Tuning the properties of injectable poly(oligoethylene glycol methacrylate) hydrogels by controlling precursor polymer molecular weight
Bibliographic record
Abstract
Tuning the properties of in situ-gelling injectable hydrogels based on synthetic polymers typically involves changing the chemistry of polymer backbones or the density of reactive functional groups on precursor polymers. Herein, we describe injectable, hydrazone crosslinked hydrogels based on well-defined poly(oligoethylene glycol methacrylate) (POEGMA) precursors prepared via reversible addition-fragmentation chain transfer (RAFT). These hydrogels have different molecular weights but similar functional group content, enabling engineering of hydrogel properties without substantially changing the chemistry of the precursor polymer. Specifically, although the number of functional crosslinks formed in each gel was found to be equivalent, hydrogels prepared with higher molecular weight precursor polymers showed faster gelation times, higher compressive and shear moduli, slower degradation, and less swelling than gels prepared with lower molecular weight precursor polymers. Thus, this approach is particularly attractive in cases in which separating the effects of physical and chemical changes to gel substrates is critical to understanding or controlling underlying biological interactions.
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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".