Enhanced Sensitivity in Photovoltaic 2D MoS <sub>2</sub> /Te Heterojunction VOC Sensors
Bibliographic record
Abstract
Abstract Volatile organic compound (VOC) sensors have a broad range of applications including healthcare monitoring, product quality control, and air quality management. However, many such applications are demanding, requiring sensors with high sensitivity and selectivity. 2D materials are extensively used in many VOC sensing devices due to their large surface‐to‐volume ratio and fascinating electronic properties. These properties, along with their exceptional flexibility, low power consumption, room‐temperature operation, chemical functionalization potential, and defect engineering capabilities, make 2D materials ideal for high‐performance VOC sensing. Here, a 2D MoS 2 /Te heterojunction is reported that significantly improves the VOC detection compared to MoS 2 and Te sensors on their own. Density functional theory (DFT) analysis shows that the MoS 2 /Te heterojunction significantly enhances the adsorption energy and therefore sensing sensitivity of the sensor. The sensor response, which denotes the percentage change in the sensor's conductance upon VOC exposure, is further enhanced under photo‐illumination and zero‐bias conditions to values up to ≈7000% when exposed to butanone. The MoS 2 /Te heterojunction is therefore a promising device architecture for portable and wearable sensing applications.
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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 | 0.001 | 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".