He has published 47 peer-reviewed journal articles, two book chapters, and one US patent. His interdisciplinary research interests exist at the intersection of energy and materials including novel materials for renewable and sustainable energies, thermal management, thermoelectric materials and systems for cryogenic to ultra-high temperature applications, high entropy materials, and materials discovery. He has unique expertise in materials engineering, design, synthesis, processing, nano-structuring, manufacturing, and analysis. He has contributed to the development of several key technologies such as electromagnetic field interaction with materials, self-powered wearable electronics, waste heat recovery, nanostructured thermoelectric materials and high-efficiency devices, and solid-state processing of halide perovskites.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Friday, January 27, 2023
2:00 p.m - 3:00 p.m - ME North 106

Advancing Surgery and Intervention with Autonomous Miniature Robots and Intelligent Personalized Planning

Dr. Xiaolong Liu, Ph.D.
Assistant Research Scientist in the Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics at the Johns Hopkins University.

Abstract: Research in medical robotics and computer-assisted surgery has made remarkable progress in the last twenty years for many surgical procedures. Novel strategies and technological developments are required to take full advantage of the precision, maneuverability, intelligence, and automaticity that medical systems can provide. This talk focuses on my research efforts to leverage magnetic manipulation and design optimization techniques for designing, actuating, and controlling miniature robots that can perform surgical tasks in confined spaces inside human body varying from abdominal cavities to heart chambers, without making significant incisions to the anatomies. In addition to enabling functional autonomous miniature robots, pre-operative surgical planning is an important component for intelligent surgery and intervention. Predicting surgical outcomes and identifying optimal surgical implantation are non-trivial tasks due to the variety of patient-specific anatomies, the growth of pediatric patients, and uncertainty of modeling and computation. In this talk, I will discuss the use of machine intelligence in designing patient-specific cardiovascular implants and computing optimal surgical options under uncertainty for congenital heart disease treatment. I will end my talk by discussing my future work further advancing the field of medical robotics and computer-assisted surgery through developing programmable soft robotic mechanisms and intelligent personalized surgical planning techniques.

Biosketch: Dr. Xiaolong Liu is an Assistant Research Scientist in the Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics at the Johns Hopkins University. Prior to that, he was a Postdoctoral Researcher in the Department of Mechanical Engineering at the University of Maryland College Park. He obtained his Ph.D. degree from the University of Tennessee Knoxville in the field of medical robotics. He was a Principal Research Engineer and led a team at AUBO Robotics, Inc. for developing magnetically actuated medical robotic systems. His research focuses on medical robotics and computer-assisted surgery. He received the Maryland Innovation Initiative Award in 2021 for his cardiovascular surgical planning system that can help cardiac surgeons to make high-quality patient- specific
implants.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Wednesday, January 25, 2023
2:00 p.m - 3:00 p.m - ME North 106

Engineering Tunable Biomimetic Nanosensors for Rapid Disease Diagnostics and Image-Guided Interventions

Dr. Indrajit Srivastava, Ph.D.
Postdoctoral Research Associate at the Departments of Bioengineering and Electrical & Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC).

Abstract: With the emergence of nanotechnology, the field of biomedicine has been ushered into a new dawn for early diagnosis and treatment regimens for a multitude of human diseases. However, the design and development of functional nanoconstructs are often hindered by biological barriers and limitations of material properties that can negatively impact their efficacy when introduced into a physiologically relevant system. In this seminar, I will discuss how combining nanoengineering design principles and natural materials (cell membrane fragments) for the development of biomimetic nanosensors that retains many surface features of the natural material and be used for the development of image-guided surgical and therapeutic interventions for cancer, cardiovascular diseases (CVD), and bacterial pathogenesis. First, I will discuss how designing biomimetic nanoparticles mimicking the chemical attributes of red-blood-cell can be used to accurately delineate tumors from healthy regions and simultaneously provide a qualitative indicator of metastases cancer progression, thereby assisting fluorescence-guided cancer surgeries. Second, I will discuss the designing of a new class of biomimetic surface-enhanced Raman scattering (SERS)-plasmonic nanosensors having improved dispersibility characteristics, enhanced signal brightness, and used for spectroscopy-guided tumor cell identification and multi-modal cancer surgery. I will delve further into how biomimetic SERS nanosensors can be tuned for rapid and accurate detection of blood-clot-related proteins for CVD. Finally, I will conclude the talk by briefly talking about my plans in creating methodologies that develops nanoparticles with tunable physiochemical and biomimetic characteristics and easily impartible activable molecular imaging signals. Such biomimetic nanoparticles combined with data-driven approaches can lead us to breakthroughs in understanding fundamental questions (organ-specific targeting, endosomal escape, toxin neutralization) and aid in developing next-generation of nanotherapeutics, nanovaccines, and infectious disease detections.

