Anti-solvent materials enhanced structural and optical properties on ambiently fabricated perovskite thin films

Authors

Mohammad Nur-E-Alam, Edith Cowan UniversityFollow
Mohammad Aminul Islam
Yap Boon Kar
Tiong Sieh Kiong
Halina Misran
Mayeen Uddin Khandaker
Yasser Fouad
Manzoore Elahi M. Soudagar
Erdem Cuce

Document Type

Journal Article

Publication Title

Scientific Reports

Volume

14

Issue

1

Publisher

Nature

School

School of Science

Funders

King Saud University (RSPD2025R698) / Malaysian Ministry of Higher Education (FRGS/1/2020/TK0/UM/02/33, JPT.S(BPKI)2000/016/018/015JId.4 (21) / 2022003HICOE)

Comments

Nur-E-Alam, M., Islam, M. A., Kar, Y. B., Kiong, T. S., Misran, H., Khandaker, M. U., ... & Cuce, E. (2024). Anti-solvent materials enhanced structural and optical properties on ambiently fabricated perovskite thin films. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-70344-3

Abstract

Perovskite solar cells (PSCs) hold potential for low-cost, high-efficiency solar energy, but their sensitivity to moisture limits practical application. Current fabrication requires controlled environments, limiting mass production. Researchers aim to develop stable PSCs with longer lifetimes under ambient conditions. In this research work, we investigated the stability of perovskite films and solar cells fabricated and annealed in natural air using four different anti-solvents: toluene, ethyl acetate, diethyl ether, and chlorobenzene. Films (about 300 nm thick) were deposited via single-step spin-coating and subjected to ambient air-atmosphere for up to 30 days. We monitored changes in crystallinity, electrical properties, and optics over time. Results showed a gradual degradation in the films’ crystallinity, morphology, and electro-optical properties. Notably, films made with ethyl acetate exhibited superior stability compared to other solvents. These findings contribute to advancing stable and high-performance PSCs manufactured under normal ambient conditions. In addition, we also discuss the possible machine learning (ML) approach to our future work direction to optimize the materials structures, and synthesis process parameters for future high-efficient perovskite solar cells fabrication.

DOI

10.1038/s41598-024-70344-3

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