Efficient design and synthesis of an amorphous conjugated polymer network for a metal-free electrocatalyst of hydrogen evolution reaction

Abstract

Performances of functional polymers are enhanced by designing the structures in different hierarchies, such as monomers, polymers, secondary structures, and nanoscale morphologies. In the present work, an amorphous conjugated polymer network was designed and synthesized to obtain a metal-free electrocatalyst for hydrogen evolution reaction (HER) in an efficient manner. A prediction model for the catalytic performance, i.e. overpotential for HER, was constructed using machine learning on small data based on the literature. The straightforward linear prediction model assisted in the design of the network polymer containing quinone and heteroaromatic moieties. The simultaneous multiple reactions of benzoquinone (BQ) and benzoxazole (BO) formed the amorphous conjugated polymer network at 200 °C. After the morphology control, the BQ–BO polymer showed an overpotential of 230 mV for the electrochemical HER at 10 mA cm⁻², which is one of the highest performances in the metal-free electrocatalysts synthesized at low temperatures. Moreover, we found that the amorphous conjugated polymer networks showed specific hydration behavior in aqueous media. The results indicate that designing an amorphous conjugated polymer network coupled with machine learning is a potential approach for the development of functional materials.