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Entropy-engineered molybdate/CNT supercapacitors with machine-learning-assisted lifetime prediction and device optimization
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- Authors
- Sayed Mohammed Adnan, Mohd Shoeb, Fouzia Mashkoor, Kwang-Ju Kim, Mi-Sun Kang, Jungwon Yu, Hyan Su Bae, In-Su Jang, Changyoon Jeong
- Issue Date
- 2026-09
- Citation
- Journal of Power Sources, v.685, pp.1-22
- ISSN
- 0378-7753
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Entropy-guided electrode design is a promising strategy for improving the redox-site diversity, charge-transfer kinetics, and cycling durability of pseudocapacitive oxides. Herein, a medium-entropy molybdate, (Co,Mn,Ni,Zn)MoO4, is integrated with carbon nanotubes to develop a high-performance supercapacitor electrode. The multication A-site configuration expands the accessible redox chemistry, while the MoO42− framework supports structural stability in alkaline electrolyte. The CNT network provides continuous conductive pathways, reduces polarization, and enhances utilization of disorder-generated redox sites. The optimized composite delivers 880 F g-1 at 2 A g-1 and retains 580 F g-1 at 10 A g-1 in a three-electrode configuration, with 83% capacitance retention after 10,000 cycles. A symmetric device operated at 1.2 V in 6 M KOH achieves an energy density of 23.2 Wh kg-1 at 1200 W kg-1, along with 89% capacitance retention and 98% coulombic efficiency during extended cycling. Kinetic analysis confirms a hybrid charge-storage mechanism dominated by surface-controlled processes with diffusion-assisted pseudocapacitive contribution. Machine-learning models based on voltage, current density, and cycle number enable early prediction of long-term stability. This study provides a device-relevant framework combining entropy-engineered oxide design with data-guided lifetime optimization for durable supercapacitors.
- Keyword
- Cycle life prediction, Machine learning, Medium-entropy molybdate, Supercapacitor
- KSP Keywords
- A-site, Alkaline electrolyte, CNT network, Capacitance retention, Carbon nano-tube(CNT), Charge transfer kinetics, Conductive pathways, Coulombic Efficiency, Cycle number, Early prediction, Hybrid charge