Removing 65 Years of Approximation in Rotating Ring Disk Electrode Theory with Physics-Informed Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F24%3A10255421" target="_blank" >RIV/61989100:27360/24:10255421 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.acs.org/doi/10.1021/acs.jpclett.4c01258" target="_blank" >https://pubs.acs.org/doi/10.1021/acs.jpclett.4c01258</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jpclett.4c01258" target="_blank" >10.1021/acs.jpclett.4c01258</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Removing 65 Years of Approximation in Rotating Ring Disk Electrode Theory with Physics-Informed Neural Networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Removing 65 Years of Approximation in Rotating Ring Disk Electrode Theory with Physics-Informed Neural Networks

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10405 - Electrochemistry (dry cells, batteries, fuel cells, corrosion metals, electrolysis)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Physical Chemistry Letters

ISSN

1948-7185

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

24

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

6315-6324

Kód UT WoS článku

001243965800001

EID výsledku v databázi Scopus

2-s2.0-85196030632