Bio‐inspired Dry Adhesives: Contact Electrification and Electrostatic Interactions
Bibliographic record
Abstract
Abstract This article discusses contact electrification and related electrostatic forces as they pertain to the adhesive properties of gecko foot pads and gecko‐inspired adhesives. Following an introduction to gecko adhesion and gecko‐inspired adhesives, fundamentals of contact electrification‐driven electrostatic interactions are discussed, along with electrostatic phenomena that can affect these interfacial interactions. Particular attention has been given to three special phenomena: electrostatic discharge, surface charge leakage, and charge penetration into the matrix of fibrillar dry adhesives. Through the analysis of the effects of these three phenomena, it is demonstrated how the general characteristics of fibrillar dry adhesives, such as strong attachment, easy detachment, and self‐cleaning, can be considered and explained in terms of contact electrification‐driven electrostatic interactions of these materials. In this connection, the effects of many factors (electrical conductivity, dielectric properties, and geometrical properties) on contact electrification‐driven electrostatic interactions of fibrillar dry adhesives have also been discussed. The ensuing discussion on the magnitude of electrostatic adhesion forces that fibrillar dry adhesives can develop via contact electrification has been carried out with respect to three key parameters (surface roughness, humidity, and tip geometry) and the effects that these can have on the strength of the contact electrification‐driven electrostatic interactions of gecko and gecko‐inspired adhesives.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".