Nanosecond Laser Fabrication of Hydrophobic Stainless Steel Surfaces: The Impact on Microstructure and Corrosion Resistance
Bibliographic record
Abstract
Creation of hydrophobic and superhydrophobic surfaces has attracted broad attention as a promising solution for protection of metal surfaces from corrosive environments. This work investigates the capability of nanosecond fiber laser surface texturing followed by a low energy coating in the fabrication of hydrophobic 17-4 PH stainless steel surfaces as an alternative to the ultrashort lasers previously utilized for hydrophobic surfaces production. Laser texturing of the surface followed by applying the hydrophobic coating resulted in steady-state contact angles of up to 145°, while the non-textured coated base metal exhibited the contact angle of 121°. The microstructure and compositional analysis results confirmed that the laser texturing process neither affects the microstructure of the base metal nor causes elemental loss from the melted regions during the ultrafast melting process. However, the electrochemical measurements demonstrated that the water-repelling property of the surface did not contribute to the anticorrosion capability of the substrate. The resultant higher corrosion current density, lower corrosion potential, and higher corrosion rate of the laser textured surfaces were ascribed to the size of fabricated surface micro-grooves, which cannot retain the entrapped air inside the hierarchical structure when fully immersed in a corrosive medium, thus degrading the material's corrosion performance.
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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".