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About me
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Short description of portfolio item number 1
Short description of portfolio item number 2
We developed a unique Arduino-based current monitoring method for measuring non-linear concentration waves in microfluidic channels, and modelled its behaviour with a Burger’s equation.
We developed a DFT-based machine learning potential of the Lithium Graphite system using the Behler–Parrinello Neural Network and Bayesian hyperparameter optimization. The potential can accurately predict the stacking, lattice constants and open circuit voltage over the charge cycle.
I collaborated with experimentalists at University of California Berkeley to identify an electrochemical rate enhancement region near the magic angle in twisted bilayer graphene.
We used first principle DFT to probe the possibility of oxygen redox from disorder and (anionic/cationic) doping in the excess Lithium Vanadium Oxide. We also quantified the ease of Lithium diffusion in disorder for fast charging applications.
We theoretically determined electron-transfer rates from novel flat bands in twisted trilayer graphene by incorporated tight-binding density of states in a modified Marcus Hush Chidsey rate model. Experimental collaborators at UC Berkeley validated results and reported unique dependence of rates on interlayer electron density.
We resolve spatial domains and flat band features on the twisted bilayer graphene surface by solving the steady state charge transport equation inside a 3D nanopipette volume. We find best resolution at magic angle twist, with 2-5nm nanopipette radii and -0.05 V applied voltage. We also report a 2D map of electrochemical current from scanning the nanopipette over the bilayer 2D surface.
Using tight binding density of states in twisted trilayer graphene, we found unconventional high electron transfer rates at incommensurate twist angles away from magic angles. This feature is accessible only in multilayer graphene, which retains high activity with wide range of redox couples. Temperature can switch activity between magic/incommensurate angles.
We use machine learning-assisted Density Functional Theory calculations to analyze the magnetic moment evolution in two model Li-rich Ni-rich cathode materials during charge-discharge. We find cationic redox is characterized by a monotonic increase in total magnetic moment, with almost negligle oxygen contribution. In contrast, the total magnetic moment dips during anionic redox due to pairing of 2p oxygen electrons. Magnetic moment characterization is thus a robust and course-grained redox monitoring tool for Li-rich transition metal oxide cathodes.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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