Synthesis and Characterization of Backbone Degradable Azlactone-Functionalized Polymers
Bibliographic record
Abstract
We report the design of reactive and degradable copolymers that contain both azlactone side chain functionality and hydrolyzable backbone ester groups. Copolymerization of the vinyl monomer 2-vinyl-4,4-dimethylazlactone (VDMA) and the cyclic ketene acetal 2-methylene-1,3-dioxepane (MDO) using conventional or reversible-deactivation radical polymerization techniques yielded copolymers and block copolymers that exhibit amine reactivity associated with poly(vinyl azlactone)s but also hydrolytic degradability associated with conventional polyesters. Our results demonstrate that control over monomer feed ratios and other parameters can be used to tune both copolymer composition (e.g., the number/ratio of azlactone and ester repeat units) and the physical properties of the resulting materials (e.g., glass transition temperatures and ability to self-assemble into nanoscale structures). Post-fabrication functionalization of reactive azlactone groups in MDO- co -VDMA copolymers by treatment with primary amines proceeds rapidly and quantitatively, and can be achieved without disruption or degradation of backbone ester groups. These azlactone-functionalized copolymers are thus well suited for use as templates for the design of new degradable polymers and as building blocks for the design of covalently and ionically cross-linked macromolecular thin films, capsules, and gels that degrade in aqueous environments.
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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".