Voltage-controlled NiO/ZnO p–n heterojunction diode: a new approach towards selective VOC sensing
Bibliographic record
Abstract
Metal oxide resistive gas sensors suffer from poor selectivity that restricts their practical applicability. Conventional sensor arrays are used to improve selectivity which increased the system complexity. Here, we have proposed a novel NiO/ZnO-based p-n junction single-diode device for selective sensing of several volatile organic compounds (VOCs) simultaneously by tuning bias voltage. The operating voltage was varied between 3 and 5 volts to achieve selective sensing of 2-propanol (19.1 times for 95 ppm with response and recovery times of 70 s and 55 s respectively' at 3 volts), toluene (20.1 times for 95 ppm with response and recovery times of 100 s and 60 s respectively, at 4 volts), and formaldehyde (11.2 times for 95 ppm with response and recovery times of 88 s and 54 s respectively, at 5 volts). A probable mechanism of the tunable selectivity with operating bias voltage due to increase in surface carriers with increasing voltage was hence put forth. Thus, this device may play an important role to develop future selective multiple VOC sensor thereby replacing standard sensor arrays.
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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 | 0.001 | 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".