Formation of porous networks on polymeric surfaces by femtosecond laser micromachining
Bibliographic record
Abstract
In this study, porous network structures were successfully created on various polymer surfaces by femtosecond laser micromachining. Six different polymers (poly(tetrafluoroethylene) (PTFE), poly(methyl methacrylate) (PMMA), high density poly(ethylene) (HDPE), poly(lactic acid) (PLA), poly(carbonate) (PC), and poly(ethylene terephthalate) (PET)) were machined at different fluences and pulse numbers, and the resulting structures were identified and compared by lacunarity analysis. At low fluence and pulse numbers, porous networks were confirmed to form on all materials except PLA. Furthermore, all networks except for PMMA were shown to bundle up at high fluence and pulse numbers. In the case of PC, a complete breakdown of the structure at such conditions was observed. Operation slightly above threshold fluence and at low pulse numbers is therefore recommended for porous network formation. Finally, the thickness over which these structures formed was measured and compared to two intrinsic material dependent parameters: the single pulse threshold fluence and the incubation coefficient. Results indicate that a lower threshold fluence at operating conditions favors material removal over structure formation and is hence detrimental to porous network formation. Favorable machining conditions and material-dependent parameters for the formation of porous networks on polymer surfaces have thus been identified.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".