An integrated optic hydrogen sensor for fast detection of hydrogen
Bibliographic record
Abstract
Hydrogen is used as the main propellant for space shuttles, as an energy source in fuel cells, in oil refineries, and for many other applications. Hydrogen is extremely volatile, easily flammable, and highly explosive. Storage and handling of hydrogen is a challenging task and a good hydrogen sensor is highly desirable. An ideal hydrogen sensor should be fast, reversible, highly selective, compact in size, easy to fabricate, and cheap in price. Unfortunately such a sensor to date is not available. In this paper we propose a multi-channel integrated optical sensor for detection of hydrogen. The sensor consists of a high index waveguide on a low index substrate and uses Pd or Pd alloy thin film as the sensing medium. Since a single channel hydrogen sensor will be affected by the presence of other gases and the variations of temperature, humidity, and input power; a multi-channel sensing scheme and differential measurements are proposed to correct for some of these effects. All the components of the multi-channel sensor can be realized using planar technology and the complete sensor can be fabricated on a single chip. The sensor is compact and the response time is expected to be very short. The concept of multi-channel sensing presented in this work is very general and can be extended to other gas sensors as well.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".