Pressure Sensing Piezoelectric Hydrogels for Flexible Wearable Devices
Bibliographic record
Abstract
Piezoelectric materials generate an electric current in response to a mechanical stress or deformation. They are used in a variety of applications, including as self-powered pressure sensors in wearable devices for monitoring motion and gait. Piezoelectric hydrogels hold great potential as interface materials that are compatible with human tissues because of their water content and ability to deform and stretch. In this paper, we demonstrate that carboxymethyl cellulose (CMC) hydrogels crosslinked with cupric cations (Cu2+) using copper(II) chloride (CuCl2) respond to applied pressure by generating an electric current, with a stable response to repeated pressure stimuli. Cyclic voltammetry measurements were performed to characterize the electrical behaviour of the hydrogels, and experiments with one-time compression and cyclical sinusoidal compressions were conducted. The piezoelectric behaviour of CMC crosslinked by CuCl2correlates well with its significant dipole moment, highlighted by computer simulations. Conversely, this is not observed with CMC hydrogel in the absence of copper, which has a small dipole moment. The piezoelectric effect is also significantly smaller in CMC hydrogels crosslinked with cupric cations using copper(II) sulfate (CuSO4), demonstrating the important role of the anions. The results obtained show that CMC hydrogels crosslinked using CuCl2can be easily fabricated and can be used to produce composite insoles for plantar pressure monitoring and gait analysis.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".