Stable Antifouling and Antibacterial Coating Based on Assembly of Copper-Phenolic Networks
Why this work is in the frame
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Bibliographic record
Abstract
Biofilm formation on medical devices has become a worldwide issue arising from its resistance to bactericidal agents and presenting challenges to eradicating biofouling adhesion, especially in biological fluids. Metal-phenolic networks have been demonstrated as a versatile and efficient strategy to prevent biofilm formation by endowing medical devices with prolonged antifouling and antibacterial activities in a one-step surface modification. In this study, we report a simple and environmentally friendly method using coordination chemistry between copper ions (Cu 2+ ) and dopamine-containing copolymer to fabricate metal-phenolic network-based coatings. The phenolic groups also imparted the adhesion of glycopolymer-containing dopamine residues to inorganic and organic substrates, resulting in dual antifouling and bactericidal surfaces. 2-gluconamidoethyl methacrylamide monomer (GAEMA) was first copolymerized with dopamine methacrylamide (DMA) using a free-radical polymerization process. The resulting copolymer (GAEMA-DMA), denoted as GADMA, was then mixed with copper ions in a one-step process to form the GADMA-Cu coating. The GADMA-Cu coating was hydrophilic and significantly reduced the water contact angle (WCA) and adsorption of bovine serum albumin protein even after incubation in a bovine serum albumin solution for 30 h. Moreover, the coating exhibited strong antibacterial activity against Escherichia coli and Staphylococcus aureus and was biocompatible with 99% cell viability toward normal human fibroblast (HDFa) cells. Thus, our coating shows great potential for application in medical devices.
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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 | 0.000 | 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) | 0.000 | 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 it