H<sub>2</sub>S Gas Sensor Based on SnO<sub>2</sub> and CuO Nanoparticles
Bibliographic record
Abstract
Hydrogen Sulfide (H2S) is a toxic gas that has adverse effects on human health as well as the oil and gas industry; hence, it is important to identify its characteristics appropriately. The present research was aimed to study the sensing behavior of SnO2-CuO thin layers obtained by the sol-gel method. The working temperature in the present investigation was the environment temperature and the resistance curves related to the sensing behavior were obtained. The microstructure of the covered layers was studied using Field Emission Scanning Electron Microscope (FE-SEM), AFM and XRD analysis method. The thin layer of SnO2-CuO optimized with sensory characteristics was coated using sol-gel method and were heated for 5 hours at 400 °C. The best response number was 1180 (no unit), the response time was 43 seconds and the least recovery time was 210 seconds. Moreover, it had a longer response time compared to the unheated sample, but the response number and the recovery time were improved due to the thermal treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".