JOURNAL ARTICLE

2D Janus monolayers AsXBr (X = S, Se and Te) for enhanced solar cell efficiency.

  • Published In: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics, 2025, v. 39, n. 26/27. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chitara, Rajesh; Kolavada, Himalay; Gupta, Sanjeev K.; Gajjar, P. N. 3 of 3

Abstract

This study explores the potential of Janus AsXBr (X  =  S, Se, Te) monolayers as efficient materials for solar cell applications using first-principles calculations. The influence of compositional variation on key photovoltaic properties, including electronic structure and optical absorption, is systematically analyzed. The results reveal that AsXBr monolayers exhibit optimal bandgaps, strong light absorption and suitable band alignments for efficient charge carrier separation. The calculated Shockley–Queisser efficiencies are 31.58% for AsSBr, 32.78% for AsSeBr and 28.04% for AsTeBr, demonstrating their potential for high-efficiency solar energy conversion. These findings highlight AsXBr monolayers as promising candidates for next-generation photovoltaic technologies, offering a balance of efficiency, stability and strong absorption in the visible spectrum. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 2025/10, Vol. 39, Issue 26/27, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2025
  • ISSN:0217-9792
  • DOI:10.1142/S0217979225400648
  • Accession Number:188803451
  • Copyright Statement:Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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