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Pt/α-MoC Catalyst Boosting pH-Universal Hydrogen Evolution Reaction at High Current Densities

Wei Liu, Anyang Wang, Jihan Zhang, Shixiang Yu, Maolin Wang, Shuheng Tian, Haoyi Tang, Ziwen Zhao, Xiao Ren, Yuzheng Guo, Ding Ma

2025ACS Nano28 citationsDOI

Abstract

Constructing subnanometric electrocatalysts is an efficient method to synergistically accelerate H 2 O dissociation and H + reduction for pH-universal hydrogen evolution reaction (HER) for industrial water electrolysis to produce green hydrogen. Here, we construct a subnanometric Pt/α-MoC catalyst, where the α-MoC component can dissociate water effectively, with the rapid proton release kinetics of Pt species on Pt/α-MoC to obtain a good HER performance at high current densities in all-pH electrolytes. Quasi-in situ X-ray photoelectron spectroscopy analyses and density functional theory calculations confirm the highly efficient water dissociation capability of α-MoC and the thermodynamically favorable desorption process of hydrolytically dissociated protons on Pt sites at the high current density. Consequently, Pt/α-MoC requires only a low overpotential of 125 mV to achieve a current density of 1000 mA cm –2 . Moreover, a Pt/α-MoC-based proton exchange membrane water electrolysis device exhibits a low cell voltage (1.65 V) and promising stability over 300 h with no performance degradation at an industrial-level current density of 1 A cm –2 . Notably, even at a current of 100 A, the cell voltage remains low at 2.15 V, demonstrating Pt/α-MoC’s promising potential as a scalable alternative for industrial hydrogen production. These findings elucidate the synergistic mechanism of α-MoC and atomically dispersed Pt in promoting efficient HER, offering valuable guidance for the design of electrocatalysts in high current density hydrogen.

Topics & Concepts

CatalysisBoosting (machine learning)HydrogenMaterials scienceChemistryChemical engineeringNanotechnologyComputer scienceOrganic chemistryEngineeringMachine learningElectrocatalysts for Energy ConversionAdvanced Memory and Neural ComputingFuel Cells and Related Materials
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