The rotating Ring Disk Electrode (RRDE), since its introduction in 1959 by Frumkin and Nekrasov, has become indispensable with diverse applications in electrochemistry, catalysis, and material science. The collection efficiency (N) is an important parameter extracted from the ring and disk currents of the RRDE, providing valuable information about reaction mechanism, kinetics, and pathways. The theoretical prediction of N is a challenging task: requiring solution of the complete convective diffusion mass transport equation with complex velocity profiles. Previous efforts, including by Albery and Bruckenstein who developed the most widely used analytical equations, heavily relied on approximations by removing radial diffusion and using approximate velocity profiles. 65 years after the introduction of RRDE, we employ a physics-informed neural network to solve the complete convective diffusion mass transport equation, to reveal the formerly neglected edge effects and velocity corrections on N, and to provide a guideline where conventional approximation is applicable.

Department of Chemistry and Physico chemical processes Faculty of Materials Science and Technology VSB Technical University of Ostrava 17 listopadu 2172 15 708 00 Ostrava Poruba Czech Republic

Department of Chemistry Physical and Theoretical Chemistry Laboratory University of Oxford South Parks Road Oxford OX1 3QZ Great Britain

Department of Computing Imperial College London Exhibition Road London SW7 2AZ United Kingdom

Department of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ United Kingdom

Independent Researcher Offenbach am Main 63067 Germany

St John's College University of Oxford St Giles' Oxford OX1 3JP Great Britain

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