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Data-Driven Machine Learning Approach for Predicting the Capacitance of ZnCo2O4 -Based Supercapacitor Electrodes

https://doi.org/10.2139/ssrn.7073208
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inferredcites →https://doi.org/10.1002/adma.201304137openalex-snapshot
Carbons and Electrolytes for Advanced Supercapacitors
inferredcites →https://doi.org/10.1016/j.carbon.2017.08.022openalex-snapshot
All-solid-state hybrid supercapacitors based on ZnCo2O4 nanowire arrays and carbon nanorod electrode materials
inferredcites →https://doi.org/10.1016/j.ceramint.2016.09.105openalex-snapshot
3D ZnCo2O4 nanowires@MnO2 nanosheets core-shell structures grown on carbon cloth for excellent supercapacitor electrodes
inferredcites →https://doi.org/10.1016/j.colsurfa.2023.131423openalex-snapshot
2D sheet to 1D rod-like morphology regulated self-assembled ZnCo2O4 microstructures under mixed solvent conditions for battery-type supercapacitors
inferredcites →https://doi.org/10.1016/j.electacta.2012.12.056openalex-snapshot
Charging and discharging electrochemical supercapacitors in the presence of both parallel leakage process and electrochemical decomposition of solvent
inferredcites →https://doi.org/10.1016/j.ensm.2021.05.024openalex-snapshot
Machine learning-based prediction of supercapacitor performance for a novel electrode material: Cerium oxynitride
inferredcites →https://doi.org/10.1016/j.est.2022.106499openalex-snapshot
Electrochemical and electronic structure properties of high-performance supercapacitor based on Nd-doped ZnO nanoparticles
inferredcites →https://doi.org/10.1016/j.est.2024.110698openalex-snapshot
Electrode materials for supercapacitors: A comprehensive review of advancements and performance
inferredcites →https://doi.org/10.1016/j.est.2024.111808openalex-snapshot
Unveiling the hybrid era: Advancement in electrode materials for the high-performance supercapacitor: A comprehensive review
inferredcites →https://doi.org/10.1016/j.est.2024.112292openalex-snapshot
AI-based approach for predicting the storage performance of zinc oxide-based supercapacitor electrodes
inferredcites →https://doi.org/10.1016/j.est.2024.113556openalex-snapshot
Utilizing machine learning and deep learning for enhanced supercapacitor performance prediction
inferredcites →https://doi.org/10.1016/j.jallcom.2019.152456openalex-snapshot
Aqueous asymmetric supercapacitors based on ZnCo2O4 nanoparticles via facile combustion method
inferredcites →https://doi.org/10.1016/j.jallcom.2025.179240openalex-snapshot
Machine learning model of polypyrrole based-supercapacitor electrode: Fabrication, characterization, and prediction
inferredcites →https://doi.org/10.1016/j.jechem.2022.11.012openalex-snapshot
Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review
inferredcites →https://doi.org/10.1016/j.jiec.2024.07.014openalex-snapshot
Supercapacitors: Overcoming current limitations and charting the course for next-generation energy storage
inferredcites →https://doi.org/10.1016/j.jpowsour.2024.234911openalex-snapshot
Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation
inferredcites →https://doi.org/10.1016/j.matlet.2018.09.028openalex-snapshot
Artificial neural network enabled capacitance prediction for carbon-based supercapacitors
inferredcites →https://doi.org/10.1016/j.matlet.2021.130039openalex-snapshot
Synthesis and characterization of ZnCo2O4 electrode for high-performance supercapacitor application
inferredcites →https://doi.org/10.1021/acs.accounts.0c00785openalex-snapshot
Data-Driven Strategies for Accelerated Materials Design
inferredcites →https://doi.org/10.1021/acsnano.4c01787openalex-snapshot
Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes
inferredcites →https://doi.org/10.1038/s41598-023-33524-1openalex-snapshot
The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: a machine learning approach
inferredcites →https://doi.org/10.1039/c9na00105kopenalex-snapshot
Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
inferredcites →https://doi.org/10.1039/d1ta02505hopenalex-snapshot
A review on the recent advances in hybrid supercapacitors
inferredcites →https://doi.org/10.1039/d2ta07148gopenalex-snapshot
Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges
inferredcites →https://doi.org/10.1039/d4ya00577eopenalex-snapshot
A machine learning approach for estimating supercapacitor performance of graphene oxide nano-ring based electrode materials
inferredcites →https://doi.org/10.1063/5.0204701openalex-snapshot
Current research on transition metal oxide materials used to improve super-capacitor performance
inferredcites →https://doi.org/10.1088/1402-4896/ad190copenalex-snapshot
Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes
inferredcites →https://doi.org/10.1088/2399-6528/ac3574openalex-snapshot
Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes
inferredcites →https://doi.org/10.1149/2.f07103ifopenalex-snapshot
Advancing the Supercapacitor Materials and Technology Frontier for Improving Power Quality
inferredcites →https://doi.org/10.20964/2016.12.50openalex-snapshot
A Brief Review on Electrode Materials for Supercapacitor
inferredcites →https://doi.org/10.3390/catal10090969openalex-snapshot
Electrode Materials for Supercapacitors: A Review of Recent Advances
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