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

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 HSO indicates that the best performing film was obtained at a current density of -4.8 mA/cm and 50 °C. The Tafel slopes (b) and the overpotentials at a geometric current density of -10 mA/cm (η) 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, η, 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 η of 0.09-0.10 V after 200 LSVs.