Biosketch: Dr. Indrajit Srivastava is currently a Postdoctoral Research Associate with Prof. Shuming Nie and Prof. Viktor Gruev at the Departments of Bioengineering and Electrical & Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). His postdoctoral work focuses on designing biomimetic nanoparticles for fluorescence and spectroscopy-guided cancer surgeries. As a Ph.D. student with Prof. Dipanjan Pan at UIUC, his thesis focused on developing intrinsically fluorescent nanoparticles called carbon dots and expanding their applications as therapeutics, in vivo bioimaging of diseases, and array-based biosensing of analytes. Previously, he received his B.E. in Metallurgical Engineering & Materials Science from the Indian Institute of Engineering Science & Technology, Shibpur, India, supported by Prof. A. K. Seal Memorial Undergraduate Academic Fellowship. His work has been recognized with several awards and honors, like multiple Baxter Young Investigator Awards, American Chemical Society PMSE Future Faculty Scholar, Alexander von Humboldt Research Fellowship, Carbon Journal Dissertation Award, and BMES Career Development Award. Through his mentoring and outreach activities, he has shown his commitment in enhancing diversity, equity, and inclusion in STEM.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Monday, January 23, 2023
2:00 p.m - 3:00 p.m - ME North 106

Biological signals-inspired neuromuscular system modeling and advanced adaptive optimal control for wearable robotic assistive devices

Dr. Qiang Zhang, Ph.D.
Advanced Rehabilitation Research and Training (ARRT) Post-Doctoral research fellow in the Closed Loop Engineering for Advanced Rehabilitation at University of North Carolina Chapel Hill.

Abstract: Traditional rehabilitation training mainly depended on physical therapy, and it is time-consuming, high cost, and limited by the availability of professional physical therapists. More advanced neurorehabilitation techniques have recently been applied to improve diminished joint functions due to neurological disorders, including powered exoskeletons, soft exosuits, and functional electrical stimulation (FES). Intuitively, robotic devices-based rehabilitation often uses knowledge of a person's residual volitional ability to determine compensation torque or force such as during the assist-as-need (AAN) therapy. However, the prediction of human residual volitional efforts with high accuracy and the control design with the consideration of volitional efforts are challenging. Here, I will describe our recent work in which 1) we take sensor fusion between skeletal muscle's surface electromyography (sEMG) and ultrasound (US) imaging signals to improve the human residual volitional efforts prediction accuracy; 2) we design an assist-as-needed control framework for a powered ankle exoskeleton with the sensor fusion-based volitional efforts prediction; 3) we design a closed-loop control approach with the real-time US imaging signals feedback to address drop foot syndrome. In the second half of my talk, I will shift gears to describe ongoing work on the robotic assistance personalization control based on the reinforcement learning approach. Powered exoskeletons are promising devices to improve the walking patterns of people with neurological impairments. Providing personalized external assistance for human locomotion though is challenging due to uncertainties and the time-varying nature of human-robot interaction. Here, we have formulated an adaptive optimal control solution based on reinforcement learning for a wearable bilateral hip exoskeleton to automatically tailor control parameters for each wearer, such as assistance torque shape profile parameters or impedance parameters, to minimize the defined cost function of the human-robot symbiotic system. In addition, we have analyzed the personalized assistance on the hip joint kinematics, kinetics, gait patterns, and muscle activation levels of the symbiotic system.

