Enabling Weather-Independent Gas Detection through Deep Learning on Light-Activated Sensors
Bibliographic record
Abstract
Light-activated gas sensors offer a low-temperature, low-power approach for detecting target species, and their high-performance capabilities make them ideal for practical applications. The direct integration of Bi-doped In 2 O 3 nanofibers onto micro light-emitting diode (μLED) platforms enables high-performance sensors for simultaneous NO 2 and H 2 O detection. Introducing Bi into In 2 O 3 matrices facilitates the formation of oxygen vacancies and the dissociative adsorption of H 2 O, enhancing the adsorption and reactions with NO 2 . Under blue illumination, this μLED sensor system exhibits high NO 2 sensitivity, with a response value ( R g / R a ) of 264.9 at 1 ppm and 60% relative humidity and response and recovery times of less than 30 s. The use of μLEDs enhances light activation with a high energy transfer efficiency, resulting in outstanding NO 2 sensing characteristics. A convolutional neural network-based algorithm is employed to analyze transient sensing signals, accurately predicting with 99% classification accuracy and 10% regression error for both NO 2 and H 2 O, thereby demonstrating weather-independent sensing. This integration of Bi-doped In 2 O 3 nanofibers, which are specifically activated by blue illumination, μLEDs, and deep learning analytics, enables highly effective real-time environmental monitoring of NO 2 and humidity under environmentally variable outdoor conditions.
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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) | 0.001 | 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".