The Effect of Calcination Temperature Variation on the Sensitivity of CO Gas Sensor from Zinc Oxide Material by Hydrothermal Process
Bibliographic record
Abstract
Carbon monoxide (CO) is a poisonous gas and could be lethal towards human. A sensitive CO gas sensor isnecessary to prevent accidents caused by CO gas. ZnO is a semiconductor material having many applicationsincluding gas sensors. However ZnO is rarely reported to be used as CO gas sensor material. Therefore, in thisresearch, CO gas sensor has been prepared from ZnO material synthesized via hydrothermal process at 100°C for24 hours using ZnCl2 powder and NH4OH solution. The resulted ZnO gel was subsequently dried andspin-coated on a glass substrate. The ZnO-coated glasses were then calcined at various temperatures of 500°C,550°C, and 600°C for 30 minutes. Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD),Brunauer-Emmet-Teller (BET) analysis were used to characterized the morphology, structure and active surfacearea of ZnO. The sensitivity of the ZnO material towards CO gas was measured using a potentiostat in achamber with operating temperatures 30°C, 50°C, and 100°C with each of gas concentration 10 ppm, 100 ppm,250 ppm, and 500 ppm. It was found that the sample calcined at 550oC showed the highest sensitivity towardsCO gas (0.82) due to the highest active surface area (47.2 m2g-1). It was also observed that the sensitivityincreased with the increasing of operating temperature and CO gas concentration.
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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 | 0.001 |
| 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) | 0.001 | 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".