Kuthanazhi, Brinda, Banerjee, Debalina, Maslennikov, Dmitry, Vasylenko, Andrij
ORCID: 0000-0002-6933-0628, Scheifers, Jan P, Hawkins, Cara J, Ritchie, Daniel, Robertson, Craig M, Zanella, Marco, Manning, Troy D
ORCID: 0000-0002-7624-4306 et al (show 6 more authors)
(2026)
Discovery of two new Cu-Sn chalco-halides for potential solar absorber applications
JOURNAL OF MATERIALS CHEMISTRY A, 14 (27).
pp. 17257-17271.
ISSN 2050-7488, 2050-7496
Abstract
We explore multiple-cation chalco–halide phase fields evaluated by their synthetic accessibility using machine learning models. Exploratory synthesis guided by computational tools leads to the discovery of two new compounds; CuSn<inf>2</inf>SI<inf>3</inf> and Cu<inf>0.35</inf>Sn<inf>5.29</inf>S<inf>2</inf>I<inf>7</inf>, their structures, and electronic and optical properties are reported herein. This is the first report of a stable quaternary compound in the Cu–Sn–S–I phase field. The two new compounds show related crystal structures where Sn<inf>4</inf>S<inf>2</inf>I<inf>4</inf> layers are a common structural motif in both. These Sn<inf>4</inf>S<inf>2</inf>I<inf>4</inf> layers are connected by Cu<inf>2</inf>I<inf>2</inf> layers and disordered Cu–Sn–I layers, forming the three-dimensional structures of CuSn<inf>2</inf>SI<inf>3</inf> and Cu<inf>0.35</inf>Sn<inf>5.29</inf>S<inf>2</inf>I<inf>7</inf> respectively. Electronic band structure calculations using density functional theory show the presence of a direct band gap in CuSn<inf>2</inf>SI<inf>3</inf> and suggest anisotropic transport, in line with the layered structure of the compound. A mixture of the two compounds with ∼86% CuSn<inf>2</inf>SI<inf>3</inf>, shows a band gap in the visible region, close to 2.1 eV and a significant photo-induced charge carrier mobility of ∼1.3 cm2 V−1 s−1. This demonstrates Cu–Sn chalco–halides can form a promising phase space to explore for solar absorber materials, with further design and tuning of band gap.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3403 Macromolecular and Materials Chemistry, 34 Chemical Sciences, Machine Learning and Artificial Intelligence |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Physical Sciences Faculty of Science & Engineering > School of Physical Sciences > Chemistry |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 13 Apr 2026 08:53 |
| Last Modified: | 16 Jun 2026 05:14 |
| DOI: | 10.1039/d5ta06204g |
| Open Access URL: | https://doi.org/10.1039/D5TA06204G |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3197876 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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