Stable Antifouling and Antibacterial Coating Based on Assembly of Copper-Phenolic Networks
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".