Seminars will be on Fridays from 12:30 to 1:30pm at Mudd 826 unless otherwise noted.
We're excited to announce our first CEEC workshop. Prof. Byungha Shin from KAIST will deliver a tutorial lecture on X-ray photoelectron spectroscopy (XPS). The lecture should be beneficial for both new users and more experienced users, and it is not focused on electrochemical systems specifically. Please feel free to share this notice with other students you know who might be interested in learning more about XPS.
Bio of Prof. Shin: Byungha Shin is an Associate Professor in the Department of Materials Science (MSE) and Engineering at Korea Advanced Institute of Science and Technology (KAIST) in Daejeon, Korea. Prof. Shin received B.S. in MSE from Seoul National University in 2000, M.S. in MSE from the University of Michigan in July 2002, and Ph.D. in Applied Physics from Harvard University in 2007. From May 2007 to March 2010, he was a post-doctoral researcher in the Department of MSE at Stanford University. From May 2010 until he joined KAIST in Feb 2014, he worked at IBM T. J. Watson Research Center in as s post-doctoral research and a Research Staff Member. His past research experience includes the study of thin film growth kinetics and high-k dielectric materials for microelectronic applications. His primary research interest is developing novel materials for energy applications with the current emphasis on hybrid perovskite optoelectronic devices (PV and LED), chalcogenide thin film solar cells, and photoelectrochemical water splitting.
PhD Candidate in Chemical Engineering
Advisor: Alan West
Applications of Data Science in Battery Science: Quantitative Parameter Estimation, Model Selection, and Variable Selection
Moore's Law has led to increasing computational capabilities available at extremely low costs. The cheapness of computer resources allows for the generation of vast amounts of data from physics-based numerical models. In parallel, there have been innovative methodologies developed that allow computer algorithms to perform data analysis and decision making that traditionally could only be performed by humans with technical expertise. This talk will illustrate how physics-based battery models can be coupled with algorithmic techniques to perform improved parameter estimation, model selection, variable selection, and to assist in the design of experiments.
PhD Candidate in Chemical Engineering
Advisor: Daniel Esposito
High speed imaging of bubble flows in membraneless electrochemical cells
As solar electricity continues to increase in installed capacity, significant research efforts have focused on energy storage devices to provide renewable electricity during intermittent hours. One promising energy storage technology is water electrolysis, which uses water and electricity to generate hydrogen fuel. A major challenge for electrochemical hydrogen production is the capital cost, which for state-of-the-art devices is still prohibitively high. Membraneless electrolyzers have been proposed as a low cost reactor design for water electrolysis. The design of these devices is a significant departure from the architectures of conventional membrane- electrode assembly (MEA) and alkaline electrolyzers. In order to safely and efficiently separate the product gases, many of these membraneless designs require flowing electrolyte. In this study we use high speed video and image processing to quantitatively characterize the multiphase flow in a reactor scheme using angled mesh flow-through electrodes. While it is generally recognized that the dynamics of multiphase flow can strongly impact cell performance, attempts to characterize gas evolving electrodes can be non-trivial due to the complexity of the underlying physics. Through a post-processing analysis we are able to detect the size, location, and velocity of the hydrogen bubbles generated during operation. This non-invasive technique can be used to quantify gas evolution efficiency yields, bubble size distributions, current distributions, and product gas cross-over. These techniques can be applied to monitor operating conditions as well as guide the design of future electrolysis systems.
If you would like to nominate a student, faculty, or industry professional to present at a seminar, please email email@example.com.