MSE 520: Seminar Series

The scientific community has been striving for decades to generate biomimetic materials to access many of the beneficial properties seen in Nature. However, there has been limited success in obtaining structural control, catalytic activity, molecular transport, and modulated responsiveness to small perturbation. It remains challenging to decipher critical design rules to realize protein-like behavior in synthetic polymers. I will present our efforts to narrow this gap by developing protein-like random heteropolymers. Specifically, I will discuss three areas including insights gained in protein-polymer interactions using model peptide-polymer conjugates, protein stabilization in non-native environments and how to harvest statistically controlled randomness to design polymers as synthetic membrane proteins. These fundamental studies led to a rich library of functional materials for bioremediation, water treatment, disposable electronic, rapid ion transport and robust catalysis with many waiting to be explored.

Prof. Ting Xu received her Ph.D from the Department of Polymer Science and Engineering from the University of Massachusetts, Amherst in 2004. She did her postdoctoral training jointly between the University of Pennsylvania and the Cold Neutron for Biology and Technology (CNBT) team at National Institute of Science and Technology from 2004-2006. She jointed University of California, Berkeley in both the Department of Material Sciences and Engineering and Department of Chemistry in January 2007. She was promoted to Associated professor with tenure in July 2012 and Professor in 2017.

Her research interests are to design and fabricate functional materials by controlling self-assemblies in multi-component systems. She was named as one of “Brilliant 10” by Popular Science Magazine in 2009. She is the recipient of several awards including 2007 DuPont Science and Technology Grant; 2008 3M Nontenured Faculty Award; 2008 DuPont Young Professor Award; 2009 Office of Naval Research Young Investigator Award; 2010 Li Ka Shing Woman Research Award; 2010 NASA Patent Award; 2011 Camille-Dreyfus Scholar-Teacher Award; 2011 ACS Arthur K. Doolittle Award, 2018 Bakar Fellow and 2018 S. T. Li Prize.

Metallic nano-composites with constituent immiscible elements such as Cu-Nb, Cu-Mo are synthesized using “bottom-up” nano-layering or self-organization during magnetron sputtering and used as model systems to explore the interaction of interphase boundaries with defects introduced via plastic deformation or ion irradiation. The results of these experimental studies are integrated with atomistic modeling and dislocation theory to provide insight into the unprecedented combination of properties achieved in certain nanolayered composites such as ultra-high flow strengths, high plastic flow stability, high fatigue strength, high thermal stability, high sink strength for radiation-induced point defects and trapping of helium in the form of stable clusters at interfaces. A quantification of the defect-interface interactions as well as the processing-interface structure relationship allows the development of materials design concepts with controlled interface structures in nanocomposites to achieve tailored response in engineering applications.

Amit Misra is Professor and Chair of the Department of Materials Science and Engineering (MSE) at the University of Michigan, Ann Arbor since 2014. Prior to that he worked at Los Alamos National Laboratory, New Mexico (LANL) from 1996 to 2014. At LANL, his most recent appointment was as the Director of a US Department of Energy, Office of Basic Energy Sciences (DOE/BES) funded Energy Frontier Research Center (EFRC) titled Center for Materials at Irradiation and Mechanical Extremes. Professor Misra has a PhD in Materials Science and Engineering from University of Michigan (1994) and BS in Metallurgical Engineering (1989) from IIT-BHU, India. He is a naturalized citizen of USA. His primary research expertise is in processing-structure-property relations in advanced structural metallic materials for tailored response in extreme environments for next-generation of automotive, aerospace, defense and nuclear energy technologies. He has mentored over 40 early career scientists and engineers (postdocs and graduate students). He has co-authored over 300 peer-reviewed publications.

The negligible spin-orbit coupling in many organic molecules creates opportunities to alter the energy of excited electrons by manipulating their spin. In particular, molecules with a large exchange splitting have garnered interest due to their potential to undergo singlet fission (SF), a process where a molecule in a high-energy spin-singlet state shares its energy with a neighbor, placing both in a low-energy spin-triplet state. When incorporated into photovoltaic and photocatalytic systems, SF can offset losses from carrier thermalization, which account for ~50% of the energy dissipated by these technologies. Likewise, compounds that undergo SF’s inverse, triplet fusion (TF), can be paired with infrared absorbers to create structures that upconvert infrared into visible light. In this presentation, I will review our group’s efforts to create organic:inorganic structures that use SF and TF for improved light harvesting and photon upconversion.

