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A DFT study of the adsorption behavior and sensing properties of CO gas on monolayer MoSe2 in CO2-rich environment.

Authors :
Vinturaj, V. P.
Yadav, Ashish Kumar
Singh, Rohit
Garg, Vivek
Bhardwaj, Ritesh
Ajith, K. M.
Pandey, Sushil Kumar
Source :
Journal of Molecular Modeling. Aug2024, Vol. 30 Issue 8, p1-14. 14p.
Publication Year :
2024

Abstract

Context: Carbon monoxide, also known as the "silent killer," is a colorless, odorless, tasteless, and non-irritable gas that, when inhaled, enters the bloodstream and lungs, binds with the hemoglobin, and blocks oxygen from reaching tissues and cells. In this work, the monolayer MoSe2-based CO gas sensors were designed using density functional theory calculation with several dopants including Al, Au, Pd, Ni, Cu, and P. Here, Cu and P were found to be the best dopants, with adsorption energies of −0.67 eV (Cu) and −0.54 eV (P) and recovery times of 1.66 s and 13.8 ms respectively. Cu conductivity for CO adsorption was found to be 2.74 times that of CO2 adsorption in the 1.0–2.26 eV range. P displayed the highest selectivity, followed by Pd and Ni. The dopants, Pd and Ni, were found suitable for building CO gas scavengers due to their high recovery times of 9.76 × 1020 s and 2.47 × 1011 s. Similarly, the adsorption of CO2 on doped monolayer MoSe2 was also investigated. In this study, it is found that monolayer MoSe2 could be employed to create high-performance CO sensors in a CO2-rich environment. Method: The electrical characteristics of all doped MoSe2 monolayers are obtained using a DFT calculation with the PBE-GGA method from the Quantum ESPRESSO package. The self-consistent field (SCF) computations were performed using a 7 × 7 × 1 k-point grid and a norm-conserving pseudo potential (NCPP) file. To determine electrical conductivity, the semi-classical version of Boltzmann transport theory, implemented in the Boltz Trap code, was used. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16102940
Volume :
30
Issue :
8
Database :
Academic Search Index
Journal :
Journal of Molecular Modeling
Publication Type :
Academic Journal
Accession number :
179067348
Full Text :
https://doi.org/10.1007/s00894-024-06014-y