Bayesian-Optimization-Assisted Laser Reduction of Poly(acrylonitrile) for Electrochemical Applications

Abstract

Laser reduction of polymers has recently been explored to rapidly and inexpensively synthesize high-quality graphitic and carbonaceous materials. However, in past work, laser-induced graphene has been restricted to semiaromatic polymers and graphene oxide; in particular, poly(acrylonitrile) (PAN) is claimed to be a polymer that cannot be laser-reduced successfully to form electrochemically active material. In this work, three strategies to surmount this barrier are employed: (1) thermal stabilization of PAN to increase its sp 2 content for improved laser processability, (2) prelaser treatment microstructuring to reduce the effects of thermal stresses, and (3) Bayesian optimization to search the parameter space of laser processing to improve performance and discover morphologies. Based on these approaches, we successfully synthesize laser-reduced PAN with a low sheet resistance (6.5 Ω sq −1 ) in a single lasing step. The resulting materials are tested electrochemically, and their applicability as membrane electrodes for vanadium redox flow batteries is demonstrated. This work demonstrates electrodes that are processed in air, below 300 °C, which are cycled stably over 2 weeks at 40 mA cm −2 , motivating further development of laser reduction of porous polymers for membrane electrode applications such as RFBs.