Antifouling Biomimetic Liquid-Infused Stainless Steel: Application to Dairy Industrial Processing
Bibliographic record
Abstract
Fouling is a widespread and costly issue, faced by all food-processing industries. Particularly, in the dairy sector, where thermal treatments are mandatory to ensure product safety, heat-induced fouling represents up to 80% of the total production costs. Significant environmental impacts, due the massive consumption of water and energy, are also to deplore. Fouling control solutions are thus desperately needed, as they would lead to substantial financial gains as well as tremendous progress toward eco-responsible processes. This work aims at presenting a novel and very promising dairy fouling-mitigation strategy, inspired by nature, and to test its antifouling performances in real industrial conditions. Slippery liquid-infused surfaces were successfully designed directly on food grade stainless steel, via femtosecond laser ablation, followed by fluorosilanization and impregnation with an inert perfluorinated oil. Resulting hydrophobic surfaces (water contact angle of 112°) exhibited an extremely slippery nature (contact angle hysteresis of 0.6°). Outstanding fouling-release performances were obtained for these liquid-infused surfaces as absolutely no trace of dairy deposit was found after 90 min of pasteurization test in pilot-scale equipment followed by a short water rinse.
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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".