Self-Immolative Hydrogels with Stimulus-Mediated On–Off Degradation
Bibliographic record
Abstract
Hydrogels are of interest for a wide range of applications from sensors to drug delivery and tissue engineering. Self-immolative polymers, which depolymerize from end-to-end following a single backbone or end-cap cleavage, offer advantages such as amplification of the stimulus-mediated cleavage event through a cascade degradation process. It is also possible to change the active stimulus by changing only a single end-cap or linker unit. However, there are very few examples of self-immolative polymer hydrogels, and the reported examples exhibited relatively poor stability in their nontriggered state or slow degradation after triggering. Described here is the preparation of hydrogels composed of self-immolative poly(ethyl glyoxylate) (PEtG) and poly(ethylene glycol) (PEG). Hydrogels formed from 2 kg/mol 4-arm PEG and 1.2 kg/mol PEtG with a light-responsive linker end-cap had high gel content (90%), an equilibrium water content of 89%, and a compressive modulus of 26 kPa. The hydrogel degradation could be turned on and off repeatedly through alternating cycles of irradiation and dark storage. Similar cycles could also be used to control the release of the anti-inflammatory drug celecoxib. These results demonstrate the potential for self-immolative hydrogels to afford a high degree of control over responses to stimuli in the context of smart materials for a variety of applications.
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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".