End-Capping Strategies for Triggering End-to-End Depolymerization of Polyglyoxylates
Bibliographic record
Abstract
Polymers that undergo end-to-end depolymerization in response to the cleavage of a stimuli-responsive end-cap are promising for diverse applications from drug delivery to responsive coatings and plastics. It is critical that the end-cap is designed to respond to an appropriate stimulus for the application. In the current work, end-caps for triggering the depolymerization of poly(ethyl glyoxylate) (PEtG) were explored. First, a phenylboronate, a disulfide, and an azobenzene were utilized to impart redox-responsive properties to PEtG. Then, methoxy-substituted trityl groups were used to provide sensitivity to mild acid. A multiresponsive platform was also introduced, allowing PEtG to respond to multiple stimuli, either simultaneously or independently. Incorporation of a cross-linkable trialkene end-cap enabled the preparation of networks that could subsequently be depolymerized. Finally, high molar mass PEtG could be depolymerized by mechanical stimulation independent of the end-cap. It is anticipated that the versatility in end-capping strategies and potential depolymerization stimuli will not only expand PEtG’s utility for different applications but also be useful for other classes of end-to-end depolymerizable polymers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".