Enhancement in hazardous gas detection capabilities of MoS2 monolayer-based devices through defect engineering and photonic activation
Bibliographic record
Abstract
The gas-sensing potential of transition metal dichalcogenides (TMDs) drew attention owing to their high surface sensitivity and tunable optoelectronic features. Among the TMDs, monolayer MoS 2 stands out as a promising material for advanced gas sensors. However, TMDs-based gas sensors still require considerable improvement in room temperature sensitivity, response times, and stability, which may be achievable through alterations in kinetics. Herein, we report a highly sensitive NH 3 gas sensor based on monolayer MoS 2 , whose sensing performance is greatly enhanced by defect engineering and photonic activation. Intestinally induced sulfur vacancies create chemically active adsorption sites, increasing adsorption energy and enhancing charge transfer between NH 3 molecules and MoS 2 . On the other hand, visible-light illumination stimulates photoresponsivity by generating electron-hole pairs to speed up desorption and recovery time. With these combined stimuli, very large modulations to the electronic band structure occur, thus enhancing the gas-surface interaction dynamics and hence sensing performance. Thus, this study highlights the potential of defect-engineered and photonic-activated monolayer MoS 2 as a strong candidate for advanced gas detection and presents a scalable pathway for next-generation sensor development, meeting the demands of environmental and industrial monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".