Conformal Adhesion Enhancement on Biomimetic Microstructured Surfaces
Bibliographic record
Abstract
Inspired by the superior adhesive ability of the gecko foot pad, we report an experimental study of conformal adhesion of a soft elastomer thin film on biomimetic micropatterned surfaces (micropillars), showing a remarkable adhesion enhancement due to the surface patterning. The adhesion of a low-surface-energy poly(dimethylsiloxane) tape to a SU-8 micropatterned surface was found be able to increase by 550-fold as the aspect ratio increases from 0 to 6. The dependency of the adhesion enhancement on the aspect ratio is highly nonlinear. A series of peeling experiment coupled with optical interference imaging were performed to investigate the adhesion enhancement as a function of the height of the micropillars and the associated delamination mechanisms. Local elastic energy dissipation, side-wall friction, and plastic deformations were analyzed and discussed in terms of their contributions to the adhesion enhancement. We conclude that the local adhesion and friction events of pulling micropillars out of the embedded polymer film play a primary role in the observed adhesion enhancement. The technical implications of this local friction-based adhesion enhancement mechanism were discussed for the effective assembly of similar or dissimilar material components at small scales. The combined use of the micro/nanostructured surfaces with the van der Waals interactions seem to be a potentially more universal solution than the conventional adhesive bonding technology, which depends on the chemical and viscoelastic properties of the materials.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".