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 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 | 0.000 | 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 teacher head, 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".