Highly Stable Hierarchically Structured All-Polymeric Lubricant-Infused Films Prevent Thrombosis and Repel Multidrug-Resistant Pathogens
Bibliographic record
Abstract
Thrombus formation and infections caused by bacterial adhesion are the most common causes of failure in blood-contacting medical devices. Reducing the interaction of pathogens using repellent surfaces has proven to be a successful strategy in preventing device failure. However, designing scale-up methodologies to create large-scale repellent surfaces remains challenging. To address this need, we have created an all-polymeric lubricant-infused system using an industrially viable swelling-coagulation solvent (S-C) method. This induces hierarchically structured micro/nano features onto the surface, enabling improved lubricant infusion. Poly(3,3,3-trifluoropropylmethylsiloxane) (PTFS) was used as the lubricant of choice, a previously unexplored omniphobic nonvolatile silicone oil. This resulted in all-polymeric liquid-infused surfaces that are transparent and flexible with long-term stability. Repellent properties have been demonstrated using human whole blood and methicillin-resistant Staphylococcus aureus (MRSA) bacteria matrices, with lubricated surfaces showing 93% reduction in blood stains and 96.7% reduction in bacterial adherence. The developed material has the potential to prevent blood and pathogenic contamination for a range biomedical devices within healthcare settings.
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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".