Skip to main content
Log in

Synthesis and Characterization of Conducting Polyaniline Nanostructured Thin Films for Solar Cell Applications

  • Advanced Coating and Thin Film Materials for Energy, Aerospace and Biological Applications
  • Published:
JOM Aims and scope Submit manuscript

Abstract

Optical-quality transparent, conducting polyaniline (PANI) thin films are suitable candidates for efficient counter electrodes for high-performance solar cells. In the first part of this work, the synthesis of highly uniform and homogenous nanostructured PANI films is reported. The film properties were assessed via scanning electron microscopy, atomic force microscopy, optical profilometry, spectrophotometry, and conductimetry. Simultaneous modeling, optimization and physical characterization of the PANI nanostructured films have not received much attention in the literature. Hence, in the second part, a multi-objective optimization approach with three objectives, namely minimum film thickness, maximum transparency, and maximum conductivity, was performed based on artificial neural network models with a novel k-fold cross-validation technique. The developed models can accurately predict the film characteristics in a wide range of design variables with most residuals remarkably less than 1.0%. Furthermore, after optimization, conductivity was increased three-fold (~ 2.2 × 10−1 S/cm) at a good level of transparency (~ 55%), which suit solar cell applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. J. Wei, F. Huang, S. Wang, L. Zhou, Y. Xin, P. Jin, Z. Cai, Z. Yin, Q. Pang, and J.Z. Zhang, Mater. Res. Bull. 106, 35 (2018).

    Google Scholar 

  2. A. Bahramian and D. Vashaee, Sol. Energy Mater. Sol. Cells 143, 284 (2015).

    Google Scholar 

  3. J. Wu, Y. Li, Q. Tang, G. Yue, J. Lin, M. Huang, and L. Meng, Sci. Rep. 4, 4028 (2014).

    Google Scholar 

  4. J. Yuan, J. Gu, G. Shi, J. Sun, H.-Q. Wang, and W. Ma, Sci. Rep. 6, 26459 (2016).

    Google Scholar 

  5. M. Asyraf, M. Anwar, L.M. Sheng, and M.K. Danquah, JOM 69, 2515 (2017).

    Google Scholar 

  6. S. Bhadra, D. Khastgir, N.K. Singha, and J.H. Lee, Prog. Polym. Sci. 34, 783 (2009).

    Google Scholar 

  7. K.M. Molapo, P.M. Ndangili, R.F. Ajayi, G. Mbambisa, S.M. Mailu, N. Njomo, M. Masikini, P. Baker, and E.I. Iwuoha, Int. J. Electrochem. Sci. 7, 11859 (2012).

    Google Scholar 

  8. P. Kar and A. Choudhury, Adv. Polym. Technol. 32, E760 (2013).

    Google Scholar 

  9. J.C. Yu, J.A. Hong, E.D. Jung, D.B. Kim, S.-M. Baek, S. Lee, S. Cho, S.S. Park, K.J. Choi, and M.H. Song, Sci. Rep. 8, 1070 (2018).

    Google Scholar 

  10. J.N. Pereira, P. Vieira, A. Ferreira, A.J. Paleo, J.G. Rocha, and S. Lanceros-Méndez, J. Polym. Res. 19, 9815 (2012).

    Google Scholar 

  11. G. Wu, P. Tan, D. Wang, Z. Li, L. Peng, Y. Hu, C. Wang, W. Zhu, S. Chen, and W. Chen, Sci. Rep. 7, 43676 (2017).

    Google Scholar 

  12. X.P. Chen, Q.H. Liang, J.K. Jiang, C.K.Y. Wong, S.Y.Y. Leung, H.Y. Ye, D.G. Yang, and T.L. Ren, Sci. Rep. 6, 20621 (2016).

    Google Scholar 

  13. J.C. Wang, R. Bruttini, and A.I. Liapis, Ind. Eng. Chem. Res. 55, 6649 (2016).

    Google Scholar 

  14. J.B. de Lima Filho and Á.A. Hidalgo, Synth. Met. 223, 80 (2017).

    Google Scholar 

  15. S.R. Bhattacharyya, R.N. Gayen, R. Paul, and A.K. Pal, Thin Solid Films 517, 5530 (2009).

    Google Scholar 

  16. J. Schmidhuber, Neural Netw. 61, 85 (2015).

    Google Scholar 

  17. R. Simon, Resampling strategies for model assessment and selection. in Fundamentals of Data Mining in Genomics and Proteomics, (Springer, New York, 2007), pp. 173–186.

  18. Z. Wang and G.P. Rangaiah, Ind. Eng. Chem. Res. 56, 560 (2017).

    Google Scholar 

  19. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, IEEE Trans. Evol. Comput. 6, 182 (2002).

