A First-Principle Study to Investigate Electrical and Optical Properties of Tin Oxide
This paper explores the electrical and optical properties of SnO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, a prominent Transparent Conducting Oxide (TCO), using Density Functional Theory (DFT). TCOs are essential for optoelectronic applications due to their unique combination of transparency and electrical conductivity. The study focuses on critical electrical properties, including the band gap (Eg), Total Density of States (TDOS), and Partial Density of States (PDOS). SnO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> exhibits a direct band gap at the Γ point, with tin (Sn) electronic states contributing significantly to the conduction band, enhancing electrical conductivity. The optical analysis, particularly of the dielectric function in the ultraviolet (UV) region, reveals substantial electron transitions from the valence to the conduction band. The material demonstrates significant optical responses across the visible, vacuum ultraviolet (VUV), and UV regions. These findings underscore SnO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> potential in photonic and optoelectronic applications, providing valuable insights for optimizing TCO-based devices and advancing transparent conductive material technology.
Authors
- Bibek Chettri (ORCID: https://orcid.org/0000-0002-4338-8194)
- Sanat Kr. Das (ORCID: https://orcid.org/0000-0002-2325-0358)
- Bikash Sharma (ORCID: https://orcid.org/0000-0003-2013-9686)
- Ashok Rao
- Prasanna Karki (ORCID: https://orcid.org/0000-0003-0481-8611)
- Roshan Pradhan (ORCID: https://orcid.org/0000-0001-6225-0767)
- Pronita Chettri (ORCID: https://orcid.org/0000-0003-2233-0951)
- Nancy Sharma
Institutions
- Manipal Academy of Higher Education (IN)
- Sikkim Manipal University (IN)
Publication Details
- Published
- 2024-11-30
- DOI
- https://doi.org/10.1109/edkcon62339.2024.10870789
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00