Biosketch: Qiang Zhang received the B.Sc. degree in Mechanical Engineering and the M.Sc. degree in Mechatronics Engineering from Wuhan University (WHU), Wuhan, China, in 2014 and 2017, respectively, the M.Sc. degree in Mechanical Engineering from the University of Pittsburgh, Pittsburgh, PA, USA, in 2019, and the Ph.D. degree in Biomedical Engineering from the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, and North Carolina State University, Raleigh, NC, USA, in 2021.
Currently, he is an Advanced Rehabilitation Research and Training (ARRT) Post-Doctoral research fellow in the Closed Loop Engineering for Advanced Rehabilitation at the UNC/NCSU Joint Department of Biomedical Engineering in the University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, and North Carolina State University, Raleigh, NC, USA. His current research interests include biological signal-based neuromusculoskeletal modeling, human motion intent detection, Lyapunov-based nonlinear control/adaptive control, machine learning-based control, surface electromyography/ultrasound imaging processing, and their applications to wearable robotic devices' control and next-generation healthcare. Dr. Zhang was a recipient of a number of awards, including the finalist for the Best Student Paper Award of the 2019 IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), the 2021 UNC/NCSU BME Department Ph.D. Student Research Award, and 2022 ASME DSCD Rising Star Award.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Friday, December 9, 2022
11:00 a.m. - 12:00 p.m.  -  ME North 106

Advanced Intelligent Materials and Structures

Dr. Anahita Emami, Ph. D.
Assistant Professor of Mechanical Engineering at Texas State University

Abstract: Recent advancements in computational science and Artificial Intelligence (AI) techniques, specifically machine learning and deep learning have been emerging as powerful tools in engineering and opened up exciting areas of research and discovery in design, mechanics of materials, and advanced manufacturing. In this talk, the application of AI and computational modeling in the design of an intelligent sensor will be presented. A data-driven model for pressure distribution measurements of a nanocomposite polymer sensor with the potential for electronic skin (e-skin) will be discussed. The proposed sensor has an irregular arrangement of electrodes to provide unique sets of readings for different pressure distributions and allows us to develop Artificial Neural Network (ANN) models to predict the pressure distributions. We used sensory data obtained from the three-dimensional coupled electromechanical finite element simulation to develop ANN models. Then, we increased the resolution of pressure distributions and added more hidden layers in ANN to build a Deep Neural Network (DNN) model for accurate and robust prediction of the position and magnitude of the pressure with input uncertainty and random noises which will be further discussed. Next, other ongoing research projects in Advanced Intelligent Materials and Structures lab will be introduced and briefly discussed. Finally, future research directions will be presented.

Biosketch: Dr. Anahita Emami is an Assistant Professor of Mechanical Engineering at Texas State University and the Principal Investigator of the Advanced Intelligent Materials and Structures (AIMS) lab. Her research interests lie at the intersection of Mechanics of Materials and Structures, Advanced Manufacturing, and the Application of Artificial Intelligence in Design and Advanced Manufacturing Process Optimization. Before joining Texas State University, she worked as a postdoc at the University of Texas at Austin. She received her Ph.D. in Engineering Mechanics from Virginia Tech in 2018 and holds master's and bachelor's degrees in Mechanical Engineering from Purdue School of engineering and technology at IUPUI and University of Tehran, respectively.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Wednesday, December 7, 2022
11:00 a.m. - 12:00 p.m.  -  ME North 106

Biomechanical Modeling and Machine Learning for Personalized Computational Assessment of Aortic Diseases

Dr. Minliang Liu, Ph. D.
Postdoctoral Fellow in the Wallace H. Coulter Department of
Biomedical Engineering, Georgia Institue of Technology and Emory University

Abstract: As the largest artery in the human body, the aorta carries blood away from the heart to the rest of the body. This major blood vessel can be vulnerable to a number of life‐threatening diseases, including aortic aneurysm, aortic dissection, and aortic valve stenosis. With the advancement of clinical imaging modalities and numerical methods, patient‐specific biomechanical evaluation of in vivo aortic disease conditions is getting closer to reality. However, despite promising results from numerous engineering studies, current patient‐specific computational models have limited clinical translatability, which can be attributed to several major bottlenecks of current technologies, for instance: (i) accurate identification of the unknown in vivo patient‐specific hyperelastic properties, which are nonlinear and anisotropic, remains to be one of the critical challenges in the field of cardiovascular biomechanics; and (ii) classical patient‐specific models, which consist of multiple computationally expensive steps, can be impractical for time‐sensitive clinical applications that require prompt feedback to clinicians. To resolve these challenges, I have been developing inverse methods for material parameter identification, probabilistic failure index for aortic tissues, data‐driven machine learning surrogate models for accelerating computations, and physics‐informed machine learning approaches for soft tissue constitutive modeling. The research presented in this talk bridges the gap between biomechanics and machine learning and may pave the way for fast and accurate personalized computational toolkits to aid in the clinical prognosis and treatment of cardiovascular diseases.


