Enhancing NO<sub>2</sub> Gas Sensing: The Dual Impact of UV and Thermal Activation on Vertically Aligned Nb-MoS<sub>2</sub> for Superior Response and Selectivity
Bibliographic record
Abstract
Nitrogen dioxide (NO 2 ) is considered to be a highly hazardous gas found in combustion engine exhaust, which causes several diseases at a young age. To detect NO 2 at room temperature (RT), two-dimensional transition metal dichalcogenides play an essential role because of their greater surface-to-volume ratio. However, their higher limit of detection (LOD), slow response, and incomplete recovery kinetics hinder their use in efficient gas sensors. To mitigate these issues, we fabricate a facile and robust niobium (Nb)-doped molybdenum disulfide (MoS 2 ) sensor using low-pressure chemical vapor deposition on a SiO 2 /Si substrate. Doping is confirmed through various characterization techniques. As compared to pristine MoS 2, three batches of sensors are prepared with different weight percentages of Nb (8, 16, and 24%). Out of these, the 16% Nb-MoS 2 sensor gives a greatly enhanced relative response of ∼30% for 500 ppb NO 2 at 100 °C with an LOD of 489 ppt. Also, the sensor gives an ultrahigh response of ∼39% (18%) for 50 ppm (500 ppb) NO 2 under 0.4 mW/cm 2 intensity of UV light and exhibits a lower LOD of 117 ppt at RT. In addition, the 16% Nb-MoS 2 sensor shows impressive selectivity toward NO 2 against a range of reducing and oxidizing gases, along with exceptional long-term durability and stability. Based on density functional theory calculations, a comprehensive gas sensing mechanism is proposed. The calculations focus on identifying the favorable sites for NO 2 adsorption on 16% Nb-MoS 2 nanoflakes. This study offers a compelling and practical approach to boosting the efficiency of Nb-MoS 2 -based NO 2 gas sensors.
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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".