Instabilities of Teflon AF thin films in alumina nanochannels and adhesion of bi-level Teflon AF nanopillars
Bibliographic record
Abstract
In this paper, a novel replica-molding technique for fabrication of bi-level Teflon AF nanopillars, as an electrostatic-based dry adhesive, is reported. The technique reported herein relies on the concurrent heating and cooling of the Teflon AF melt which filled vertically aligned alumina nanochannels as the mold. Unlike conventional polymer infiltration methods which consist of filling the mold by only heating the polymer above its glass transition temperature, in the current method, the polymer melt was also simultaneously cooled down during the infiltration process. Concurrent cooling of the Teflon AF melt allowed control over the interfacial instabilities of the polymer thin film, which formed ahead of the polymer melt upon its infiltration into the alumina nanochannels. By doing so, the geometrical properties of the peculiar fluffy nanostructure which was subsequently developed-after removal of the mold-on top of the extremely high aspect-ratio Teflon AF nanopillars (200 nm in diameter, ~25 μm tall) were modified. The height of the base nanopillars was measured and the structural properties (i.e., surface area fraction and roughness) of the fluffy nanostructure terminating the base nanopillars at the tip were quantified. Next, the effects of the topographical properties of the bi-level Teflon AF nanopillars on their adhesion, in both the normal and shear directions, were investigated. Tribological results were discussed in detail to clarify the contribution of the structural properties of the fabricated dry adhesive toward its remarkable adhesion and friction forces generated via contact electrification. It is worthwhile to mention that bi-level Teflon AF nanopillars with these specific structural properties have generated enhanced adhesion and friction strengths, up to ~2.1 and 13 N cm(-2), respectively.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".