Enhanced Gas Sensing Performance of Photo-Activated, Pt-Decorated, Single-Crystal ZnO Nanowires
Bibliographic record
Abstract
ZnO nanoparticles and nanowires decorated with platinum nanoparticles in different loading concentrations were prepared to detect low concentrations of NO 2 under 365-nm ultraviolet-light emitting diode (UV-LED) irradiation at room temperature. Solution precipitation, self-assembly crystallization, and photo-deposition techniques were used to synthesize the sensing material. The synthesized sensors at different stages of development were characterized by XRD, HRTEM and FE-SEM analyses. Although the sensing response of the pristine ZnO nanoparticles was 0.41 for NO 2 detection, the response improved significantly to 1.5 using ZnO nanowires in the identical photo-activation settings of 365 nm UV wavelength and 25 mW/cm 2 irradiance. The decoration of the surface of the ZnO nanowires with Pt nanoparticles further enhanced the sensing performance, whereas the 0.1wt% Pt-decorated ZnO nanowire sensor exhibited a response of 4.33 with a 140-s response time, which is more than one order of magnitude higher and 50s faster than the response generated by the ZnO nanoparticles. This improved performance is attributed to the role of Pt active sites in promoting NO 2 adsorption on the ZnO nanowires' surface as well as enhancing the layer electron utilization.
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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".