Hydrogen Evolution Reaction of Electrodeposited Ni‐W Films in Acidic Medium and Performance Optimization Using Machine Learning
Roger de Paz-Castany, Konrad Eiler, Aliona Nicolenco, Maria Lekka, Eva García‐Lecina, Guillaume Brunin, Gian‐Marco Rignanese, David Waroquiers, Thomas Collet, Annick Hubin, Eva Pellicer
Abstract
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 H 2 SO 4 indicates that the best performing film was obtained at a current density of −4.8 mA/cm 2 and 50 °C. The Tafel slopes ( b ) and the overpotentials at a geometric current density of −10 mA/cm 2 ( η 10 ) 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 10 , 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 10 of 0.09–0.10 V after 200 LSVs.