Environmental Gas and Light Sensing Using ZnO Nanowires
Bibliographic record
Abstract
In this paper, vertical arrays of zinc oxide (ZnO) nanowires (NWs) are synthesized by a chemical vapor deposition system and then deposited on patterned electrodes using dielectrophoresis. The NW devices illustrate four orders of magnitude increase in conductivity when exposed to ultraviolet (UV) irradiation of 1220 μW/cm2. UV response has a fast component, due to electron-hole generation, as well as a slower component, attributed to the release of oxygen species from the surface of NW. Moreover, due to the increased electron density in the presence of UV, the type of oxygen species on the surface of ZnO changes to more reactive negative ions. In addition, when the pressure is decreased to 0.05 mBar, the conductivity of the NWs increases ~3.5 times due to the removal of oxygen molecules from the surface. For the first time, UV irradiation is used to improve the carbon monoxide (CO) sensing properties of ZnO NWs. When exposed to 250 μW/cm2UV irradiation, not only the sensitivity increases more than 75%, but also a repeatable and recoverable response is obtained, which is due to formation of more reactive oxygen ions. For the same reason, when the temperature is elevated, higher sensitivity to CO is achieved. The devices demonstrate exponential sensitivities of more than four decades when the relative humidity (RH) increases to 86% at room temperature.
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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) | 0.000 | 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".