Laser-induced patterns on metals and polymers for biomimetic surface engineering
Bibliographic record
Abstract
One common feature of many functional surfaces found in nature is their modular composition often exhibiting several length scales. Prominent natural examples for extreme behaviors can be named in various plant leaf (rose, peanut, lotus) or animal toe surfaces (Gecko, tree frog). Influence factors of interest are the surface’s chemical composition, its microstructure, its organized or random roughness and hence the resulting surface wetting and adhesion character. Femtosecond (fs) laser micromachining offers a possibility to render all these factors in one single processing step on metallic and polymeric surfaces. Exemplarily, studies on Titanium and PTFE are shown, where the dependence of the resulting feature sizes on lasing intensity is investigated. While Ti surfaces show rigid surface patterns of micrometer scaled features with superimposed nanostructures, PTFE exhibits elastic hairy structures of nanometric diameter, which upon a certain threshold tend to bundle to larger features. Both surface patterns can be adjusted to mimic specific wetting and flow behaviour as seen on natural examples. Therefore, fs-laser micromachining is suggested as an interesting industrially scalable technique to pattern and fine-tune the surface wettability of a surface to the desired extends in one process step. Possible applications can be seen with surfaces, which require specific wetting, fouling, icing, friction or cell adhesion behaviour.
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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) | 0.004 | 0.001 |
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".