Biosketch: Dr. Minliang Liu is currently a Postdoctoral Fellow in the Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University. He received his Ph.D. in Mechanical Engineering from Georgia Institute of Technology in 2020, and B.Eng. degree in Mechatronic Engineering from Zhejiang University in 2015. His research interests lie primarily at the intersection of cardiovascular biomechanics, computational modeling, and machine learning. He is a recipient of the American Heart Association Predoctoral Fellowship from 2019 to 2020. In 2021, He received the Georgia Tech Sigma Xi Best Ph.D. Thesis award.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

Thursday, December 1, 2022
2:00 p.m. - 3:00 p.m.  -  ME North 106

Understanding battery safety issues from a multiphysics perspective

Dr. Jun Xu
Associate professor at the University of North Carolina at Charlotte
Email: jun.xu@uncc.edu; Website: www.xugroup2014.com

Abstract: Lithium-ion batteries are one of the critical momentums of our current mobile society. With the further development and application of increasingly high energy density batteries and large capacity battery packs in electric vehicles, cellphones, laptops, and large-scale energy storage systems, the consequences of the battery safety issues now become significant threats. Internal short circuits (ISCs) and thermal runaways (TRs) are typical battery safety issues where electrochemistry, thermal, and mechanics are strongly coupled. Interdisciplinary endeavors are in pressing need to address these safety issues. In this talk, multiphysics modeling and characterization at both cell level (~102 mm) and active particle level (~1 μm) will be highlighted to provide a mechanistic understanding of the nature of triggering and evolution of ISCs as well as the responsible mechanical instabilities of the solid-solid interfaces. In the meantime, a machine-learning combined physics-based modeling will be introduced to achieve faster computation with higher accuracy. Results provide new insights for multiphysics behaviors in battery safety issues and offer engineering-ready modeling methodologies for the next-generation battery design, evaluation, and monitoring.

Bio: Dr. Jun Xu is an Associate Professor in the Department of Mechanical Engineering & Engineering Science at the University of North Carolina at Charlotte. He is the inaugural Director of NC Battery Complexity, Autonomous Vehicle and Electrification Research Center. Dr. Xu's research mainly focuses on battery safety and impact dynamics. Dr. Xu now serves as an executive committee member of the Advanced Energy System Division, ASME. He serves as Associate Editor of ASME Journal of Electrochemical Energy Conversion and Storage, Scientific Reports and Batteries. Dr. Xu has published more than 120 peer-reviewed journal papers with citations of 4600+, H-index 39. Dr. Xu was included in World's Top 2% Scientist List (Stanford University, 2022) and awarded the prestigious James H. Woodward Faculty Research Award (2021) and Early-Career Faculty Awards for Excellence in Research (2022) at UNC Charlotte. Dr. Xu earned his Ph.D. degree from Columbia University in 2014. 

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

 

Tuesday November 29, 2022
11:00 a.m. - 12:00 p.m.  -  ME North 106

Multiscale Materials Modeling and Machine Learning for Grain Boundaries (GBs)

Chongze Hu, Ph. D.
Postdoctoral Appointee at the Center of Integrated Nanotechnologies,
Sandia National Laboratories, Albuquerque, NM


Abstract: In polycrystalline materials, the grain boundary (GB) is one of the most important crystallineimperfections that controls materials processing, microstructural evolution, and various materials properties.The five crystallography degrees of freedom (DOFs) make GB structure highly complicated, which limits our fundamental understandings of the GB stability and behaviors. This presentation is an overview of Dr. Hu's research portfolio and outlines novel computational frameworks for predicting GB properties. It highlights the significance of (i) utilizing data-driven computational techniques to address the grand challenge of five DOFs for GB [1-4], (ii) revealing the fundamental mechanism of GB transitions in various materials [5-7], and (iii) understanding the thermodynamics and local chemistry of far-from equilibrium GBs, such as disconnections (i.e., step + dislocation), in nanocrystalline alloys [8].

