Surface and Tribological Behaviors of the Bioinspired Polydopamine Thin Films under Dry and Wet Conditions
Bibliographic record
Abstract
Dopamine is a "sticky" biomolecule containing the typical functional groups of mussel adhesive proteins. It can self-polymerize into a nanoscale thin film on various surfaces. We investigated the surface, adhesion, friction, and cracking properties of polydopamine (PDA) thin films for their effective transfer to functional devices and biocompatible coatings. A series of surface characterizations and mechanical tests were performed to reveal the static and dynamic properties of PDA films coated on glass, polydimethylsiloxane (PDMS), and epoxy. We found that PDA films are highly hydrated under wet conditions because of their porous membrane-like nanostructures and hydrophilic functional groups. Upon dehydration, the films form cracks when they are coated on soft substrates due to internal stresses and the large mismatch in elastic modulus. The adhesive pull-off force or the effective work of adhesion increased with the contact time, suggesting dynamic interactions at the interface. A significant decrease in friction forces in water was observed on all three material surfaces coated with PDA; thus, the film might serve as a water-based lubrication coating. We attributed the different behavior of PDA films in air and in water to its hydration effects. These research findings provide insight into the stability, mechanical, and adhesive properties of the PDA films, which are critical for their applications.
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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".