Unlocking the Lattice Oxygen Mechanism for Alkaline Water Electrolysis: From Activation to Stabilization

Abstract

The oxygen evolution reaction (OER) at the anode constitutes a major kinetic barrier in the electrochemical water splitting process for hydrogen generation. Engaging the lattice oxygen-mediated mechanism (LOM) has emerged as a pivotal approach to surmount the intrinsic thermodynamic scale constraints of the conventional adsorbate evolution mechanism (AEM). The participation of lattice oxygen presents significant activity-stability obstacles that must be promptly resolved in practical applications. This study examines recent advancements in the activation and stabilization of LOM in alkaline OER, and explores three mechanism-driven strategies: elemental doping for intrinsic electronic modulation, dynamic structural evolution via defects and phase reconstruction, and heterostructure/interface engineering for space-constrained activation. We integrate these methodologies into a coherent descriptor framework, offering a standard framework for evaluating and projecting the LOM performance of various material families. The work methodically explores critical topics, including mechanistic discourse, the universality of activation techniques, and the difficulties of converting laboratory-scale performance to industrial applications. We anticipate future research topics, such as integrating machine learning with in-situ characterization, expanding LOM techniques to acidic environments, and the imperative of establishing standardized testing methodologies. This review seeks to serve as a significant reference for expert researchers, while also presenting an understandable introductory framework for novices aspiring to deeply grasp LOM-based catalyst design.

Article information

Article type
Review Article
Submitted
10 Mar 2026
Accepted
31 May 2026
First published
12 Jun 2026

Catal. Sci. Technol., 2026, Accepted Manuscript

Unlocking the Lattice Oxygen Mechanism for Alkaline Water Electrolysis: From Activation to Stabilization

R. Jin, Y. Dai, B. Wang, N. Gulshan, H. Aziz, G. Ma, X. Guo, Y. Ding, Y. Wang, D. Khalafallah and S. Li, Catal. Sci. Technol., 2026, Accepted Manuscript , DOI: 10.1039/D6CY00302H

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