Oleogelation strategies for lubricant stability and diverse media repulsion on textured hydrophobic surfaces
Bibliographic record
Abstract
The study evaluates lubricant thickening and immobilization strategies of safflower (SAFO), medium chain triglyceride (MCT), castor (CO) and grapeseed (GS) oils, in combination with beeswax (BW) and ethyl cellulose (EC) gelling agents, to enhance the anti-wetting and self-cleaning properties of textured polymer containers against water, ketchup, and paint. Sliding ability of the coating was evaluated using critical sliding angle and inclined sliding velocity tests, while self-cleaning and durability was assessed through short- and long-term pouring tests. Similar to previous studies on Newtonian viscous lubricants, the non-Newtonian EC-based lubricants exhibited a reduction in water droplet mobility with increasing viscosity. However, droplet sliding was also influenced by the polyunsaturated fatty acid (PUFA) content, Ricinoleic acid presence, and fatty acid chain length, with CO demonstrating unique interactions that altered sliding behavior. In terms of sliding performance, dual layer oleogels with a mobile fluid layer showed distinct sliding behavior compared to pure oil lubricants, highlighting the interplay between oil structuring and mobility. Notably, the MCT/BW dual layer oleogel demonstrated high droplet mobility across different test media, attributed to its soft texture and weak oil-gel interactions. Pouring durability tests indicated that the CO/EC oleogel offered promising self-cleaning properties against water, while MCT/EC formulations effectively minimized ketchup residue. Long-term water pouring tests confirmed that MCT-based coatings, particularly pure MCT and MCT/BW coatings, successfully eliminated adhered media layers. Overall, these findings emphasize the role of oil-gelling strategies in tailoring lubricant retention, anti-wetting, and self-cleaning properties, with specific combinations proving more effective for different media types.
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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".