    Google Scholar 

  20. A. Bahramian, Thin Solid Films 592, 39 (2015).

    Google Scholar 

  21. A. Bahramian, Surf. Interface Anal. 47, 1 (2015).

    Google Scholar 

  22. U. Ozdemir, B. Ozbay, S. Veli, and S. Zor, Chem. Eng. J. 178, 183 (2011).

    Google Scholar 

  23. G.H. Shafabakhsh, O.J. Ani, and M. Talebsafa, Constr. Build. Mater. 85, 136 (2015).

    Google Scholar 

  24. M. Tanzifi, S.H. Hosseini, A.D. Kiadehi, M. Olazar, K. Karimipour, R. Rezaiemehr, and I. Ali, J. Mol. Liq. 244, 189 (2017).

    Google Scholar 

  25. X. X. Wu and J. G. Liu, A new early stopping algorithm for improving neural network generalization. in: Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation, (2009) pp. 15–18

  26. Mathworks, Genetic Algorithm Options, MATLAB Documentation Center 2014, (2014), No. accessed 3 June 2014.

  27. Mathworks, Global Optimization Toolbox 3.2.5 User Guide, (2014).

  28. X. Tang and X. Yan, J. Sol-Gel. Sci. Technol. 81, 378 (2017).

    Google Scholar 

  29. M. Guglielmi, P. Colombo, F. Peron, and L.M. Degli Esposti, J. Mater. Sci. 27, 5052 (1992).

    Google Scholar 

  30. W. Zhao, L. Ye, S. Zhang, B. Fan, M. Sun, and J. Hou, Sci. Rep. 4, 6570 (2014).

    Google Scholar 

  31. D.S. Fryer, R.D. Peters, E.J. Kim, J.E. Tomaszewski, J.J. de Pablo, P.F. Nealey, C.C. White, and W.L. Wu, Macromolecules 34, 5627 (2001).

    Google Scholar 

  32. A. Bahramian, J. Appl. Polym. Sci. 132, 41858 (2015).

    Google Scholar 

  33. J.C. Manifacier, J. Gasiot, and J.P. Fillard, J. Phys. E 9, 1002 (1976).

    Google Scholar 

  34. R. Swanepoel, J. Phys. E: Sci. Instrum. 16, 1215 (1983).

    Google Scholar 

  35. M.S. Ghamsari and A.R. Bahramian, High transparent sol–gel derived nanostructured TiO2 thin film. Mater. Lett. 62, 361 (2008).

    Google Scholar 

  36. J. Tauc and A. Menth, J. Non-Cryst. Solids 8–10, 569 (1972).

    Google Scholar 

  37. F. Kanwal, A. Batool, M. Adnan, and S. Naseem, Mater. Res Innov. 19, S8-354 (2015).

    Google Scholar 

  38. Y.J. Cheng, S.H. Yang, and C.S. Hsu, Chem. Rev. 109, 5868 (2009).

    Google Scholar 

  39. A.G. Baker, ARO Sci. J. Koya Univ. 7, 47 (2019).

    Google Scholar 

  40. E.M. Scherr, A.G. MacDiarmid, S.K. Manohar, J.G. Masters, Y. Sun, X. Tang, M.A. Druy, P.J. Glatkowski, V.B. Cajipe, J.E. Fischer, K.R. Cromack, M.E. Jozefowicz, J.M. Ginder, R.P. McCall, and A.J. Epstein, Synth. Met. 41, 735 (1991).

    Google Scholar 

  41. Q. Qin and R. Zhang, Electrochim. Acta 89, 726 (2013).

    Google Scholar 

  42. Mathworks, Neural Network Toolbox 8.2 User Guide (2014).

  43. Q. Tai, B. Chen, F. Guo, S. Xu, H. Hu, B. Sebo, and X.-Z. Zhao, ACS Nano 5, 3795 (2011).

    Google Scholar 

  44. H. Bejbouji, L. Vignau, J.L. Miane, M.-T. Dang, E.M. Oualim, M. Harmouchi, and A. Mouhsen, Sol. Energy Mater. Sol. Cells 94, 176 (2010).

    Google Scholar 

  45. A.A.B. Baloch, S.P. Aly, M.I. Hossain, F. El-Mellouhi, N. Tabet, and F.H. Alharbi, Sci. Rep. 7, 11984 (2017).

    Google Scholar 

  46. M. Sendova-Vassileva, H. Dikov, G. Popkirov, E. Lazarova, V. Gancheva, G. Grancharov, D. Tsocheva, P. Mokreva, and P. Vitanov, J. Phys: Conf. Ser. 514, 012018 (2014).

    Google Scholar 

Download references

Acknowledgement

The authors would like to thank the Hamedan University of Technology for financially supporting this work through Grant No. 18-96-1-3.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bijan Medi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2422 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Medi, B., Bahramian, A. & Nazari, V. Synthesis and Characterization of Conducting Polyaniline Nanostructured Thin Films for Solar Cell Applications. JOM 73, 504–514 (2021). https://doi.org/10.1007/s11837-020-04361-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11837-020-04361-8

Navigation