JOURNAL ARTICLE
Study of electronic, optical, thermodynamic, and thermoelectric properties of GeC bulk and monolayer structure.
Published In: Modern Physics Letters B, 2024, v. 38, n. 24. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Goudarzi, M.; Karamdel, J.; Hassanabadi, H.; Zorriasatein, Sh. 3 of 3
Abstract
Using density functional theory and Boltzmann equations, this study calculates and compares the electronic, optical, thermoelectric, and thermodynamic properties of bulk and single-layer germanium carbide structures. It has been shown that germanium carbide in the bulk structure has an indirect energy band gap of 1.61 eV. In a single layer structure, it is a metal. Thermodynamic properties including specific heat at constant volume have been investigated using the quasi-harmonic approximation (QHA) package in QE. According to the studies conducted on optical properties, this material shows good reflection in the visible region. In addition, this structure has high electrical conductivity and low thermal conductivity, which is essential for thermoelectric materials. Thermoelectric calculations using semi-classical Boltzmann transfer theory show that the performance of two-dimensional compounds with graphene gives good thermoelectric results. At 100 K, the highest figure of merit (ZT) for GeC is 1.00. Our findings evaluate the GeC compound as a suitable thermoelectric material in the temperature gradient from 100 to 900 K. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Modern Physics Letters B. 2024/08, Vol. 38, Issue 24, p1
- Document Type:Article
- Subject Area:Physics
- Publication Date:2024
- ISSN:0217-9849
- DOI:10.1142/S0217984924502154
- Accession Number:177355956
- Copyright Statement:Copyright of Modern Physics Letters B 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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