Covalently Crosslinked and Physically Stable Polymer Coatings with Chemically Labile and Dynamic Surface Features Fabricated by Treatment of Azlactone-Containing Multilayers with Alcohol-, Thiol-, and Hydrazine-Based Nucleophiles
Bibliographic record
Abstract
We report approaches to the design of covalently crosslinked and physically stable surface coatings with chemically labile and dynamic surface features based on the functionalization of azlactone-containing materials with alcohol-, thiol-, and hydrazine-based nucleophiles. Past studies demonstrate that residual azlactone groups in polymer multilayers fabricated by the reactive layer-by-layer assembly of poly(2-vinyl-4,4-dimethylazlactone) and branched poly(ethylenimine) can react with amine-based nucleophiles to impart new surface and bulk properties through the creation of chemically stable amide/amide-type bonds. Here, we demonstrate that the azlactone groups in these covalently crosslinked materials can also be functionalized using less nucleophilic alcohol- or thiol-containing compounds, using an organic catalyst, or converted to reactive acylhydrazine groups by direct treatment with hydrazine. These methods (i) broaden the pool of molecules that can be used for post-fabrication functionalization to include compounds containing alcohol, thiol, or aldehyde groups and (ii) yield surface coatings with chemically labile amide/ester-, amide/thioester-, and amide/imine-type bonds that make possible the design of new dynamic and stimulus-responsive materials (e.g., surfaces that release covalently bound molecules or undergo changes in extreme wetting behaviors in response to specific chemical stimuli). Our results expand the range of functionality that can be installed in, and thus the range of new functions that can be imparted to, azlactone-containing coatings beyond those that can be accessed using primary amine-based nucleophiles. The chemical approaches demonstrated here using model polymer-based reactive multilayer coatings are general and should thus also prove useful for the design of new responsive surfaces based on other types of azlactone-functionalized materials.
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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) | 0.001 | 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".