A versatile platform to achieve mechanically robust mussel-inspired antifouling coatings <i>via</i> grafting-to approach
Bibliographic record
Abstract
Although significant progress has been made in mussel-inspired antifouling coatings, most of them suffer from low mechanical stability. Herein, we present a facile and efficient method to fabricate mechanically robust mussel-inspired antifouling coatings. A polyvinyl alcohol (PVA)-based mussel-inspired coating material, which exhibits the highest adhesion capability (always at 5B level in a tape adhesion test based on the ASTM D3359 method) and excellent anti-abrasive properties (little mass loss after 1000 abrasion cycles), is used as a universal platform for further modification to introduce antifouling properties. Intriguingly, the hydroxyl groups in this PVA-based coating material are used as the anchor for the installation of either hydrophilic or hydrophobic segments (or both) via a grafting-to approach. Single modifiers, methoxypolyethylene glycol (MPEG), sodium 2-hydroxyethanesulfonate (SHS) and 1H,1H,2H,2H-perfluorooctan-1-ol (PFO), and complex modifiers, MPEG/PFO, are tethered onto the coating through an effective urethane bond formation reaction to endow the surfaces with antifouling efficacy. The functionalized surfaces are shown to be effective in preventing bovine serum albumin (BSA) adsorption and in reducing bacterial (Gram-positive bacteria S. aureus and Gram-negative bacteria E. coli) adhesion. More importantly, such modification does not influence the strong adhesion and excellent anti-abrasion properties of the coating. To the best of our knowledge, this is the first example of merging excellent mechanical properties and antifouling capability for mussel-inspired coatings, which might find niches in a broad range of applications in the industrial and biomedical fields.
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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) | 0.001 | 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) | 0.001 | 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".