Multifunctional Textured Surfaces with Enhanced Friction and Hydrophobic Behaviors Produced by Fiber Debonding and Pullout
Bibliographic record
Abstract
Fiber debonding and pullout are well-understood processes that occur during damage and failure events in composite materials. In this study, we show how these mechanisms, under controlled conditions, can be used to produce multifunctional textured surfaces. A two-step process consisting of (1) achieving longitudinal fiber alignment followed by (2) cutting, rearranging, and joining is used to produce the textured surfaces. This process employs common composite manufacturing techniques and uses no reactive chemicals or wet handling, making it suitable for scalability. This uniform textured surface is due to the fiber debonding and pullout occurring during the cutting process. Using well-established fracture mechanics principles for composite materials, we demonstrate how different material parameters such as fiber geometry, fiber and matrix stiffness and strength, and interface behavior can be used to achieve multifunctional textured surfaces. The resulting textured surfaces show very high friction coefficients on wet ice (9× improvement), indicating their promising potential as materials for ice traction/tribology. Furthermore, the texturing enhances the surface's hydrophobicity as indicated by an increase in the contact angle of water by 30%. The substantial improvements to surface tribology and hydrophobicity make fiber debonding and pullout an effective, simple, and scalable method of producing multifunctional textured surfaces.
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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".