Erratum to: 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 MoSe2 field-effect transistor (FET) in a NO2 gas sensor. This sensor exhibited ultra-high sensitivity (S = ca. 1,907 for NO2 at 300 ppm), real-time response, and rapid on–off switching. The high sensitivity of our MoSe2 gas sensor is attributed to changes in the gap states near the valence band induced by the NO2 gas absorbed in the MoSe2, 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 NO2 concentration is the key mechanism in a MoSe2 FET-based NO2 gas sensor. This comprehensive study, which addresses material growth, device fabrication, characterization, and device simulations, not only indicates the utility of MoSe2 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".