Density Functional\nTheory Study of Two-Dimensional\nSnS<sub>2</sub> Doping for Detection of Hydrogen Sulfide

The efficient and rapid detection of toxic and combustible\nH<sub>2</sub>S released during industrial processes is extremely crucial.\nHowever, two-dimensional (2D) SnS<sub>2</sub> shows a weak interaction\nwith H<sub>2</sub>S, leading to difficult detection. In this work,\nwe use density functional theory (DFT) calculations to modify the\nSnS<sub>2</sub> monolayer by N, P, Ge, and Se doping and investigate\nthe adsorption properties and gas-sensing mechanism of each doped\nSnS<sub>2</sub>. By analyzing the adsorption energy, charge density\ndifference, band structure, and recovery time, we suggest that Ge\nand Se doping is detrimental to the detection of H<sub>2</sub>S. Significantly,\nN and P doping can efficiently strengthen the interaction between\nSnS<sub>2</sub> and H<sub>2</sub>S and simultaneously maintain the\nphysisorption with the adsorption energy of −0.60 eV and −0.64\neV, leading to a suitable recovery time (5.64 × 10<sup>–2</sup> s and 1.20 × 10<sup>–2</sup> s). The H<sub>2</sub>S@N\nand P-SnS<sub>2</sub> systems exhibit significant band gap decreases\n(1.51 and 0.84 eV). Moreover, combined with nonequilibrium Green’s\nfunction (NEGF) method, the simulation of current–voltage characteristics\nfurther reveals their high sensitivity, reaching nearly 100%. Hence,\nthe DFT and NEGF calculations in this work provide an efficient doping\nstrategy to make 2D SnS<sub>2</sub> a highly reusable and sensitive\ngas sensor for the detection of H<sub>2</sub>S.

Publication Details

Journal
Figshare
Published
2024-10-16
DOI
https://doi.org/10.1021/acsanm.4c04339.s001
Primary Topic
2D Materials and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Density Functional\nTheory Study of Two-Dimensional\nSnS2 Doping for Detection of Hydrogen Sulfide

Figshare
2D Materials and Applications
article

Density Functional\nTheory Study of Two-Dimensional\nSnS2 Doping for Detection of Hydrogen Sulfide

article en

Abstract

The efficient and rapid detection of toxic and combustible\nH<sub>2</sub>S released during industrial processes is extremely crucial.\nHowever, two-dimensional (2D) SnS<sub>2</sub> shows a weak interaction\nwith H<sub>2</sub>S, leading to difficult detection. In this work,\nwe use density functional theory (DFT) calculations to modify the\nSnS<sub>2</sub> monolayer by N, P, Ge, and Se doping and investigate\nthe adsorption properties and gas-sensing mechanism of each doped\nSnS<sub>2</sub>. By analyzing the adsorption energy, charge density\ndifference, band structure, and recovery time, we suggest that Ge\nand Se doping is detrimental to the detection of H<sub>2</sub>S. Significantly,\nN and P doping can efficiently strengthen the interaction between\nSnS<sub>2</sub> and H<sub>2</sub>S and simultaneously maintain the\nphysisorption with the adsorption energy of −0.60 eV and −0.64\neV, leading to a suitable recovery time (5.64 × 10<sup>–2</sup> s and 1.20 × 10<sup>–2</sup> s). The H<sub>2</sub>S@N\nand P-SnS<sub>2</sub> systems exhibit significant band gap decreases\n(1.51 and 0.84 eV). Moreover, combined with nonequilibrium Green’s\nfunction (NEGF) method, the simulation of current–voltage characteristics\nfurther reveals their high sensitivity, reaching nearly 100%. Hence,\nthe DFT and NEGF calculations in this work provide an efficient doping\nstrategy to make 2D SnS<sub>2</sub> a highly reusable and sensitive\ngas sensor for the detection of H<sub>2</sub>S.

Figshare
Openalex Percentile: Top 76%
2D Materials and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.