Molecular Interaction Mechanisms Between Lubricant‐Infused Slippery Surfaces and Mussel‐Inspired Polydopamine Adhesive and DOPA Moiety
Bibliographic record
Abstract
Lubricant-infused slippery surfaces have recently emerged as promising antifouling coatings, showing potential against proteins, cells, and marine mussels. However, a comprehensive understanding of the molecular binding behaviors and interaction strength of foulants to these surfaces is lacking. In this work, mussel-inspired chemistry based on catechol-containing chemicals including 3,4-dihydroxyphenylalanine (DOPA) and polydopamine (PDA) is employed to investigate the antifouling performance and repellence mechanisms of fluorinated-based slippery surface, and the correlated interaction mechanisms are probed using atomic force microscopy (AFM). Intermolecular force measurements and deposition experiments between PDA and the surface reveal the ability of lubricant film to inhibit the contact of PDA particles with the substrate. Moreover, the binding mechanisms and bond dissociation energy between a single DOPA moiety and the lubricant-infused slippery surface are quantitatively investigated employing single-molecule force spectroscopy based on AFM (SM-AFM), which reveal that the infused lubricant layer can remarkably influence the dissociation forces and weaken the binding strength between DOPA and underneath per-fluorinated monolayer surface. This work provides new nanomechanical insights into the fundamental antifouling mechanisms of the lubricant-infused slippery surfaces against mussel-derived adhesive chemicals, with important implications for the design of lubricant-infused materials and other novel antifouling platforms for various bioengineering and engineering applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".