A mini-review on the MXenes capacity to act as electrocatalysts for the hydrogen evolution reaction

MXenes公司 密度泛函理论 材料科学 吉布斯自由能 纳米技术 氢经济 氢燃料 化学 计算化学 热力学 物理 有机化学
作者
Louai Mahdi Maghrabi,Nirpendra Singh,Kyriaki Polychronopoulou
出处
期刊:International Journal of Hydrogen Energy [Elsevier BV]
卷期号:51: 133-166 被引量:10
标识
DOI:10.1016/j.ijhydene.2023.09.291
摘要

Green hydrogen is seen as the fuel of the future as various efforts are made to further implement it in different industries and sectors. Green hydrogen offers a clean and emissions free renewable source of high energy density that helps work towards the global net zero emissions goal. Green hydrogen, which is produced via the hydrogen evolution reaction (HER), requires a technological breakthrough in the electrolyzer; the latter necessitates for the presence of non-precious, stable, and highly active electrocatalysts. Two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides (MXenes) are promising future electrocatalysts that have great reported exhibiting potential for commercial scalability due to their excellent intrinsic properties and structural flexibility. First, the synthesis methods are comprehensively summarized and discussed based on their electrocatalytic usage for the HER alongside their electronic properties. Second, this mini-review captures the current status of MXenes as electrocatalysts both experimentally and theoretically. The present MXenes can be separated into three categories: pristine, doped and hybrid structures where comparisons are drawn based on electrochemical descriptors backed by DFT studies. In addition, density functional theory (DFT) screening studies based on the Gibbs free energy of hydrogen adsorption are reported on different MXene variants. Pristine, double transition metal, carbonitride, vacancy induced, transition metal anchored and unique surface terminations MXenes are all reported, showing promising energetics for further experimental expansion in this field. Finally, secondary tools such as molecular dynamics and machine learning are coupled with DFT to explore novel MXene structures and efficiently predict their HER performance.
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