Issue 11, 2023

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting

Abstract

Green hydrogen produced via electrochemical water splitting is a suitable candidate to replace emission-intensive fuels. However, the successful widespread adoption of green hydrogen is contingent on the development of low-cost, earth-abundant catalysts. Herein, machine learning models built on experimental data were used to optimize the precursor ratios of hydroxide-based electrocatalysts, with the objective of improving the product's electrocatalytic performance for overall water splitting. The Neural Network-based models were found to be the most effective in predicting and minimizing the overpotentials of the catalysts, reaching a minimum in two iterations. The relatively mild reaction conditions of the synthesis procedure, coupled with its scalability demonstrated herein, renders the optimized catalyst relevant for industrial implementation in the future. The optimized catalyst, characterized to be a molybdate-intercalated CoFe LDH, demonstrated overpotentials of 266 and 272 mV at 10 mA cm−2 for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.

Graphical abstract: Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting

Supplementary files

Article information

Article type
Communication
Submitted
24 May 2023
Accepted
14 Aug 2023
First published
21 Aug 2023

Mater. Horiz., 2023,10, 5022-5031

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting

C. Y. J. Lim, R. I Made, Z. H. J. Khoo, C. K. Ng, Y. Bai, J. Wang, G. Yang, A. D. Handoko and Y. Lim, Mater. Horiz., 2023, 10, 5022 DOI: 10.1039/D3MH00788J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements