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Machine Learning-Assisted Fabrication of PCBM-Perovskite Solar Cells with Nanopatterned TiO2 Layer
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  • Siti Norhasanah Sanimu,
  • Hwa-Young Yang,
  • Jeevan Kandel,
  • Ye-Chong Moon,
  • Gangasagar Sharma Gaudel,
  • Seung-Ju Yu,
  • Yong Ju Kim,
  • Sejung Kim,
  • Bong-Hyun Jun,
  • Won-Yeop Rho
Siti Norhasanah Sanimu
Jeonbuk National University
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Hwa-Young Yang
Ulsan National Institute of Science and Technology
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Jeevan Kandel
Jeonbuk National University
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Ye-Chong Moon
Jeonbuk National University
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Gangasagar Sharma Gaudel
Jeonbuk National University
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Seung-Ju Yu
Jeonbuk National University
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Yong Ju Kim
Jeonbuk National University
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Sejung Kim
Jeonbuk National University
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Bong-Hyun Jun
Konkuk University
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Won-Yeop Rho
Jeonbuk National University

Corresponding Author:[email protected]

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Abstract

To unlock the full potential of PSCs, machine learning (ML) was implemented in this research to predict the best combination of mesoporous-titanium dioxide (mp-TiO2) and weight percentage (wt%) of phenyl-C61-butyric acid methyl ester (PCBM), along with the current density (Jsc), open-circuit voltage (Voc), fill factor (ff) and energy conversion efficiency (ECE). Then, the combination that yielded the highest predicted ECE was selected as a reference to fabricate PCBM-PSCs with nanopatterned TiO2 layer. Subsequently, the PCBM-PSCs with nanopatterned TiO2 layers were fabricated and characterized to further understand the dual effects of nanopatterning depth and wt% of PCBM on PSCs. Experimentally, the highest ECE of 17.336% is achieved at 127 nm nanopatterning depth and 0.10 wt% of PCBM, where the Jsc, Voc and ff are 22.877 mA/cm2, 0.963 V and 0.787, respectively. The measured Jsc, Voc, ff and ECE values show consistencies with the ML prediction. Hence, these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs, but also highlighted that nanopatterning depth has a significant impact on Jsc, and the incorporation of PCBM on perovskite layer influenced the Voc and ff, which further boosted the performance of PSCs.
25 Apr 2023Submitted to Energy & Environmental Materials
30 Apr 2023Submission Checks Completed
30 Apr 2023Assigned to Editor
07 May 2023Review(s) Completed, Editorial Evaluation Pending
08 May 2023Reviewer(s) Assigned
25 May 2023Editorial Decision: Revise Major
14 Jul 20231st Revision Received
14 Jul 2023Submission Checks Completed
14 Jul 2023Assigned to Editor
14 Jul 2023Review(s) Completed, Editorial Evaluation Pending
16 Jul 2023Reviewer(s) Assigned
23 Jul 2023Editorial Decision: Revise Minor
08 Aug 20232nd Revision Received
09 Aug 2023Assigned to Editor
09 Aug 2023Submission Checks Completed
09 Aug 2023Review(s) Completed, Editorial Evaluation Pending
10 Aug 2023Editorial Decision: Accept