Designing Stainless Steel Surfaces with Anti‐Pitting Properties Applying Laser Ablation and Organofluorine Coatings
Bibliographic record
Abstract
Long‐lasting and superhydrophobic stainless steel with anti‐pitting properties is achieved by modifying conventional AISI 304L through a two‐step strategy: 1) application of a femtosecond surface laser ablation treatment to generate micro‐nano structures on the surface; and 2) deposition of organofluorine nanometric coating. Samples with two different patterns, namely paraboloid‐ and cauliflower‐like, are approached and investigated by means of contact angle hysteresis, X‐ray photoelectron spectroscopy, and electrochemical techniques. Results indicate that the stainless steel surface acquires efficient anticorrosive properties due to the homogenization and refinement of the patterned microstructure into a magnetite rich phase, in combination with the formation of a carbonaceous and sol–gel layer. The adherent semiconducting oxide layer is stable over time in presence of an aggressive chloride environment. The prepared superhydrophobic surfaces prevent the steel substrates from getting wet with water, protecting them from the pitting corrosion caused by the electrolyte intrusion. The corrosion resistance is explained by a mechanism in which, in addition of the silane coating, the air trapped into the micro‐nano patterned surfaces plays an important role.
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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 | 0.001 | 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 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".