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Elucidating electrochemical mechanisms with accelerated characterization and relaxation structure analysis
Huang, Jake D.
Huang, Jake D.
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2023
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2024-04-18
Abstract
Electrochemical energy conversion devices like fuel cells, electrolyzers, and batteries are critical to sustainable energy development. However, substantial enhancements to efficiency, durability, and cost-effectiveness are needed to implement these technologies at large scale. Realizing these advances will require a clear understanding of the notoriously convoluted phenomena that govern the performance of electrochemical systems. To address this challenge, a coherent approach to building fundamental electrochemical knowledge is developed and applied to reversible proton-conducting ceramic cells.
First, a versatile framework for analyzing electrochemical impedance spectroscopy (EIS) is developed. EIS is a ubiquitous measurement technique that probes distinct, but strongly overlapping, frequency-domain signals of electrochemical processes. A new perspective on uncertainty and optimality in EIS deconvolution enables robust, autonomous separation of overlapping impedance responses and selection of interpretable models for the system under study. Next, an accelerated experimental characterization technique integrating heterogeneous time- and frequency-domain data is introduced. This method enables measurements 20-100 times faster than EIS without precision loss, allowing investigation of transient states and efficient completion of previously infeasible experiments. These characterization and analysis tools are then applied to a series of reversible proton-conducting ceramic electrochemical cells (PCECs). High-volume measurements under varied conditions are used to construct multi-dimensional hypersurface representations of electrochemical activity. The structure of these hypersurfaces is investigated with statistical and computer vision techniques, revealing an intricate and intertwined set of physicochemical processes. This study illuminates how individual cell components dictate performance as a function of operating conditions, providing the most detailed description of electrochemical phenomena in PCECs to date. Finally, these approaches are being adapted to an autonomous materials discovery platform that will independently investigate unexplored composition spaces to identify promising new materials for energy conversion.
This work suggests that shifting from conventional impedance measurement and one-dimensional analysis to multi-domain characterization and multi-dimensional analysis can substantially expedite scientific learning. The tools developed here, which are disseminated in several open-source software packages, should allow researchers to quickly understand the influence of new materials and device designs. This knowledge, in turn, can facilitate efficient hypothesis-driven research to drive innovations in electrochemical energy conversion.
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