Machine Learning Driven Synthesis of Cobalt Oxide Entrapped Heteroatom-Doped Graphitic Carbon Nitride for Enhanced Oxygen Evolution Reaction.

Abstract

Developing highly efficient electrocatalysts for the oxygen evolution reaction is hindered by sluggish multi-electron kinetics, poor charge transfer efficiency, and limited active site accessibility. Transition metal-based electrocatalysts, such as cobalt oxides, have shown promise. However, poor charge transfer efficiency, limited active site accessibility, and suboptimal interaction with support materials have lowered their oxygen evolution reaction performance. Additionally, optimization of materials remains a complex task, often relying on trial-and-error approaches that do not clearly understand the key features that govern oxygen evolution reaction performance. In this study, we have addressed these challenges through machine learning, which enables the systematic design and optimization of electrocatalysts. By leveraging machine learning, we have developed a highly effective cobalt oxide nanocrystal-based electrocatalyst embedded within sulfur and phosphorus-doped carbon nitride. The homogeneous distribution of cobalt oxide nanocrystals on the sulfur and phosphorus-doped carbon nitride substrate further improves the accessibility of active sites during electrochemical reactions, leading to enhanced oxygen evolution reaction performance. The cobalt oxide sulfur and phosphorus-doped carbon nitride catalyst has shown promising oxygen evolution reaction activity, characterized by a low overpotential of 262 mV, a Tafel slope of 66 mV dec ⁻ ¹, and a high electrochemically active surface area of 140.58 cm². These results highlight the synergistic interaction between cobalt oxide and sulfur and phosphorus-doped carbon nitride, which contributes to the catalyst's superior electrocatalytic performance and provides a promising pathway for the design of advanced oxygen evolution reaction catalysts through machine learning-guided material optimization.