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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".