Bio: Chongze Hu is currently a postdoctoral appointee in the Center of Integrated Nanotechnologies at Sandia National Laboratories (SNL) and an adjunct faculty of the Department of Mechanical Engineering at the University of New Mexico. His primary research area of expertise is computational materials and data science. He has engaged in numerous research projects funded by DOE BES, Sandia LDRD, VBFF, and others. Before joining SNL, he received his Ph.D. degree in Materials Science and Engineering from UC San Diego in 2020. His works have been published in journals including the Nature Communications, Science Advances, Materials Today, Materials Horizons, npj Computational Materials, and others. He currently serves as one of the young editorial broad members of Rare Metals.


Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME North 106

 

Monday October 31, 2022
2:00 p.m. - 3:00 p.m.  -  ME South 205

Design Approaches for 3D printing in Medicine

Paul Egan, Assistant Professor

 Abstract: This talk will describe how emerging 3D printing approaches are enabling the design of new structures with beneficial applications in medicine.  In medical applications, 3D printing enables the rapid design and fabrication of customizable systems for individual health needs.  For instance, multi-material structures can provide tuned energy absorption for prosthetics, architected structures can mimic bone to promote tissue growth, and soft food printing can deliver appealing foods with nutrient-rich compositions.  In each of these applications, adoption of design approaches such as parametric design, heuristic optimization, and human-in-the-loop design will be discussed.  These approaches enable 3D printed systems to achieve higher performance through clever decision-making regarding their configuration of materials, processes, and structure. For prosthetics, combinations of ABS and TPU materials enable recoverable compression at low loads and increased energy absorption for higher loading scenarios.  For bone tissue scaffolds, lattices are tunable with anisotropic properties that align with physiological loading directions while providing a hierarchical structure to promote tissue growth.  In food printing, careful mixing of healthy additives enables the printing of intricate shapes with desirable textures.  Overall, advances in these applications demonstrate the merits in using engineering design approaches to fully leverage the potential of 3D printing for medical applications.

Bio: Dr. Paul Egan is a tenure-track assistant professor in the Department of Mechanical Engineering at Texas Tech University and leads the Medicine, Mechanics, and Manufacturing (M3D) Design Lab.  The M3D Lab investigates a unique integration of engineering design methods for system modeling, optimization, and experimentation of diverse biomedical systems.  Research applications currently focus on 3D printing approaches for tissue scaffolds, personalized nutrition, prosthetics, and surgical training.  Dr. Egan has demonstrated leadership bridging entrepreneurship among engineering and health fields through advising Sling Health teams and participating in the NSF I-Corps program, in addition to building an engineering design curriculum focused on applying mechanical design to achieve medical design innovations.    Prior to joining Texas Tech, Dr. Egan completed a Mechanical Engineering Doctorate from Carnegie Mellon University as an NDSEG fellow and worked as a postdoctoral research fellow at ETH Zurich.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME South 205

Monday October 17, 2022
2:00 p.m. - 3:00 p.m.  -  ME South 205

Pushing the Boundaries of Aerodynamic Design

Victor Maldonado, Associate Professor

 Abstract: In this talk, the idea of aerodynamic design is explored through various aeronautical examples. Aerodynamic design can be defined as the engineering field, and ultimately the art, that leverages aerodynamics and uses them to “tailor” the flows to produce desirable effects, usually modifying the forces acting on bodies for some specific conditions. In the research examples presented, the goal is to maximize the aerodynamic efficiency of a single aircraft, or accomplish a tradeoff in aerodynamic performance between multiple aircraft variants or components, such that other attractive benefits like cross-domain (underwater and air) operation or increased range/endurance can be achieved. Flow control technology, both passive and active, will be discussed and how it has been applied on fixedwings and rotor blades to achieve drag reduction and improve the lift-to-drag ratio (L/D), figure of merit, as well as to control rotor blade vibration. Finally, a new design theory is presented for aircraft that must operate in extremely turbulent conditions (~10% intensity) such as flight in hurricanes. Based on recent results, the aircraft structure undergoes extreme load fluctuations and deflections, which will likely cause mid-air structural failure or become very difficult to maintain flight control. A new design paradigm is suggested where aircraft are outfitted with more control surfaces throughout the aircraft (instead of the standard three control surfaces on all aircraft) in order to respond to the effects of extreme turbulence.

