Deposition and Adhesion of Polydopamine on the Surfaces of Varying Wettability
Bibliographic record
Abstract
Mussel-inspired chemistry, particularly the versatile coating capability of polydopamine (PDA), has received much research interest as a promising strategy for fabricating functional coatings in numerous fields. However, the understanding of deposition mechanisms and adhesion behaviors of PDA on different substrates still remains incomplete, significantly limiting the related fundamental research and its practical applications. In this work, a colloidal probe atomic force spectroscopy technique was employed to quantify the interaction forces and adhesion between the PDA coatings and the substrate surfaces with different wettabilities. The surface force measurements and thermodynamic analysis of interaction energy indicate that the surface wettability has a significant influence on the adhesion, deposition behaviors, and morphologies of PDA coatings. Compared with the hydrophilic surfaces, the hydrophobic surfaces exhibit stronger adhesion with the PDA coatings. Furthermore, for the first time, this work demonstrates that ethanol has the capability of effectively displacing the trapped air/vapor layer or the so-called "hydrophobic depletion layer" on the hydrophobic substrate to allow the intimate contact between PDA and the substrate, thus enhancing the adhesion and facilitating the PDA deposition. This work provides new insights into the fundamental PDA deposition mechanism as well as the design and development of versatile mussel-inspired coatings on the substrates of varying hydrophobicity.
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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".