MoS2–MoSe2 nanoflakes-based n–n heterojunction toward highly sensitive and selective room-temperature NO2 gas sensor

The present research reports the controlled fabrication of MoS2–MoSe2 hybrid nanostructures utilizing DC magnetron co-sputtering technique for highly selective and sensitive room-temperature (RT) NO2 detection. Among the optimized sensing thin films, the MoS2–MoSe2 sensor with a precisely engineered thickness of ∼350 nm demonstrates an exceptional sensor response of ∼61.3% toward 0.7 ppm NO2 at RT, coupled with a rapid response/recovery time of ∼77/87 s. This superior sensitivity of the MoS2–MoSe2 sensor is attributed to the abundant adsorption sites, the synergistic effects of MoS2–MoSe2 heterostructure, efficient charge transfer dynamics, and the unique n–n heterojunction band alignment, which collectively enhance carrier modulation and gas interaction kinetics. Additionally, the sensor delivers remarkable repeatability over 21 consecutive cycles, long-term operational stability exceeding 80 days, and outstanding selectivity toward NO2 against potentially interfering gases, underscoring its reliability for real-world applications. Thus, the MoS2–MoSe2 nanocomposite sensor emerges as a promising candidate for next-generation NO2 gas sensing, offering a robust platform for real-time air quality monitoring and environmental safety applications.

Authors

Institutions

Publication Details

Journal
Applied Physics Letters
Published
2025-06-16
DOI
https://doi.org/10.1063/5.0274966
Citations
6
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
Field-Weighted Citation Impact
4.38

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MoS2–MoSe2 nanoflakes-based n–n heterojunction toward highly sensitive and selective room-temperature NO2 gas sensor

Akshay Tomar, V. K. Malik, Sheetal Issar, Sonika Kodan et al.
6 citations
Applied Physics Letters
Gas Sensing Nanomaterials and Sensors
4.38
article

MoS2–MoSe2 nanoflakes-based n–n heterojunction toward highly sensitive and selective room-temperature NO2 gas sensor

Akshay Tomar, V. K. Malik, Sheetal Issar, Sonika Kodan, Ramesh Chandra
article en
6 citations

Abstract

The present research reports the controlled fabrication of MoS2–MoSe2 hybrid nanostructures utilizing DC magnetron co-sputtering technique for highly selective and sensitive room-temperature (RT) NO2 detection. Among the optimized sensing thin films, the MoS2–MoSe2 sensor with a precisely engineered thickness of ∼350 nm demonstrates an exceptional sensor response of ∼61.3% toward 0.7 ppm NO2 at RT, coupled with a rapid response/recovery time of ∼77/87 s. This superior sensitivity of the MoS2–MoSe2 sensor is attributed to the abundant adsorption sites, the synergistic effects of MoS2–MoSe2 heterostructure, efficient charge transfer dynamics, and the unique n–n heterojunction band alignment, which collectively enhance carrier modulation and gas interaction kinetics. Additionally, the sensor delivers remarkable repeatability over 21 consecutive cycles, long-term operational stability exceeding 80 days, and outstanding selectivity toward NO2 against potentially interfering gases, underscoring its reliability for real-world applications. Thus, the MoS2–MoSe2 nanocomposite sensor emerges as a promising candidate for next-generation NO2 gas sensing, offering a robust platform for real-time air quality monitoring and environmental safety applications.

Applied Physics LettersVol. 126(24)
Indian Institute of Technology Roorkee (IN)
Defence Research and Development Organisation
Openalex Percentile: Top 5%
Gas Sensing Nanomaterials and Sensors
4.38
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.