Polymeric electro-dry-adhesives for use on conducting surfaces
Bibliographic record
Abstract
This work presents electro-dry-adhesives, designed for use on conducting surfaces, that synergistically combine biomimetic dry adhesives with mushroom-like fibres and embedded conductive polymer electrodes. Together, the dry-adhesive surface and electrodes enable the electro-dry-adhesives to generate greatly improved shear adhesion bond strengths. The electro-dry-adhesives described in this work are fabricated with a poly(dimethylsiloxane) biomimetic dry-adhesive surface and embedded interdigitated electrodes manufactured from a carbon black and poly(dimethylsiloxane) composite conductive polymer. The poly(dimethylsiloxane) dry-adhesive layer allows the electro-dry-adhesives to be used both passively and actively, and the poly(dimethylsiloxane) acts as a dielectric insulator between the electrodes and enables the electro-dry-adhesive to be used on conducting surfaces. In order to compare both passive and active use of the electro-dry-adhesives, shear adhesion bond strength is measured and compared with voltage potentials from 0 kV up to 3 kV applied across the electrodes with up to a 2.56 times increase in shear adhesion bond strength. The increase in shear adhesion bond strength due to the generation of an induced electrostatic attractive force is compared to the theoretical maximum shear adhesion bond strength at each of the voltages applied.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".