A Highly Porous and Conductive Composite Gate Electrode for NO, NO2, O2, H2 and NH3 Exhaust Gas Sensors
Bibliographic record
Abstract
The real-time knowledge of the NO, NO2 and NH3 concentration at high temperature, would allow manufacturers of automobiles to meet the upcoming stringent EURO7 anti-pollution measures for diesel engines. Knowledge of the concentration of each of these species will also enable engines to run leaner (i.e. more fuel efficient) while still meeting the anti-pollution requirements. Our proposed technology is promising in the field of automotive sensors. It based on AlGaN/GaN high electron mobility transistor (HEMT) sensors incorporating highly porous platinum in the gate. Compared to platinum full gate AlGaN/GaN HEMT sensor, the Highly porous gate sensor has dramatically improved current response to O2, NO, NO2 H2 and NH3, to the NO concentration in the range of 0–2000 ppm, 0-2500 ppm NO2, 0-300 ppm NH3, (1.5-10%) H2 and O2 (1.5-100%) species at high temperature (500 °C). This improvement in sensitivity is the result of the increase in catalytic surface area interaction between gas and platinum. Keywords—HEMT, sensors, GaN, H2, O2, NO2, NO, NH3 high temperature, highly porous gate.
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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 | 0.000 | 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 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".