Integrated optical hydrogen and temperature sensor on silicon-on-insulator platform
Bibliographic record
Abstract
A compact, reliable and safe hydrogen sensor is required for the existing and emerging applications of hydrogen including aerospace and fuel cells. An optical sensor is an attractive option for hydrogen sensing because of its compactness, immunity from electromagnetic interference, and inherent safety. In this work we present the results of experimental demonstrations of a Pd-based hydrogen sensor and a ring resonator based temperature sensor on a siliconon- insulator (SOI) platform. The hydrogen sensor consists of a ridge waveguide with a very thin coating of palladium. The sensor response time is less than 10 seconds for 4% hydrogen concentration, and the sensor response was repeatable under hundreds of cycles of exposure to hydrogen. The response of the hydrogen sensor is affected by variation of temperature, and this effect must be considered in a real life application of the hydrogen sensor. To overcome this limitation we design and experimentally demonstrate a temperature sensor on SOI using a ring resonator, which shows good sensitivity over a wide range of temperature. The hydrogen sensor and the temperature sensor can be integrated on the same chip to implement a sensor capable of reliably measuring hydrogen concentration under varying temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".