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Performance optimization of Co2O3-PVDF-CNT-based supercapacitor electrode through multi-response optimization method

  • Mayank Vyas
  • Kapil PareekEmail author
  • Rupesh Rohan
  • Pawan Kumar
Original Paper
  • 23 Downloads

Abstract

Taguchi optimization technique is used for obtaining optimal chemical composition of active materials to achieve maximum specific capacitance and minimum ESR for cobalt oxide-based electrode for supercapacitor. The study is based on interaction effect of three control factors which are mass loading of Co2O3, PVDF and MW-CNT, respectively. A regression model equation is also developed and validated experimentally. Optimum level of the control factors for maximum specific capacitance is found as Co2O3-40%, PVDF-10%, and MWCNT-30% and for minimum ESR is found as Co2O3-40%, PVDF-20%, and MWCNT-5%, respectively. The results can lead to new insights into development of supercapacitors with enhanced performance.

Keywords

Supercapacitor Taguchi optimization Metal oxide Impedance spectroscopy Cyclic voltammetry 

Notes

Funding information

The authors gratefully acknowledge the support from the Department of Science and Technology, Govt. of India (ECR/2016/1001039). Testing facilities were available at Material Research Centre (MRC) and Centre for Energy & Environment, Malaviya National Institute of Technology Jaipur.

