Universal Surface-Initiated Polymerization of Antifouling Zwitterionic Brushes Using a Mussel-Mimetic Peptide Initiator
Bibliographic record
Abstract
We report a universal method for the surface-initated polymerization (SIP) of an antifouling polymer brush on various classes of surfaces, including noble metals, metal oxides, and inert polymers. Inspired by the versatility of mussel adhesive proteins, we synthesized a novel bifunctional tripeptide bromide (BrYKY) that combines atom-transfer radical polymerization (ATRP) initiating alkyl bromide with l-3,4-dihydroxyphenylalanine (DOPA) and lysine. The simple dip-coating of substrates with variable wetting properties and compositions, including Teflon, in a BrYKY solution at pH 8.5 led to the formation of a thin film of cross-linked BrYKY. Subsequently, we showed that the BrYKY layer initiated the ATRP of a zwitterionic monomer, sulfobetaine methacrylate (SBMA), on all substrates, resulting in high-density antifouling pSBMA brushes. Both BrYKY deposition and pSBMA grafting were unambiguously confirmed by ellipsometry, X-ray photoelectron spectroscopy, and goniometry. All substrates that were coated with BrYKY/pSBMA dramatically reduced bacterial adhesion for 24 h and also resisted mammalian cell adhesion for at least 4 months, demonstrating the long-term stability of the BrYKY anchoring and antifouling properties of pSBMA. The use of BrYKY as a primer and polymerization initiator has the potential to be widely employed in surface-grafted polymer brush modifications for biomedical and other applications.
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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 | 0.000 | 0.001 |
| 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".