Dielectric Polarization‐Driven Energy Amplification in 2D Nanostructure‐Embedded PVC Gel TENGs for Tribo‐Resistive Sensing Applications
Bibliographic record
Abstract
ABSTRACT Plasticized poly(vinyl chloride) (PVC) gels are prototypical soft ionic polymers that combine strongly negative charge polarity with inherently high permittivity; however, their mobile ions impose substantial dielectric loss and leakage currents, which limit the output of triboelectric nanogenerators (TENGs). Here, graphene oxide (GO) nanosheets are embedded as 2D capacitive layers in a PVC gel, where they immobilize excess ions and add interfacial polarization, giving a dielectric constant of 32 at 1 kHz while lowering the dissipation factor (tan δ) by 65% relative to the pristine gel. The optimized GO‐doped gel TENG delivers 282 V, 20.1 µA, and 612 µW/cm 2— approximately 2.3, 2.0, and 2.5 times the values of the pristine PVC gel, respectively. A single GO‐PVC gel layer simultaneously functions as both dielectric and electrode, powering a self‐powered tribo‐resistive sensor that pinpoints pressures up to 800 kPa over a 5 × 5 virtual grid, with a spatial resolution of ≈ 1.8 mm and pressure sensitivities of 194 mV/kPa (0–200 kPa) and 25 mV/kPa (200–800 kPa). By suppressing ion‐driven loss while amplifying polarization, this 2D capacitive‐layer strategy is transferable to other ionic‐gel systems—including ionic‐liquid gels and ionomers—charting a versatile route toward high‐output soft TENGs for energy‐autonomous wearables and electronic skin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".