Enno Kätelhön

Enno Kätelhön

University of Oxford

H-index: 29

Europe-United Kingdom

About Enno Kätelhön

Enno Kätelhön, With an exceptional h-index of 29 and a recent h-index of 20 (since 2020), a distinguished researcher at University of Oxford, specializes in the field of Theory and Modelling in Physical Chemistry.

His recent articles reflect a diverse array of research interests and contributions to the field:

Rotating Disk Electrodes beyond the Levich Approximation: Physics-Informed Neural Networks Reveal and Quantify Edge Effects

Machine learning in fundamental electrochemistry: Recent advances and future opportunities

A critical evaluation of using physics-informed neural networks for simulating voltammetry: strengths, weaknesses and best practices

Experimental voltammetry analyzed using artificial intelligence: thermodynamics and kinetics of the dissociation of acetic acid in aqueous solution

Predicting voltammetry using physics-informed neural networks

The application of physics-informed neural networks to hydrodynamic voltammetry

Determination of standard electrochemical rate constants from semi-circular sweep voltammetry: A combined theoretical and experimental study

A new approach to characterising the porosity of particle modified electrodes: Potential step chronoamperometry and the diffusion indicator

Enno Kätelhön Information

University

Position

___

Citations(all)

2506

Citations(since 2020)

1478

Cited By

3019

hIndex(all)

29

hIndex(since 2020)

20

i10Index(all)

58

i10Index(since 2020)

46

Email

University Profile Page

Google Scholar

Enno Kätelhön Skills & Research Interests

Theory and Modelling in Physical Chemistry

Top articles of Enno Kätelhön

Rotating Disk Electrodes beyond the Levich Approximation: Physics-Informed Neural Networks Reveal and Quantify Edge Effects

Analytical Chemistry

2023/8/17

Machine learning in fundamental electrochemistry: Recent advances and future opportunities

2023/4/1

Haotian Chen
Haotian Chen

H-Index: 23

Enno Kätelhön
Enno Kätelhön

H-Index: 21

A critical evaluation of using physics-informed neural networks for simulating voltammetry: strengths, weaknesses and best practices

Journal of Electroanalytical Chemistry

2022/11/15

Experimental voltammetry analyzed using artificial intelligence: thermodynamics and kinetics of the dissociation of acetic acid in aqueous solution

Analytical Chemistry

2022/4/5

Predicting voltammetry using physics-informed neural networks

The Journal of Physical Chemistry Letters

2022/1/10

The application of physics-informed neural networks to hydrodynamic voltammetry

Analyst

2022

Haotian Chen
Haotian Chen

H-Index: 23

Enno Kätelhön
Enno Kätelhön

H-Index: 21

Determination of standard electrochemical rate constants from semi-circular sweep voltammetry: A combined theoretical and experimental study

Journal of Electroanalytical Chemistry

2021/1/1

Enno Kätelhön
Enno Kätelhön

H-Index: 21

A new approach to characterising the porosity of particle modified electrodes: Potential step chronoamperometry and the diffusion indicator

Applied Materials Today

2021/12/1

Use of artificial intelligence in electrode reaction mechanism studies: Predicting voltammograms and analyzing the dissociative CE reaction at a hemispherical electrode

Analytical Chemistry

2021/9/23

Unscrambling illusionary catalysis in three-dimensional particle-modified electrodes: Reversible reactions at conducting particles

Applied Materials Today

2020/3/1

Enno Kätelhön
Enno Kätelhön

H-Index: 21

Particle-modified electrodes: General mass transport theory, experimental validation, and the role of electrostatics

Applied Materials Today

2020/3/1

Lifu Chen
Lifu Chen

H-Index: 10

Enno Kätelhön
Enno Kätelhön

H-Index: 21

Reversible voltammetry at cylindrical electrodes: Validity of a one-dimensional model

Journal of Electroanalytical Chemistry

2020/2/15

Enno Kätelhön
Enno Kätelhön

H-Index: 21

See List of Professors in Enno Kätelhön University(University of Oxford)

Co-Authors

academic-engine