Robust Non-Wetting PTFE Surfaces by Femtosecond Laser Machining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nature shows many examples of surfaces with extraordinary wettability,which can often be associated with particular air-trapping surface patterns. Here,robust non-wetting surfaces have been created by femtosecond laser ablation of polytetrafluoroethylene (PTFE). The laser-created surface structure resembles a forest of entangled fibers, which support structural superhydrophobicity even when the surface chemistry is changed by gold coating. SEM analysis showed that the degree of entanglement of hairs and the depth of the forest pattern correlates positively with accumulated laser fluence and can thus be influenced by altering various laser process parameters. The resulting fibrous surfaces exhibit a tremendous decrease in wettability compared to smooth PTFE surfaces; droplets impacting the virgin or gold coated PTFE forest do not wet the surface but bounce off. Exploratory bioadhesion experiments showed that the surfaces are truly air-trapping and do not support cell adhesion. Therewith, the created surfaces successfully mimic biological surfaces such as insect wings with robust anti-wetting behavior and potential for antiadhesive applications. In addition, the fabrication can be carried out in one process step, and our results clearly show the insensitivity of the resulting non-wetting behavior to variations in the process parameters,both of which make it a strong candidate for industrial applications.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it