Versatile sensing devices for self-driven designated therapy based on robust breathable composite films
Bibliographic record
Abstract
Flexible wearable electronics were developed for applications such as electronic skins, human-machine interactions, healthcare monitoring, and anti-infection therapy. But conventional materials showed impermeability, single sensing ability, and no designated therapy, which hindered their applications. Thus it was still a great challenge to develop electronic devices with multifunctional sensing properties and self-driven anti-infection therapy. Herein, flexible and breathable on-skin electronic devices for multifunctional fabric based sensing and self-driven designated anti-infection therapy were prepared successfully with cellulose nanocrystals/iron(III) ion/polyvinyl alcohol (CNC/Fe 3+ /PVA) composite. The resultant composite films possessed robust mechanical performances, outstanding conductivity, and distinguished breathability (3.03 kg/(m 2 ·d)), which benefited from the multiple interactions of weak hydrogen bonds and Fe 3+ chelation and synergistic effects among CNC, polyaniline (PANI), and PVA. Surprisingly, the film could be assembled as a multifunctional sensor to actively monitor real-time physical and infection related signals such as temperature, moisture, pH, NH 3 , and human movements even at sweat states. More importantly, this multifunctional device could act as a self-driven therapist to eliminate bacterial by the release of Fe 3+ , which was driven by the damage of metal coordination Fe-O bonds due to the high temperature caused by infection at wound sites. Thus, the composite films had potential versatile applications in electronic skins, smart wound dressings, human-machine interactions, and self-driven anti-infection therapy.
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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".