Survey of Sustainable Wearable Strain Sensors Enabled by Biopolymers and Conductive Organic Polymers
Bibliographic record
Abstract
The field of wearable sensors has evolved with operating devices capable of measuring biomechanics and biometrics, and detecting speech. The transduction, being the conversion of the biosignal to a measurable and quantifiable electrical signal, is governed by a conductive organic polymer. Meanwhile, the conformality of skin to the substrate is quintessential. Both the substrate and the conductive polymer must work in concert to reversibly deform with the user's movements for motion tracking. While polydimethylsiloxane shows mechanical compliance as a sensor substrate, it is of environmental interest to replace it with sustainable and degradable alternatives. As both the bulk of the weight and area of the sensor consist of the substrate, using renewable and biodegradable materials for its preparation would be an important step toward improving the lifecycle of wearable sensors. This review highlights wearable resistive sensors that are prepared from naturally occurring polymers that are both sustainable and biodegradable. Conductive polythiophenes are also presented, as well as how they are integrated into the biopolymer for sensors showing mechanical compliance with skin. This polymer is highlighted because of its structural conformality, conductivity, and processability, ensuring it fulfils the requirements for its use in sensors without adversely affecting the overall sustainability and biodegradability of resistive sensors. Different sustainable resistive sensors are also presented, and their performance is compared to conventional sensors to illustrate the successful integration of the biosourced polymers into sensors without comprising the desired elasticity and sensitivity to movement. The current state-of-the-art in sustainable resistive sensors is presented, along with knowledge of how biopolymers from different fields can be leveraged in the rational design of the next generation of sustainable sensors that can potentially be composted after their use.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".