Facile design of MOF-derived porous CeO2/MWCNT nanocomposites for the hydrogen evolution reaction and machine learning-assisted stability forecasting

Abstract

Applying porous electrocatalysts in electrochemical water splitting (ECWS) represents a highly effective approach to enhancing catalytic performance. In this context, MOF-derived metal oxides with controlled morphologies are considered promising candidates for advanced electrochemical studies. This study focuses on the synthesis of cerium oxide (CeO2) nanostructures derived from a cerium-based MOF, specifically Ce-1,3,5-benzenetricarboxylic acid (Ce–BTC), through thermal decomposition. To further improve the conductivity and catalytic activity, the CeO2 nanostructures were modified with varying amounts of multiwalled carbon nanotubes (MWCNTs) via an ex situ sonochemical method, forming CeO2/MWCNT nanocomposites (denoted as CCNT NCs). Binder-free electrodes were fabricated by brush coating the NCs onto nickel foam substrates. The electrocatalytic performance was assessed for the hydrogen evolution reaction (HER) in 1 M KOH. Among the synthesised composites, the CCNT-0.1 NCs (containing 0.1 wt% MWCNTs) exhibited the most promising performance, achieving a low overpotential of 87 mV at a current density of −10 mA cm−2 and a Tafel slope of 96 mV dec−1. These results surpass the performance of many previously reported CeO2-based HER electrocatalysts. The long-term operational stability of the CCNT-0.1 electrode was also analyzed using a Long Short-Term Memory (LSTM)-based time series model to predict its degradation behavior over extended durations. Overall, this work demonstrates the synergistic advantages of carbonaceous materials and nanostructured metal oxide NCs as efficient and durable electrocatalysts for hydrogen generation.

Graphical abstract: Facile design of MOF-derived porous CeO2/MWCNT nanocomposites for the hydrogen evolution reaction and machine learning-assisted stability forecasting

Supplementary files

Article information

Article type
Paper
Submitted
13 May 2025
Accepted
04 Aug 2025
First published
15 Aug 2025

Nanoscale, 2025, Advance Article

Facile design of MOF-derived porous CeO2/MWCNT nanocomposites for the hydrogen evolution reaction and machine learning-assisted stability forecasting

M. M. Patil, P. A. Koyale, S. P. Sadavar, A. R. Shelake, T. D. Dongale, A. G. Dhodamani, S. S. Sutar and S. D. Delekar, Nanoscale, 2025, Advance Article , DOI: 10.1039/D5NR01965F

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