Surface-Segregation-Induced Nanopapillae on FDTS-Blended PDMS Film and Implications in Wettability, Adhesion, and Friction Behaviors
Bibliographic record
Abstract
Polymer composites have been extensively used to tune the surface property (e.g., wettability, friction, and adhesion) for its advantages of cost-effectiveness, high efficiency, and ease of fabrication. In this work, different amount of trichloro(1H,1H,2H,2H-perfluorooctyl)silane (FDTS) was added into poly(dimethylsiloxane) elastomer to prepare polymer composite films and were selected as a model to illustrate the effects of surface segregation on surface topology, wettability, friction, and adhesion. The results show that the added FDTS forms aggregations and increasing the content of FDTS leads to the difficulty of air bubble elimination, increase in viscosity, and drop in transparency. Driven by the differences of chemical potential, FDTS aggregations migrate to the air-polymer interface, resulting in surface enrichment and formation of nanopapillae (1-200 nm). This phenomenon becomes more significant with the increment in FDTS. The change in surface composition and structure generates profound effects on wettability, friction, and adhesion. The addition of FDTS makes the surface relatively oleophobic and further increasing the content of FDTS does not helpful in improving the oleophobicity due to the notable aggregation. Friction forces first grow with the increasing content of FDTS and then decline after the maximum point at 1.0 wt % of FDTS, which is attributed to the generated regular larger nanopappillae at high concentration. However, these larger nanopapillae lead to the increase in adhesion because more interactions are formed. The findings demonstrate the behaviors of FDTS in polymer composites and provide important guidance for controlling the formation of nanostructures via aggregation and phase segregation and exploring their implications on surface properties.
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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".