Tuning Charge Storage in Bimetallic CoV-LDH for High-Performance Supercapacitor: A Synergistic Experimental and Machine Learning Approach.

The partial reduction of layered double hydroxides (LDHs) is becoming a vital approach to harness their electrochemical capabilities for high-performance supercapacitors (SCs). This paper provides a synergistic experimental and theoretical data study of controlled partial reduction, density functional theory (DFT), and machine learning (ML) to design the oxygen vacancy (Vo) chemistry of cobalt vanadium layered double hydroxides (CoV-LDHs). A solution-based partial-reduction protocol introduces Vo and provides the opportunity to precisely modulate the LDH lattice with a significant enhancement of charge-storage performance. The Vo-CoV-LDH electrode exhibits a specific capacitance of 2437 F g- 1 at 2 A g- 1, significantly surpassing its unmodified CoV-LDH (1371 F g- 1). Moreover, it delivers 78.4% capacitance retention at escalating current densities (2-10 A g- 1), in contrast to 55% for the untreated LDH. Incorporated into an asymmetric supercapacitor (ASC) device, Vo-CoV-LDH attained a remarkable energy density of 47.1 W h kg- 1 at a power density of 468.1 W kg- 1. DFT simulations reveal that the availability of Vo causes the bandgap to be narrower and the number of states near the Fermi level to be higher to accelerate electronic conductivity and redox dynamics. Simultaneously, machine-learning models are used to explain quantitative relationships between the parameters of synthesis, concentration of vacancies, and electrochemical performance, with coefficients of determination of more than 0.98. The findings support experimental reproducibility and predictive accuracy. The work demonstrates the synergistic efforts of partial reduction, DFT knowledge, and ML modeling to design Vo-engineered LDHs and, thus, a generalizable approach to the creation of an advanced energy storage material is demonstrated.