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Conductivity boosted BiVO4 for enhanced OER and supercapacitive performance: stability insights with modeling, predictions, and forecasting using machine learning technique
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Energy Mater 2024;4:[Accepted].
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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 MOF-derived BiVO4 by mixing two separately prepared Metal-Organic Frameworks (MOF) of Bi and V with the help of 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 OER at the current density of 10 mA cm-2. In addition, the obtained BVC supercapacitor possesses with a high specific capacity of 134.17 mAh g-1 (483 C g-1) at 1 mA cm-2 current density. The aqueous symmetric supercapacitor 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 electrode for OER applications, as well as 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 the future energy storage and conversion.
Keywords
Metal-organic framework, monometallic, multifunctional, oxygen evolution reaction, supercapacitor, machine learning
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Chaudhari SA, Patil VV, Jadhav VA, Thorat P, Sutar SS, Dongale TD, Parale V, Patil V, Mhamane DS, Mali MG, Park HH. Conductivity boosted BiVO4 for enhanced OER and supercapacitive performance: stability insights with modeling, predictions, and forecasting using machine learning technique. Energy Mater 2024;4:[Accept]. http://dx.doi.org/10.20517/energymater.2024.229
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© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.