Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jan 29;11(10):5479-5486.
doi: 10.1039/d0ra09837j. eCollection 2021 Jan 28.

Insights into the estimation of capacitance for carbon-based supercapacitors

Majedeh Gheytanzadeh  1 , Alireza Baghban  2 , Sajjad Habibzadeh  1   3 , Ahmad Mohaddespour  4 , Otman Abida  4
Affiliations

Insights into the estimation of capacitance for carbon-based supercapacitors

Majedeh Gheytanzadeh et al. RSC Adv. .

Abstract

Carbon-based materials are broadly used as the active component of electric double layer capacitors (EDLCs) in energy storage systems with a high power density. Most of the reported computational studies have investigated the electrochemical properties under equilibrium conditions, limiting the direct and practical use of the results to design electrochemical energy systems. In the present study, for the first time, the experimental data from more than 300 published papers have been extracted and then analyzed through an optimized support vector machine (SVM) by a grey wolf optimization (GWO) algorithm to obtain a correlation between carbon-based structural features and EDLC performance. Several structural features, including calculated pore size, specific surface area, N-doping level, I D/I G ratio, and applied potential window were selected as the input variables to determine their impact on the respective capacitances. Sensitivity analysis, which has only been performed in this study for approximating the EDLC capacitance, indicated that the specific surface area of the carbon-based supercapacitors is of the greatest effect on the corresponding capacitance. The proposed SVM-GWO, with an R 2 value of 0.92, showed more accuracy than all the other proposed machine learning (ML) models employed for this purpose.

PubMed Disclaimer

Conflict of interest statement

There are no conflicts to declare.

Figures

Fig. 1
Fig. 1. (a) The social hierarchy of grey wolves, (b) reposition mechanism of ω wolves according to positions of α, β and δ wolves.
Fig. 2
Fig. 2. Sensitivity analysis for determining effective variables on the capacitance of the carbon-based EDCLs.
Fig. 3
Fig. 3. Flowchart of optimized SVM with GWO algorithm.
Fig. 4
Fig. 4. William's plot of the proposed GWO-SVM to find outliers.
Fig. 5
Fig. 5. Experimental versus predicted capacitances of the carbon-based EDLCs from the GWO-SVM.
Fig. 6
Fig. 6. Regression plot of the train and test dataset of the capacitances of carbon-based EDCLs.
Fig. 7
Fig. 7. Relative deviation plot of GWO-SVM for the capacitance of carbon-based EDCLs.
See this image and copyright information in PMC

References

    1. Wang K. Wang W. Wang L. Li L. Energies. 2020;13:5297. doi: 10.3390/en13205297. - DOI
    1. Xiao F. Jun R. Duan C. Liwei L. Int. J. Electrochem. Sci. 2020;15:9499–9516.
    1. Xia G. Huang Y. Li F. Wang L. Pang J. Li L. Wang K. Front. Chem. Sci. Eng. 2020;14:1039–1051. doi: 10.1007/s11705-019-1901-5. - DOI
    1. Xiao Feng Y. Z. Kang L. Wang L. Duan C. Yin K. Pang J. Wang K. Front. Chem. Sci. Eng. 2020:1–13.
    1. Piłatowicz G. Marongiu A. Drillkens J. Sinhuber P. Sauer D. U. J. Power Sources. 2015;296:365–376. doi: 10.1016/j.jpowsour.2015.07.073. - DOI