Preparation of CO Gas Sensor from ZnO Material Synthesized via Thermo-Oxidation Process
Bibliographic record
Abstract
Carbon monoxide (CO) is poisonous to human because of its nature which is capable to bind to the haemoglobinin blood stronger than oxygen, so that causing toxication and even death. Therefore a sensor to early detect thepresence of CO gas is necessary. ZnO is one of semiconductor materials which widely applied as a sensormaterial. However, ZnO is rarely reported as a CO gas sensor material. In this study, ZnO as a sensor materialhas been synthesized by thermo-oxidation of Zn powder at oxidation temperature variations of 800, 850 and 900ºC. The synthesized ZnO was crushed and compacted to form pellet for sensor chip. The ZnO pellets were thensintered at 500 ºC. The material structures were examined using Scanning Electron Microscope (SEM), X-RayDiffractometer (XRD), and Brunauer-Emmet-Teller (BET) analysis. The sensitivity test towards CO gas wasconducted with the variations in sensing operating temperatures of 30, 50, 100 ºC and variation of CO gas inputconcentration of 10 ppm, 50 ppm, 100 ppm, 250 ppm, and 500 ppm. The sensitivity test results showed that thesensitivity towards CO gas decreased as the oxidation temperature increased. In addition the sensitivity increasedalong with the increasing of the sensing operating temperature and CO gas input concentration. Hence, thehighest sensitivity value was obtained from ZnO material synthesized at 800 ºC due to the highest active surfacearea of 69.4 m2g-1 with CO concentration of 500 ppm and sensor operating temperature of 100 ºC.Keywords: ZnO, thermo-oxidation, CO gas, sensitivity
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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 | 0.001 | 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".