Photoinduced Degradation of Polymer Films Using Polyglyoxylate–Polyester Blends and Copolymers
Bibliographic record
Abstract
Abstract Polymeric coatings are commonly employed to alter surface properties. While some coatings are designed to remain stable over a prolonged period, in applications such as pharmaceuticals or fertilizers, the coating is designed to erode and reveal or release the underlying material. Self-immolative polymers (SIPs) undergo depolymerization following the cleavage of stimuli-responsive end-caps from their termini, enabling controlled depolymerization in the solid state and in solution. Poly(ethyl glyoxylate) (PEtG) is a promising SIP because of its depolymerization to benign products, but its amorphous structure and low glass-transition temperature make it unsuitable alone for coating applications. This study explored the blending of PEtG with polyesters including polycaprolactone (PCL), poly(l-lactic acid), and poly(R-3-hydroxybutyrate). Block copolymers of PEtG with PCL were also synthesized and studied. It was found that the phase separation behavior and consequently the thermal and mechanical properties of the materials could be tuned according to the composition of the blend, while the stimuli-responsive degradation of PEtG was retained in the blends. This work therefore provides a framework for the application of PEtG-based coatings in applications ranging from pharmaceuticals to agricultural products.
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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".