Sean T. Roberts received his BS in Chemistry from the University of California Los Angeles in 2003 and his PhD in Physical Chemistry from the Massachusetts Institute in Technology in 2010 for work using multidimensional infrared spectroscopy to study proton transport in liquid water with Andrei Tokmakoff. Starting in 2010, Sean worked as an NSF supported ACC-F postdoctoral fellow at the University of Southern California where he worked as a member of the Center for Energy Nanosciece under the guidance of Stephen Bradforth and Alexander Benderskii. In 2014, Sean started his independent career at the University of Texas at Austin where he leads a research group that uses and develops ultrafast spectroscopic techniques to understand how the mesoscopic ordering of semiconductor nanomaterials impacts their ability to manipulate energy and transport charge. Sean is a recipient of the NSF CAREER award, was named a Cottrell Scholar in 2018, and has won funding from the Air Force Office of Scientific Research, American Chemical Society Petroleum Research Fund, and Robert T. Welch Foundation. Sean has also won numerous teaching awards and currently leads an ACS and NSF-funded education and research program, GReen Energy At Texas (GREAT), that works with community colleges to increase student retention and degree attainment in the physical sciences.

Wearable sensors have received a major recent attention owing to their considerable promise for monitoring the wearer’s health and wellness. The medical interest for wearable systems arises from the need for monitoring patients over long periods of time. These devices have the potential to continuously collect vital health information from a person’s body and provide this information to them or their healthcare provider in a timely fashion. Such sensing platforms provide new avenues to continuously and non-invasively monitor individuals and can thus tender crucial real-time information regarding a wearer’s health.This presentation will discuss recent developments in the field of wearable electrochemical sensors integrated directly on the epidermis or within the mouth for various non-invasive biomedical monitoring applications. Particular attention will be given to non-invasive monitoring of metabolites and electrolytes using flexible amperometric and potentiometric sensors, respectively, along with related materials, energy and integration considerations. The preparation and characterization of such wearable electrochemical sensors will be described, along with their current status and future prospects and challenges.

Joseph Wang is Distinguished Professor, SAIC Endowed Chair and Chair in the department of Nanoengineering at the University of California, San Diego (UCSD). He is also the Director of the UCSD Center of Wearable Sensors. He served as the director of the Center for Bioelectronics and Biosensors at Arizona State University (ASU) before joining UCSD. Professor Wang has published more than 1,060 papers and 11 books, and he holds 25 patents (H Index=158, >105,000 citations). He received two American Chemical Society National Awards in 1999 (Instrumentation) and 2006 (Electrochemistry), the ECS Sensor Achievement Award (2018) and five Honorary Professors from Spain, Argentina, Czech Republic, Romania, China and Slovenia. Professor Wang has been the founding editor of Electroanalysis (Wiley). His scientific interests are concentrated in the areas of bioelectronics, biosensors, bionanotechnology, nanomachines and electroanalytical chemistry.

This seminar will share recent advances from our research group at the intersection of molecular simulations and data science. The confluence of cheap storage, fast computers, and increases in spatial/temporal resolution of methods spanning physics-based simulations to advanced microscopy is creating challenges and opportunities for chemical engineers across many sub-disciplines. The ability to harness advanced techniques to store, visualize and process large data sets will provide a strategic research advantage and the capacity to maximize information and knowledge from our research data. Through two examples, I will highlight how the methods of data science are making an impact on my sub-disciplines of molecular simulation and reaction engineering.

After a brief discussion of context and relevant methods of data science, I will spend most of my seminar discussing a project from my group in the area of automated computational molecular design. Over the past decade, there have been many successful applications of data science towards building predictive tools for the design of small molecules or inorganic crystalline materials, primarily for energy applications such as solar energy or battery materials. We have extended this line of research into molecular design of liquids. I will explain the workflow of a molecular design algorithm we have built, as well as share examples of property design for the example of heat transfer fluids. Our workflow involves the use of a genetic algorithm for predicting new molecules, a self-learning neural network for property prediction, and molecular dynamics simulations for screening of candidate molecules. With the remaining time, I will discuss a method our group has developed for discovering complex chemical reaction networks, an application toward the degradation of common battery electrolytes, and the use of statistical models to enhance the simulations.

Jim Pfaendtner is Department Chair, Bindra Career Development Professor and Associate Professor of Chemical Engineering at the University of Washington. Additional appointments include the University of Washington Associate Vice Provost for Research Computing, Senior Scientist at the Pacific Northwest National Lab and a Senior Data Science Fellow at the UW eScience Institute. He holds a B.S. in Chemical Engineering (Georgia Tech, 2001) and a PhD in Chemical Engineering (Northwestern University, 2007). Jim’s research focus is computational molecular science and his recent teaching interests are in the area of teaching data science skills to graduate students in chemical engineering in his role as director of an NSF graduate training program (NRT) at the intersection of data science and clean energy. Jim is also passionate about health and fitness, holds a CrossFit Level 1 trainer certificate, and lately has been honing his skills at the PlayStation 4 game, “Assassins Creed: Odyssey.”