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

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Small (Weinheim an Der Bergstrasse, Germany)
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Controlled partial reduction of cobalt vanadium layered double hydroxides (CoV-LDHs) introduces oxygen vacancies (Vo) to significantly enhance supercapacitor performance. This approach, combining experiments, DFT, and ML, offers a generalizable strategy for advanced energy storage materials.

Keywords:
DFTLDHmachine learningsupercapacitortuning charge storage

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Layered double hydroxides (LDHs) are crucial for supercapacitors (SCs).
  • Partial reduction is a key strategy to enhance LDH electrochemical properties.
  • Oxygen vacancies (Vo) are critical for modulating LDH performance.

Purpose of the Study:

  • To design oxygen vacancy (Vo) chemistry in cobalt vanadium layered double hydroxides (CoV-LDHs) using controlled partial reduction.
  • To synergistically combine experimental, density functional theory (DFT), and machine learning (ML) approaches.
  • To enhance the charge-storage performance of CoV-LDHs for supercapacitors.

Main Methods:

  • Solution-based partial-reduction protocol to introduce oxygen vacancies.
  • Experimental characterization of Vo-CoV-LDH electrodes.
  • Density Functional Theory (DFT) simulations to understand electronic structure modifications.
  • Machine Learning (ML) models to correlate synthesis parameters with electrochemical performance.

Main Results:

  • Vo-CoV-LDH exhibited a specific capacitance of 2437 F g⁻¹ (vs. 1371 F g⁻¹ for unmodified CoV-LDH).
  • Improved capacitance retention (78.4% at 2-10 A g⁻¹) compared to untreated LDH (55%).
  • Asymmetric supercapacitor (ASC) device with Vo-CoV-LDH achieved 47.1 Wh kg⁻¹ energy density.
  • DFT confirmed narrowed bandgap and increased states near Fermi level, enhancing conductivity and redox dynamics.
  • ML models achieved coefficients of determination > 0.98 for predicting performance.

Conclusions:

  • Controlled partial reduction is an effective method to engineer Vo-CoV-LDHs for superior supercapacitor applications.
  • The synergistic integration of experimental, DFT, and ML methods provides a powerful, generalizable approach for designing advanced energy storage materials.
  • The study demonstrates significant improvements in electrochemical performance and offers predictive accuracy for material design.