The sensitivity enhancement of TiO<sub>2</sub>-based VOCs sensor decorated by gold at room temperature
Bibliographic record
Abstract
Abstract Detection of hazardous toxic gases for air pollution monitoring and medical diagnosis has attracted the attention of researchers in order to realize sufficiently sensitive gas sensors. In this paper, we fabricated and characterized a Titanium dioxide (TiO2)-based gas sensor enhanced using the gold nanoparticles. Thermal oxidation and sputter deposition methods were used to synthesize fabricated gas sensor. X-ray diffraction analysis was used to determine the anatase structure of TiO2 samples. It was found that the presence of gold nanoparticles on the surface of TiO2 enhances the sensitivity response of gas sensors by up to about 40%. The fabricated gas sensor showed a sensitivity of 1.1, 1.07 and 1.03 to 50 ppm of acetone, methanol and ethanol vapors at room temperature, respectively. Additionally, the gold nanoparticles reduce 50 s of response time (about 50% reduction) in the presence of 50 ppm ethanol vapor; and we demonstrated that the recovery time of the gold decorated TiO2 sensor is less than 40 s. Moreover, we explain that the improved performance depends on the adsorption-desorption mechanism, and the chemical sensitization and electronic sensitization of gold nanoparticles.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".