The Tuning of LIPSS Wettability during Laser Machining and through Post-Processing
Bibliographic record
Abstract
In this work, we investigate the fabrication of stainless-steel substrates decorated with laser-induced periodic surface structures (LIPSS) of both hydrophilic and hydrophobic wettability through different post-processing manipulation. In carrying out these experiments, we have found that while a CO2-rich atmosphere during irradiation does not affect final wettability, residence in such an atmosphere after irradiation does indeed increase hydrophobicity. Contrarily, residence in a boiling water bath will instead lead to a hydrophilic surface. Further, our experiments show the importance of removing non-sintered nanoparticles and agglomerates after laser micromachining. If they are not removed, we demonstrate that the nanoparticle agglomerates themselves become hydrophobic, creating a Cassie air-trapping layer on the surface which presents with water contact angles of 180°. However, such a surface lacks robustness; the particles are removed with the contacting water. What is left behind are LIPSS which are integral to the surface and have largely been blocked from reacting with the surrounding atmosphere. The actual surface presents with a water contact angle of approximately 80°. Finally, we show that chemical reactions on these metallic surfaces decorated with only LIPSS are comparatively slower than the reactions on metals irradiated to have hierarchical roughness. This is shown to be an important consideration to achieve the highest degree of hydro-philicity/phobicity possible. For example, repeated contact with water from goniometric measurements over the first 30 days following laser micromachining is shown to reduce the ultimate wettability of the surface to approximately 65°, compared to 135° when the surface is left undisturbed for 30 days.
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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 | 0.001 |
| 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 | 0.001 |
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".