Tuning Gelation Time and Morphology of Injectable Hydrogels Using Ketone–Hydrazide Cross-Linking
Bibliographic record
Abstract
Injectable, covalently in situ forming hydrogels based on poly(N-isopropylacrylamide) have been designed on the basis of mixing hydrazide-functionalized nucleophilic precursor polymers with electrophilic precursor polymers functionalized with a combination of ketone (slow reacting) and aldehyde (fast reacting) functional groups. By tuning the ratio of aldehyde:ketone functional groups as well as the total number of ketone groups in the electrophilic precursor polymer, largely independent control over hydrogel properties including gelation time (from seconds to hours), degradation kinetics (from hours to months), optical transmission (from 1 to 85%), and mechanics (over nearly 1 order of magnitude) can be achieved. In addition, ketone-functionalized precursor polymers exhibit improved cytocompatibility at even extremely high concentrations relative to polymers functionalized with aldehyde groups, even at 4-fold higher functional group densities. Overall, increasing the ketone content of the precursor copolymers can result in in situ-gellable hydrogels with improved transparency and biocompatibility and equivalent mechanics and stimuli-responsiveness while only modestly sacrificing the speed of gel formation.
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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) | 0.000 | 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".