Improving the Performance of Catalytic Combustion Type Methane Gas Sensors Using Nanostructure Elements Doped with Rare Earth Cocatalysts
Bibliographic record
Abstract
Conventional methane gas sensors based on catalytic combustion have the drawbacks of high working temperature, low thermal stability and small measurement range. To improve their performance, cerium, which possesses high oxygen storage and release ability, was introduced via nanotechnology to prepare Ce-contained nanostructure elements. Three kinds of elements with different carriers: Al(2)O(3), n-Al(2)O(3) and n-Ce-Al(2)O(3) were prepared and separately fabricated (Pt-Pd/Al, Pt-Pd/n-Al, Pt-Pd/n-Ce-Al). The performances of Wheatstone Bridges with three different catalytic elements were tested and compared. The results indicated that the cerium-containing element exhibited better performance than other elements regarding activity, anti-sulfur ability and thermal stability. Moreover, a constant temperature circuit was also applied in this system. The measurement range was extended from 4% to 10% by automatically decreasing the working current in a reasonable range. The maximum error for 0%-10% CH(4) was controlled below 5%, which fully meets the measurement requirements.
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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 | 0.001 |
| Open science | 0.001 | 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".