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
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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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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 | 0.001 |
| 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 it