Conductivity boosted BiVO₄ for enhanced OER and supercapacitive performance: stability insights with modeling, predictions, and forecasting using machine learning technique

Abstract

To overcome the inherent limitations in the energy generation and storage properties of transition metal-based catalysts, it is crucial to develop processes that produce catalytic materials with high performance and long-lasting effectiveness. Herein, we synthesized Metal-Organic Framework (MOF)-derived BiVO₄ by mixing two separately prepared MOFs of Bi and V using trimesic acid and terephthalic acid as linkers. The separately prepared monometallic MOFs were then mixed and carbonized in an inert atmosphere followed by oxidation in air which gives the sample BiVO₄ with carbon (BVC). The prepared BVC electrode showed the overpotential 364 mV for oxygen evolution reaction at the current density of 10 mA cm^-2. In addition, the obtained BVC supercapacitor possesses a high specific capacity of 134.17 mAh g^-1 (483 C g^-1) at 1 mA cm^-2 current density. The aqueous and solid-state symmetric supercapacitor devices were also fabricated and achieved specific capacitance of 160.9 F g^-1 and 109.8 F g^-1 at 1 mA cm^-2 current density, respectively. Moreover, the Long Short-Term Memory-based machine learning technique was employed to model, predict, and forecast the chronoamperometric stability of MOF-derived BVC electrodes for oxygen evolution reaction applications, and the capacitive retention and Coulombic efficiency BVC electrodes. The exceptional performance of the BVC electrodes is attributed to their porous structure containing conducting carbon, which offers enhanced conductivity, larger surface area and increased reactive sites for efficient electronic and ionic transfer. This novel approach to the synthesis of MOF-derived BVC has opened up new pathways for future energy storage and conversion.

Keywords

Metal-organic framework / monometallic / multifunctional / oxygen evolution reaction / supercapacitor / machine learning

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