Controllable Interfacial Adhesion Applied to Transfer Light and Fragile Objects by Using Gecko Inspired Mushroom-Shaped Pillar Surface
Bibliographic record
Abstract
Gecko-inspired surfaces are smart dry adhesive surfaces that have attracted much attention because of their wide range of potential applications. However, strong frictional force, rather than adhesive force, is frequently targeted in most of research in this area. In this study, the interfacial adhesive and frictional properties of a gecko-inspired mushroom-shaped polyurethane pillar array surface have been systematically characterized to design and control the interfacial adhesion of the surface by considering the nanoscale interfacial adhesion, the microscale structural compliance and deformation, and the macro-scale actuation. Matching the movement of the leg springs and the interfacial adhesive characteristics between the pillar array surfaces and substrates, a three-legged clamp prototype has been designed and fabricated to successfully pick up and release light and fragile objects with a smooth upper surface, such as a silicon wafer. These results provide a new insight into not only the theoretical understanding of the integrating adhesion mechanisms, but also the practical applications of utilizing and controlling the adhesive and frictional forces of gecko-inspired surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".