Textile-Based Humidity-Driven Wearable Electroluminescent for Visual Sensing
Bibliographic record
Abstract
Miniaturization and integration have become a trend of modern wearable intelligent electronics. But how to visualize sensing information in a single-level device remains a challenge. Here, we present a humidity-driven textile-based electroluminescent (EL) interactive display that allows for both sensing and visualization of humidity changes. Based on an interdigitated EL structure, a transparent humidity sensor layer with high humidity sensitivity was creatively introduced on the top-emitting layer as a bridging electrode. The visualization and sensing of humidity can be attributed to the electrical conductivity difference of the sensor layer, thus leading to the varied lighting emitting of EL devices on the application of given electric fields. Benefiting from the highly sensitive sensor layer and well-designed device structure, a variety of humidity-based behavior can be read immediately, including hand-writing and finger approach. Furthermore, our devices fabricated from textiles have great flexibility, breathability, and skin affinity, which is very suitable for human wearing. More importantly, this humidity-driven textile-based EL interactive display shows great application potential in breathing monitoring and health assessment.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".