Parametric Study of Ultrasuperhydrophobic Nanotextured Microstructured Surface Topographies Produced by Picosecond Laser Micromachining on H13 Tool Steel
Bibliographic record
Abstract
Hierarchical nano/microscale surfaces offer properties that are of high interest to industry, as they can enable value-added functionalities such as controlled frictional, optical, aerodynamic, hydrodynamic, and other phenomena.Advanced laser-based structuring/texturing technologies, such as direct laser writing, laser-induced periodic structuring, and direct laser interference patterning, are most prominent for high-speed, large-area, and cost-effective fabrications of micro/nano grooves, riblets, dimples, pillars, pyramids, and their geometric combinations.The focus of this study is to explore how surface topography components are responsible for producing hydrophobic, superhydrophobic, and ultrasuperhydrophobic (contact angles 160°…175°) surfaces by single-step picosecond laser micromachining.Four functional surfaces, including microstructured square pyramids with side lengths of 10, 20, 30, 40 µm and nanotextured riblets with feature sizes of <1 µm, were machined on H13 tool steel, and the relationship between topographic characteristics and hydrophobic performance were studied.The results demonstrate that all features are synergistically responsible for the hydrophobic performance within a range of contact angles between 140° and 175°.The most critical role in obtaining superhydrophobic and ultrasuperhydrophobic performance was played by laser-induced nanoriblets on top of periodical microstructures.When nanoriblets were removed by flattening the top surfaces, wettability performance dropped from 175° to 139° contact angles.These results lay a scientific and engineering basis for hierarchical surface formation by laser processing and identify statistical metrics affecting surface wettability for the future development of fully controlled and optimized hydrophobic-hydrophilic 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".