Sustainable Sensors Prepared by Environmentally Benign Means for Improving the Environmental Footprint of Wearable Electronics
Bibliographic record
Abstract
Abstract Sustainable electronic devices offer the virtue of energy efficiency. However, their fabrication is often reliant on environmentally deleterious methods and materials that overshadow the environmental benefits the devices provide. Toward improving the overall environmental footprint of devices, stretchable and conductive substrates for enabling wearable electronics are fabricated predominately from both sustainable and biodegradable materials (chitosan and sorbitol) along with an environmental benign solvent: water. Indeed, the >95 wt.% of the stretchable and bendable sensor consists of sustainable and biodegradable materials. By blending a collectively self‐doped and water‐soluble conductive homopolymer during processing, stretchable films with a transverse resistance as low as 0.08 MΩ are obtained. Both the conductivity and mechanical properties of the films including elongation at break and Young's modulus are contingent on the chitosan molecular weight. The elongation at break of the films prepared from high molecular weight chitosan is upward of 200%, with the optical transmission of 60% above 500 nm, and minimal conductive hysteresis with stretching. Both the mechanical compliance and conductivity of the sustainable films are ideal for enabling wearing electronics. This is demonstrated by their use as strain sensors for tracking both human movement and phonation detection.
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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".