References

  1. 1.
    Liu Y, Zhang Y, Wu XW, Zhu YS, Wu YP (2017) Features of design and fabrication of metal oxide–based supercapacitors. In: Metal Oxides in Supercapacitors. Elsevier Science, ISBN 0128104651, 9780128104651 pp 25–47Google Scholar
  2. 2.
    Sharma K, Pareek K, Rohan R, Kumar P (2019) Flexible supercapacitor based on three-dimensional cellulose/graphite/polyaniline composite. Int J Energy Res 43(1):604–611CrossRefGoogle Scholar
  3. 3.
    Brousse T, Crosnier O, Bélanger D, Long Jeffrey W (2017) Capacitive and pseudocapacitive electrodes for electrochemical capacitors and hybrid devices, Metal Oxides in Supercapacitors. Elsevier Science. ISBN 0128104651, 9780128104651, pp 1–24Google Scholar
  4. 4.
    Yang D, Ionescu MI (2017) Metal oxide–carbon hybrid materials for application in supercapacitors. In: Metal Oxides in Supercapacitors. Elsevier Elsevier Science. ISBN 0128104651, 9780128104651, pp 193–218Google Scholar
  5. 5.
    Cai Y-M, Qin Z-Y, Chen L (2011) Effect of electrolytes on electrochemical properties of graphene sheet covered with polypyrrole thin layer. Prog Nat Sci Mater Int 21(6):460–466CrossRefGoogle Scholar
  6. 6.
    Chen H, Cong TN, Yang WT, Li C, Yulong YD (2009) Progress in electrical energy storage system: a critical review. Prog Nat Sci 19(3):291–312CrossRefGoogle Scholar
  7. 7.
    Wang R, Wu J (2017) Structure and basic properties of ternary metal oxides and their prospects for application in supercapacitors, Metal Oxides In Supercapacitors. Elsevier Elsevier Science. ISBN 0128104651, 9780128104651, pp 99–132Google Scholar
  8. 8.
    Carmezim MJ, Santos CF (2017) Electrolytes in metal oxide supercapacitors, Metal Oxides in Supercapacitors. Elsevier Science. ISBN 0128104651, 9780128104651, pp 49–78Google Scholar
  9. 9.
    Rashed AE, El-Moneim AA (2017) Two steps synthesis approach of MnO2/graphene nanoplates/graphite composite electrode for supercapacitor application. Mater Today Energy 3:24–31CrossRefGoogle Scholar
  10. 10.
    Yassine M, Fabris D (2017) Performance of commercially available supercapacitors. Energies 10(9):1340Google Scholar
  11. 11.
    Allagui A, Freeborn Todd J, Elwakil Ahmed S, Maundy Brent J (2016. 6: P.) Re-evaluation of performance of electric double-layer capacitors from constant-current charge/discharge and cyclic voltammetry. Sci Rep 6:38568CrossRefGoogle Scholar
  12. 12.
    Martins VL, Torresi RM, and Rennie AJR 2018 Design considerations for ionic liquid based electrochemical double layer capacitors. Electrochimica Acta, 270: 453-460)Google Scholar
  13. 13.
    Tung-Hsu H, Chi-Hung S, Wang-Lin L (2007) Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm. Powder Technol 173(3):153–162CrossRefGoogle Scholar
  14. 14.
    Al-Refaie A, Wu T, Li M (2009) Data development analysis approaches for solving the multiresponse problem in the Taguchi method, Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 23(2):159–173  https://doi.org/10.1017/S0890060409000043
  15. 15.
    Sibalija TV, Majstorovic VD (2010) Novel approach to multi-response optimization for correlated responses. FME Trans 38:39–48Google Scholar
  16. 16.
    Kovach J, Cho BR (2007) Constrained robust design experiments and optimization with the consideration of uncontrollable factors. Int J Adv Manuf Technol 38(1–2):7–18Google Scholar
  17. 17.
    Tong LI, Su CT, Wang CH (1997) The optimization of multi response problems in Taguchi method. Int J Qual Reliab Manag 14(4):367–380CrossRefGoogle Scholar
  18. 18.
    Ahmida A, Isa D 2010 Capacitance and equivalent series resistance (ESR) Optimization using the Taguchi technique for Edlc’s, in 2010 International Conference on Electronic Devices, Systems and Applications,  https://doi.org/10.1109/ICEDSA.2010.5503046
  19. 19.
    Ramezani S, Jahani R, Mashhadizadeh MH, Shahbazi S, Jalilian S (2018) A novel ionic liquid/polyoxomolybdate based sensor for ultra-high sensitive monitoring of Al (III): optimization by Taguchi statistical design. J Electroanal Chem 814:7–19CrossRefGoogle Scholar
  20. 20.
    Salele Iro Z (2016) A brief review on electrode materials for supercapacitor. Int J Electrochem Sci 11:10628–10643Google Scholar
  21. 21.
    Zhang SS, Xu K, Jow TR (2006) Eis study on the formation of solid electrolyte interface in Li-ion battery. Electrochim Acta, ELSEVIER 51(8):1636–1640CrossRefGoogle Scholar
  22. 22.
    Macdonald DD (2006) Reflections on the history of electrochemical impedance spectroscopy. Electrochim Acta, ELSEVIER 51(8):1376–1388CrossRefGoogle Scholar
  23. 23.
    Chen X, Paul R, Dai L 2017 Carbon-based supercapacitors for efficient energy storage. National Science Review. Vol. 4(3):453 –489  https://doi.org/10.1093/nsr/nwx009
  24. 24.
    Brock J (2017) Electrochemical impedance spectroscopy methods, analysis and research, chemistry research and applications. Nova Science Publishers, Inc. ISBN: 978-1-53612-211-4Google Scholar
  25. 25.
    Talaie E, Bonnick P, Sun X, Pang Q, Liang X (2017) Methods and Protocols for Electrochemical Energy Storage Materials Research. Chemistry of Materials, 29(1):90–105Google Scholar
  26. 26.
    Lasia A (2014) Electrochemical impedance spectroscopy and its applications. Book, Springer-Verlag New York 2014, ISBN 978-1-4614-8932Google Scholar
  27. 27.
    Kristian BK, Vegge T, McCloskey BD, Hjelm J An electrochemical impedance spectroscopy study on the effects of the surface- and solution-based mechanisms in Li-O2 cells. J Electrochim Soc 163(9):A2065–A2071Google Scholar
  28. 28.
    Kroupa M, Offer GJ, Kosek J (2016) Modelling of supercapacitors: fFactors influencing performance. 163(10):A2475–A2487Google Scholar
  29. 29.
    Okamura K, Inoue R, Sebille T, Tomono T, Nakayama M (2011) An approach to optimize the composition of supercapacitor electrodes consisting of manganese-molybdenum mixed oxide and carbon nanotubes. J Electrochem Soc 158(6):A711–A717CrossRefGoogle Scholar
  30. 30.
    Strunz W, Schiller CA, Vogelsang J (2006) The evaluation of experimental dielectric data of barrier coatings in frequency- and time domain. Electrochim Acta, ELSEVIER 51(8):1437–1442CrossRefGoogle Scholar
  31. 31.
    Chia YY, Ridhuan AS, Isa D, Ahmida A, Khiew PS (2012) Optimization of process factors in super capacitor fabrication using the genetic algorithm to optimize Taguchi signal-to-noise ratios. Int J Eng Sci Innov Technol 1(2):135–149Google Scholar
  32. 32.
    Sinha R, Mathur S, Brighu U (2015) Aluminium removal from water after defluoridation with the electrocoagulation processes. Environ Technol, Taylor & Francis Group 36(21):2724–2731CrossRefGoogle Scholar
  33. 33.
    Sinha R, Mathur S (2015) Control of aluminium in treated water after defluoridation by electrocoagulation and modelling of adsorption isotherms. Desalin Water Treatment, Taylor & Francis 57(29):13760–13769CrossRefGoogle Scholar
  34. 34.
    Sinha R, Singh A, Mathur S (2014) Multiobjective optimization for minimum residual fluoride and specific energy in electrocoagulation process. Desalination and Water Treatment 2016 57(9):4194–4204Google Scholar
  35. 35.
    Sinha R, Mathur S (2015) Use of activated silica sol as a coagulant aid to remove aluminium from water defluoridated by electrocoagulation. Desalin Water Treat, Taylor & Francis 57(36):16790–16799Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mayank Vyas
    • 1
  • Kapil Pareek
    • 1
    Email author
  • Rupesh Rohan
    • 2
  • Pawan Kumar
    • 3
  1. 1.Centre for Energy and EnvironmentMalaviya National Institute of TechnologyJaipurIndia
  2. 2.Indian Rubber Manufacturers Research AssociationThaneIndia
  3. 3.Institute of Material Research and EngineeringSingaporeSingapore

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