A highly sensitive chemical gas detecting transistor based on highly crystalline CVD-grown MoSe2 films
Bibliographic record
Abstract
Layered semiconductors with atomic thicknesses are becoming increasingly important as active elements in high-performance electronic devices owing to their high carrier mobilities, large surface-to-volume ratios, and rapid electrical responses to their surrounding environments. Here, we report the first implementation of a highly sensitive chemical-vapor-deposition-grown multilayer MoSe 2 field-effect transistor (FET) in a NO 2 gas sensor. This sensor exhibited ultra-high sensitivity ( S = ca. 1,907 for NO 2 at 300 ppm), real-time response, and rapid on–off switching. The high sensitivity of our MoSe 2 gas sensor is attributed to changes in the gap states near the valence band induced by the NO 2 gas absorbed in the MoSe 2 , which leads to a significant increase in hole current in the off-state regime. Device modeling and quantum transport simulations revealed that the variation of gap states with NO 2 concentration is the key mechanism in a MoSe 2 FET-based NO 2 gas sensor. This comprehensive study, which addresses material growth, device fabrication, characterization, and device simulations, not only indicates the utility of MoSe 2 FETs for high-performance chemical sensors, but also establishes a fundamental understanding of how surface chemistry influences carrier transport in layered semiconductor devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".