Self‐immolative Polymer Hydrogels via <i>In Situ</i> Gelation
Bibliographic record
Abstract
Hydrogels are of interest for a wide range of applications. The ability to control when the hydrogel degrades can provide beneficial properties such as controlled degradation in the environment or the stimulated release of drugs or cells. Self-immolative polymers are a class of degradable polymers that undergo complete end-to-end depolymerization upon the application of a stimulus. They have been explored for hydrogel development, but the ability to prepare and selectively degrade self-immolative hydrogels under neutral aqueous conditions has so far been limited. We describe here the preparation of water-soluble polyglyoxylamides with cross-linkable pendent azides and their cross-linking to form hydrogels with 4-arm poly(ethylene glycol)s having unstrained and strained alkynes using copper-assisted and strain-promoted azide-alkyne click chemistry respectively. The influence of pendent azide density and solution polymer content on the resulting hydrogels was evaluated. A polyglyoxylamide with a 70 : 30 ratio of pendent hydroxyl:azide successfully provided hydrogels with compressive moduli ranging from 1.3-6.3 kPa under copper-free conditions at 10-20 % (w/w) of polymer in phosphate-buffered saline. Selective depolymerization and degradation of the hydrogels upon irradiation with light was demonstrated, resulting in reductions in the compressive moduli and the release of depolymerization products that were detected by NMR spectroscopy.
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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".