Electrocatalysis has the potential to become a more sustainable approach to generate hydrogen as a clean energy source and chemical feedstock. Finding a stable, eco-friendly, low cost and highly efficient catalyst is one of the prerequisites to realize large-scale industrial electrocatalytic hydrogen production. Two-dimensional metal carbide and nitride (MXene) materials have shown characteristics of promising hydrogen evolution reaction (HER) catalysts, but challenges in terms of both hydrogen adsorption strength and reaction rate still need to be addressed. In addition, previous theoretical studies of MXenes for the HER focus mainly on the thermodynamics (e.g. hydrogen adsorption energy) rather than the kinetics of the reaction (e.g. energy barrier and reaction rate). In this work, we utilize high-throughput computational methods to study both the HER thermodynamics and kinetics of M2XO2 type MXenes and how their HER activity can be enhanced by the modification of different transition metal (TM) adatoms. Compared to the relatively weak HER activity observed for the majority of pristine MXenes, the addition of TM adatoms on the MXenes surface is predicted to enhance their HER activity significantly. The presence of TM not only optimizes the Gibbs free energy of hydrogen adsorption ( ?GH) but also reduces the H2 production activation barrier. Intriguingly, we observed a HER mechanism preference switch from Volmer-Heyrovsky observed on pristine MXenes to Volmer-Tafel after modification with TM adatoms. On the basis of in-depth and systematic exploration of the electronic structure and interaction between hydrogen and MXenes, the origin of the mechanism preference switch is linked to the TM-induced electron redistribution on the surface of the MXene.
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Q.F.Z. is supported by National Natural Science Foundation of China (11404017), Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Human Resources and Social Security of China, and the program for New Century Excellent Talents in University (NCET-12-0033). R.F.Z. is supported by National Natural Science Foundation of China (51471018) and National Thousand Young Talents Program of China, and the 111 Project (B17002). Q.F.Z and R.F.Z are also supported by National Key Research and Development Program of China (No. 2017YFB0702100). Z.W.S. acknowledges the support of the Singapore National
Research Foundation (NRF Fellowship Award No. NRF-NRFF2017-04). H.T.W. acknowledges the support from the Rowland Fellows Program at Rowland Institute, Harvard University. D.L. acknowledges the support from the European Regional Development Fund in the IT4Innovations national supercomputing center - path to
exascale project, project number CZ.02.1.01/0.0/0.0/16_013/0001791 within the Operational Programme Research, Development and Education and the Grant No. 17-27790S of the Grant Agency of the Czech Republic.