Optimized SILAR Growth of Vertically Aligned ZnO Nanorods for Low-Temperature Acetone Detection
Bibliographic record
Abstract
Vertically oriented morphologies of the semiconducting metal oxide (SMO) surface provide a simple and effective means of enhancing gas sensor performance. We successfully synthesized explicitly aligned ZnO nanorods using a simple automated SILAR technique to improve acetone detection. In this work, we found that vertically oriented morphologies, such as well-aligned ZnO nanorods, can significantly enhance the sensor response due to an increase in specific active area and electron mobility, allowing a faster response to changes in the gas environment. The optimal operating temperature for our ZnO nanorod-based sensors in detecting acetone gas is 260 °C. At this temperature, the sensors exhibit a 96% response with a rapid response time of just 3 s. The improved sensing performance is attributed to both electronic and chemical sensitization mechanisms, which enhance the formation of active sites and shorten electron diffusion paths.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".