One-Pot Fabrication of Highly Flexible Fluorine-Free Lubricant-Infused Poly(vinyl alcohol) Films with Superior Antifouling Properties
Bibliographic record
Abstract
In clinical settings, biofluid-contacting devices can suffer from biofouling, leading to thrombus formation and bacterial biofilm buildup, which impair device function and pose health risks. Traditional antifouling methods, including the use of hydrophilic polymers and heparin coatings, often suffer from instability and reduced bioactivity over time. Lubricant-infused surfaces (LIS) have emerged as a promising alternative due to their long-term stability and broad-spectrum repellency. However, current LIS technologies typically involve complex, multistep processes that restrict their application to surface layers, potentially compromising performance under mechanical stress. This study introduces a novel method for bulk modification of poly(vinyl alcohol) (PVA) films, creating flexible lubricant-infused PVA membranes with superior antifouling properties. These films are fabricated by cross-linking the PVA chains using n-propyltrichlorosilane (n-PTCS) and subsequent infusion with silicone oil as a lubricant. The modified PVA films significantly prevent bacterial adhesion and prolong blood and plasma clot formation. Additionally, these films exhibit enhanced mechanical properties, particularly in elasticity and flexibility compared to unmodified PVA films. The developed technique provides a straightforward method for creating flexible, super-repellent biointerfaces with the potential to prevent blood adhesion and bacterial biofilm formation, which are common complications associated with biofluid-contacting devices and medical implants.
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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".