Beyond activity: from machine learning screening to stability decoding on the study of self-supported metal phosphides in alkaline hydrogen evolution reaction

Abstract

With the rapid depletion of fossil fuels, the development of renewable energy is urgent. The hydrogen production technology in alkaline water electrolysis has received widespread attention due to its environmental friendliness and high efficiency. Self-supported transition metal phosphides (TMPs) have shown great potential as alkaline hydrogen evolution reaction (HER) catalysts owing to their low cost, high activity, and superior stability. In addition, compared to traditional powder materials, self-supporting electrode materials could be favorable for enhancement of conductivity, activity, and durability. Herein, the latest development of self-supported TMPs in alkaline HER is reviewed. Firstly, the application of machine learning in the screening of high-performance TMP-based HER catalysts is highlighted. Then, the preparation methods and reaction principles for constructing self-supported TMPs on substrates are summarized. Subsequently, different modulation strategies and structure–activity relationships are emphasized to improve the performance of self-supported TMPs. Afterward, a detailed decoding of the key factors influencing the stability of TMPs in alkaline HER and the corresponding improvement strategies is presented. Finally, a brief overview of the challenges and research directions of future exploration for self-supported TMPs in alkaline HER is provided. This review is of great significance for constructing highly active self-supported TMP hydrogen production electrodes.