Affiliations 

  • 1 Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, 43000, Malaysia
  • 2 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia. [email protected]
  • 3 Applied Physics and Radiation Technologies Group, CCDCU, School of Engineering and Technology, Sunway University, 47500, Bandar Sunway, Selangor, Malaysia
  • 4 Department of Applied Mechanical Engineering, College of Applied Engineering, Muzahimiyah Branch, King Saud University, P.O. Box 800, 11421, Riyadh, Saudi Arabia
  • 5 School of Engineering, Lishui University, Lishui, 323000, Zhejiang, China. [email protected]
  • 6 College of Engineering, Department of Mechanical Engineering, Birmingham City University, Birmingham, B4 7XG, UK. [email protected]
Sci Rep, 2024 Aug 28;14(1):19995.
PMID: 39198679 DOI: 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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.