WS<sub>2</sub> and WS<sub>2</sub>-ZnO Chemiresistive Gas Sensors: The Role of Analyte Charge Asymmetry and Molecular Size
Bibliographic record
Abstract
We investigate the interaction of various analytes (toluene, acetone, ethanol, and water) possessing different structures, bonding, and molecular sizes with a laser-exfoliated WS 2 sensing material in a chemiresistive sensor. The sensor showed a clear response to all analytes, which was significantly enhanced by modifying the WS 2 surface. This was achieved by creating WS 2 -ZnO heterojunctions via the deposition of ZnO nanoparticles on the WS 2 surface with a high-throughput, atmospheric-pressure spatial atomic layer deposition system. Water and ethanol produced a much higher response compared to acetone and toluene for both the WS 2 and WS 2 -ZnO sensing mediums. We resolved that the charge-asymmetry points in analyte molecules play a key role in determining the sensor response. High charge-asymmetry points correspond to highly polar bonds (HPBs) in a neutral molecule that have a high probability of interaction with the sensing medium. Our results indicate that the polarity of the HPBs primarily dictates the interaction between the analyte and sensing medium and consequently controls the response of the sensor. Moreover, the size of the analyte molecule was found to affect the sensing response; if two molecules have the same HPBs and are exposed to the same sensing medium, the smaller molecule is likely to produce a higher and faster response. Our study provides a comprehensive picture of analyte–sensor interactions that can help in advancing semiconductor gas sensors, including those based on two-dimensional materials.
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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) | 0.001 | 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 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".