Materials Science and Engineering
Showing 241-250 of 308 Results
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Alberto Salleo
Hong Seh and Vivian W. M. Lim Professor
Current Research and Scholarly InterestsNovel materials and processing techniques for large-area and flexible electronic/photonic devices. Polymeric materials for electronics, bioelectronics, and biosensors. Electrochemical devices for neuromorphic computing. Defects and structure/property studies of polymeric semiconductors, nano-structured and amorphous materials in thin films. Advanced characterization techniques for soft matter.
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Krishna Saraswat
Rickey/Nielsen Professor in the School of Engineering and Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsNew and innovative materials, structures, and process technology of semiconductor devices, interconnects for nanoelectronics and solar cells.
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Samya Sen, Ph.D.
Postdoctoral Scholar, Materials Science and Engineering
Current Research and Scholarly InterestsSamya's research interests are primarily soft materials and complex fluids. He uses experimental techniques of fundamental rheology in conjunction with non-Newtonian fluid mechanics to model, characterize, design, and understand soft material behavior. The applications of his research range from yield-stress fluid design in consumer products, industrial materials, and wildfire suppression. His current research projects as a postdoctoral researcher with Prof. Appel is in the rheological of novel hydrogels for biomedical applications, including improved drug delivery. His focus is on developing transient, stimuli-responsive materials with tunable mechanical and mass transport properties which can be tuned in situ and in vitro for controlled drug-release profiles. He also works on mathematical modeling of mass transport, structural evolution, and constitutive behavior of polymeric and colloidal materials in the context of soft biomaterials.
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Austin Sendek
Adjunct Professor, Materials Science and Engineering
Hourly Speaker, Stanford Center for Professional DevelopmentBioAustin Sendek is Adjunct Professor of Materials Science & Engineering at Stanford University. His research and teaching focuses broadly on harnessing the power of machine learning and A.I. to accelerate the design and discovery of new materials for decarbonizing the global economy. He serves as an advisor and collaborator on several initiatives at Stanford, spanning from fundamental materials science research to technology entrepreneurship mentoring. He is also the Founder and Chief Executive Officer of Aionics, Inc., a technology company dedicated to designing high performance batteries with A.I. and high performance compute (HPC)-based quantum mechanical simulation. He was included on the 2019 list of Forbes 30 Under 30 in Energy, and served as a Guest Lecturer in Mechanical Engineering at Columbia University in 2019 and 2020. He holds a B.S. in Applied Physics from UC Davis and a Ph.D. in Applied Physics from Stanford University.
Upcoming courses:
FALL 2023: Materials Science and Engineering 331: Computational materials science at the atomic scale. Introduction to computational materials science methods at the atomistic level, with an emphasis on quantum methods. A brief history of computational approaches is presented, with deep dives into the most impactful methods: density functional theory, tight-binding, empirical potentials, and machine learning-based property prediction. Computation of optical, electronic, phonon properties. Bulk materials, interfaces, nanostructures. Molecular dynamics. Prerequisites - undergraduate quantum mechanics. Experience writing code is preferred but not required.
Select publications:
AD Sendek, B Ransom, ED Cubuk, LA Pellouchoud, J Nanda, EJ Reed. Machine learning modeling for accelerated battery materials design in the small data regime. ACS Energy Materials 12, 2200553 (2022).
AD Sendek, Q Yang, ED Cubuk, KAN Duerloo, Y Cui, EJ Reed. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy & Environmental Science 10 (1), 306-320 (2017).
AD Sendek, ED Cubuk, ER Antoniuk, G Cheon, Y Cui, EJ Reed. Machine learning-assisted discovery of solid Li-ion conducting materials. Chemistry of Materials 31 (2), 342-352 (2018).
AD Sendek, G Cheon, M Pasta, EJ Reed. Quantifying the search for solid Li-ion electrolyte materials by anion: a data-driven perspective. The Journal of Physical Chemistry C 124 (15), 8067-8079 (2020).
AD Sendek, ER Antoniuk, ED Cubuk, B Ransom, BE Francisco, J Buettner-Garrett, Y Cui, EJ Reed. Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium–Boron–Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes. ACS Applied Materials & Interfaces 12 (34), 37957-37966 (2020).
J Xie, AD Sendek, ED Cubuk, X Zhang, Z Lu, Y Gong, T Wu, F Shi, W Liu, EJ Reed, Y Cui. Atomic Layer Deposition of Stable LiAlF4 Lithium Ion Conductive Interfacial Layer for Stable Cathode Cycling. ACS Nano 11 (7), 7019-7027 (2017).
B Ransom, N Zhao, AD Sendek, ED Cubuk, W Chueh, EJ Reed. Two low-expansion Li-ion cathode materials with promising multi-property performance. MRS Bulletin (2021).
ED Cubuk, AD Sendek, EJ Reed. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data. The Journal of Chemical Physics 150 (21), 214701 (2019). -
Viktoryia Shautsova
Postdoctoral Scholar, Materials Science and Engineering
BioViktoryia is a Stanford Science Fellow with a background in physics, nanotechnology, and material science. Viktoryia received her bachelor’s degree in computer science from Belarus State University and a PhD in physics from Imperial College London, followed by a postdoc in material science at Oxford University. Viktoryia's passion lies in building the next generation of bioelectronic devices that interface with the brain and heart. At Stanford, Viktoryia is part of GLAM and Wu Tsai Neuroscience Institute, working with Nick Melosh, Bianxiao Cui and Mark Brongersma to develop novel nanoscale devices for label-free optical sensing of bioelectrical signals produced by neural and cardiac cells and nongenetic optical stimulation of neural activity.
