Hydrogen Evolution Reaction of Electrodeposited Ni-W Films in Acidic Medium and Performance Optimization Using Machine Learning

Abstract

Ni−W alloy films were electrodeposited from a gluconate aqueous bath at pH=5.0, at varying current densities and temperatures. While there is little to no difference in composition, i. e., all films possess ~12 at.% W, their activity at hydrogen evolution reaction (HER) in acidic medium is greatly influenced by differences in surface morphology. The kinetics of HER in 0.5 M H2SO4 indicates that the best performing film was obtained at a current density of −4.8 mA/cm2 and 50 °C. The Tafel slopes (b) and the overpotentials at a geometric current density of −10 mA/cm2 (η10) obtained for 200 cycles of linear sweep voltammetry (LSV) from a set of films deposited using different parameters were fed into a machine learning algorithm to predict optimum deposition conditions to minimize b, η10, and the degradation of samples over time. The optimum deposition conditions predicted by the machine learning model led to the electrodeposition of Ni−W films with superior performance, exhibiting b of 33–45 mV/dec and an η10 of 0.09–0.10 V after 200 LSVs.