Dopamine Assisted Self-Cleaning, Antifouling, and Antibacterial Coating <i>via</i> Dynamic Covalent Interactions
Bibliographic record
Abstract
The rapid accumulation of dead bacteria or protein on a bactericidal surface can reduce the effectiveness of the modified surface and alter its biocidal activity by shielding the surface biocide functional groups, promoting microbial attachment and subsequent biofilm formation. Thus, the alteration of biocidal activity due to biofilm formation can cause serious trouble including severe infection or implant or medical device failure leading to death. Therefore, developing a smart self-cleaning surface is of great interest. Ideally, such a surface can not only kill the attached microbials but also release the dead cells and foulants from the surface under a particular incitement on demand. In this project, a sugar-responsive self-cleaning coating has been developed by forming covalent boronic ester bonds between catechol groups from polydopamine and a benzoxaborole pendant from zwitterionic and cationic polymers. To incorporate antifouling properties and enhance the biocompatibility of the coating, bioinspired zwitterionic compound 2-methacryloyloxyethyl phosphorylcholine (MPC) was chosen and benzoxaborole pendant containing zwitterionic polymer poly(MPC- st -MAABO) (MAABO: 5-methacrylamido-1,2-benzoxaborole) was synthesized. Additionally to impart antibacterial properties to the surface, a quaternary ammonium containing cationic polymer poly(2-(methacryloyloxy)ethyl trimethylammonium (META)- st -MAABO)) was synthesized. These synthesized polymers were covalently grafted to a polydopamine (PDA) coated surface by forming a strong cyclic boronic ester complex with a catechol group of the PDA layer endowing the surface with bacteria contact-killing properties and capturing specific protein. After the addition of cis -diol containing competitive molecules, i.e., saccharides/sugars, this boronic ester complex with a catechol group of PDA was replaced and the attached polymer layer was cleaved from the surface, resulting in the release of both absorbed protein and live/killed bacteria electrostatically attached to the polymer layer. This dynamic self-cleaning surface can be a promising material for biomedical applications avoiding the gathering of dead cells and debris that are typically encountered on a traditional biocidal surface.
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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) | 0.000 | 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".