Effect of Au nanoparticles on mitigating the negative impacts of humidity on ZnO gas sensors to detect triethylamine at room temperature
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Bibliographic record
Abstract
The impact of humidity on the efficiency of gas sensors has become highlighted in the realm of gas detection. Due to the complex relationship between humidity and gas sensor performance, the development of gas sensors has recently focused on minimizing humidity-related interference. This research aims to address humidity-related challenges in zinc oxide (ZnO) gas sensors designed to detect triethylamine. The ZnO nanostructures (NSs) were synthesized using thermal decomposition methods at varying temperatures (380 °C, 480 °C, and 580 °C) and annealing times (3 h, 7 h, 12 h, and 21 h). X-ray diffraction (XRD) confirmed the formation of a wurtzite hexagonal close-packed structure in ZnO NSs. Scanning electron microscopy (SEM) images provided insights into the morphologies of ZnO NSs at different annealing temperatures, while energy dispersive spectroscopy (EDS) demonstrated the elemental distribution. Subsequently, gold (Au) nanoparticles were uniformly sputtered onto ZnO sensors with thickness variations (0.1 nm, 0.6 nm, 1 nm, 5 nm, and 10 nm). XPS was employed to analyse the elemental composition and oxygen vacancies of the synthesized sensing materials. The effectiveness of 0.6 nm-thick Au nanoparticles in mitigating humidity effects was observed in ZnO sensors synthesized at 380 °C. The results indicated that ZnO sensors coated with 0.6 nm-thick Au nanoparticles exhibited highly stable responses to ethanol and triethylamine at different humidity levels from 50 % to 90 %. Notably, these sensors demonstrated promising selectivity towards triethylamine (with a response of 17.57) compared to various gas targets at room temperature. The sensor exhibited rapid response and recovery times of 9.8 s and 4.4 s, respectively, toward triethylamine with excellent stability in variable humid environments. The sensor maintained a consistent response over 24 days, demonstrating good stability at high humidity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it