Methods and mechanisms of gas sensor selectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The selectivity of a sensor is the ability to discriminate the target from the interference molecules and display a target-specific sensor response. It is a critical trait for gas sensors that are used in real-time air pollution control, hazardous materials detection, food quality inspection and personal health monitoring. Attaining high target selectivity ensures that sensors will exhibit accurate information about the existence and concentration of a target gas, which is essential for reliable sensor response. To obtain target selectivity, it is critical to determine the optimum modification technique and receptor materials as well as to understand how each method works and how it could be designed for a specific target. For this purpose, in this review we present the working principles of the three leading chemical modification methods including catalyst decoration, composite formation, and surface functionalization, as well as the selection criteria of various recognition materials. Throughout the report, we offer a rich apprehension of these techniques by providing mechanistic insights, application areas, advantages, disadvantages, and plausible applications for the invention of the target-specific gas sensors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it