Hydrophilic Polymer-Guided Polycatecholamine Assembly and Surface Deposition
Bibliographic record
Abstract
Mussel-inspired surface chemistry based on polycatecholamines and polyphenols has been widely applied as a facile and universal method for modifying surfaces. Specifically, the catecholamine-assisted codeposition as a one-step strategy is a versatile strategy used to impart surface functionalities. Despite successful incorporation of numerous functional agents, very little understanding has emerged over the years regarding the mechanism behind their coassembly and codeposition. Here, we employed six different ultrahigh molecular weight hydrophilic polymers of diverse chemistry and architecture and three catecholamines and a polyphenol for investigating the coassembly and codeposition process. The chemistry of the polymers is found to influence the strength of the interaction between the polycatecholamine and the hydrophilic polymers, thus playing an important role in the aqueous self-assembly in solution to nanoaggregates, its formation kinetics, steric stabilization, and surface deposition. Additionally, the codeposition method was used as a platform for developing antifouling and antibiofilm coatings and evaluating their efficiency. Both the chemistry of hydrophilic polymers and the type of the catecholamine influence the antibiofilm properties of the coating. Our studies demonstrated that significant opportunities exist to further define the surface coating process and polycatecholamine self-assembly process by altering the polycatecholamine-hydrophilic polymer interactions.
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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) | 0.000 | 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".