Removing 65Years of Approximation in Rotating RingDisk Electrode Theory with Physics-Informed Neural Networks
The rotating Ring Disk Electrode (RRDE), since its introductionin 1959 by Frumkin and Nekrasov, has become indispensable with diverseapplications in electrochemistry, catalysis, and material science.The collection efficiency () is an important parameter extracted fromthe ring and disk currents of the RRDE, providing valuable informationabout reaction mechanism, kinetics, and pathways. The theoreticalprediction of is a challenging task: requiring solutionof the complete convective diffusion mass transport equation withcomplex velocity profiles. Previous efforts, including by Albery andBruckenstein who developed the most widely used analytical equations,heavily relied on approximations by removing radial diffusion andusing approximate velocity profiles. 65 years after the introductionof RRDE, we employ a physics-informed neural network to solve thecomplete convective diffusion mass transport equation, to reveal theformerly neglected edge effects and velocity corrections on , and to provide a guideline where conventionalapproximation is applicable.
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