Bio: Dr. Victor Maldonado is an Associate Professor in the Department of Mechanical Engineering at Texas Tech University, where he directs the Flow Control and Aerodynamics Lab. He received his Ph.D. degree in Aeronautical Engineering from Rensselaer Polytechnic Institute in 2012, in the field of applied fluid mechanics and aerodynamics. Dr. Maldonado was a postdoctoral scholar at Texas Tech University in 2012 before joining the Department of Mechanical Engineering at the University of Texas at San Antonio from 2013 to 2018. His research interests encompass applications in the area of Aerospace and Energy. In 2018 he was awarded the NSF CAREER Award for his work on blade tip vortex breakdown and flow control to improve power efficiency mitigate structural vibration. He was a faculty Summer Research Fellow at the NASA Glenn Research Center (2016) and the Naval Undersea Warfare Center (2022). Currently his is pursuing research on aerodynamics and structural mechanics of reconfigurable unmanned aerial vehicles (UAVs), and improving the aerodynamic and aeroacoustic performance of electric vertical takeoff and landing aircraft using a novel distributed ducted fan-wing ‘FanFoil' concept.  

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME South 205

Monday September 12, 2022
2:00 p.m. - 3:00 p.m.  -  ME South 205

How do we characterize complex materials and interfaces for better product design?

Gordon Christopher, Ph.D.

 Abstract: The study of complex materials and characterization of their mechanical properties has a long history, with the modern founding of the term “rheology,” meaning the study of flow and deformation, dating to 1929. At its core, rheology looks to understand what internal mechanisms dictate a materials response to deformation. Such understanding has applications in biomaterials, medicine, advanced manufacturing, oil recovery, commercial product formulations, and a host of other applications. In the Christopher lab, we focus on developing novel rheological, microscopy, microfluidic, and simulation methods to better understand the mechanisms that control viscoelasticity of high interface systems, biofilms, and colloidal suspensions. We apply what we learn to improve the utility and better control these materials for commercial product development, medical treatment, and advanced manufacturing. Today, I will give a general overview of several projects my lab has tackled in my time at Texas Tech:

  • It has long been known that liquid interfaces with adhered particles have distinct interfacial viscoelasticity that impact the bulk stability and flow of particle stabilized Pickering emulsions. However, it has been difficult to characterize these interfaces; in my lab we have developed various techniques (Bessel Beam microscopy, interfacial visualization rheology, and Stokesian dynamics simulations) that have enabled us to understand how particles adsorb to an interface, interparticle forces affect interfacial microstructure, and microstructure impacts interfacial viscoelasticity. Our results provide means to engineer the properties of emulsions in a controlled and predictable way.
  • Biofilm, communities of bacteria embedded in a self-secreted hydrogel, impact disease and industrial applications. The viscoelasticity of these systems can impact their efficacy in their intended function and/or ease of removal. Using both interfacial and microrheology, we have characterize the development of P. Aeruginosa biofilms in a range of conditions to understand the relationships between interface, environment, and biology on the mechanical properties of these systems. We show here how it is possible to use rheology to understand biological functionality and impact viscoelasticity of these materials.
  • 3D printing of colloidal suspensions has potential applications in ceramics manufacture, processing of energetic materials, and big area additive manufacturing. However, development of inks for these applications is severely limited by a lack of understanding in how changes to ink composition will affect printability and end use properties. Studying model systems, we have shown that there are in fact robust means to use basic rheological tests to predict ink's printability. Furthermore, we have shown how printing parameters can be used to modify end use properties of inks without changing composition.

Bio: Dr. Christopher is an Associate professor in the Department of Mechanical Engineering at Texas Tech University, where he has worked since 2011. He received a BS in Mechanical Engineering (2002) and a BA in Film (2003) from Columbia University. He attended Carnegie Mellon and graduated with a PhD in Mechanical engineering and a MS in Chemical Engineering in 2008. Afterwards, he spent 2 years in the Polymers Division of the National Institute of Standards and Technology as a NRC Postdoc. His research focuses on the development on study of complex fluids and interfaces rheology and flows through the development of novel techniques, including microfluidics, interfacial rheology, and bulk rheology. Since beginning work at Texas Tech, he has been named a Whitacre Research Fellow and won the TA Distinguished Young Rheologist award.

Department of Mechanical Engineering, Edward E. Whitacre Jr. College of Engineering
ME South 205

 

 

Department of Mechanical Engineering