Antibacterial Surfaces Based on Polymer Brushes: Investigation on the Influence of Brush Properties on Antimicrobial Peptide Immobilization and Antimicrobial Activity
Bibliographic record
Abstract
Primary amine containing copolymer, poly(N,N-dimethylacrylamide-co-N-(3-aminopropyl)methacrylamide hydrochloride) (poly(DMA-co-APMA)), brushes were synthesized on Ti surface by surface-initiated atom transfer radical polymerization (SI-ATRP) in aqueous conditions. A series of poly(DMA-co-APMA) copolymer brushes on titanium (Ti) surface with different molecular weights, thicknesses, compositions, and graft densities were synthesized by changing the SI-ATRP reaction conditions. Cysteine-functionalized cationic antimicrobial peptide Tet213 (KRWWKWWRRC) was conjugated to the copolymers brushes using a maleimide-thiol addition reaction after initial modification of the grafted chains using 3-maleimidopropionic acid N-hydroxysuccinimide ester. The modified surfaces were characterized by X-ray photoelectron spectroscopy (XPS), water contact angle measurements, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM), and ellipsometry analysis. The conjugation of the Tet213 onto brushes strongly depended on graft density of the brushes at different copolymer brush compositions. The peptide density (peptides/nm(2)) on the surface varied with the initial composition of the copolymer brushes. Higher graft density of the brushes generated high peptide density (pepetide/nm(2)) and lower number of peptides/polymer chain and vice versa. The peptide density and graft density of the chains on surface greatly influenced the antimicrobial activity of peptide grafted polymer brushes against Pseudomonas aeruginosa.
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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 | 0.001 |
| 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 | 0.000 |
| 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".