Probing the Internal Morphology of Injectable Poly(oligoethylene glycol methacrylate) Hydrogels by Light and Small-Angle Neutron Scattering
Bibliographic record
Abstract
While injectable, in situ gelling hydrogels have attracted increasing attention in the biomedical literature due to their minimally invasive administration potential, little is known about the internal morphology of these hydrogels and thus how to engineer precursor polymer compositions to achieve desired hydrogel properties. In this paper, the internal morphology of injectable in situ gelling hydrogels based on hydrazide and aldehyde-functionalized poly(oligoethylene glycol methacrylate) precursors with varying lower critical solution temperatures (LCSTs) is investigated using a combination of spectrophotometry, small-angle neutron scattering, and light scattering. If two precursor polymers with similar LCSTs are used to prepare the hydrogel, relatively homogeneous hydrogels are produced (analogous to conventional step-growth polymerized hydrogels); this result is observed provided that gelation is sufficiently slow for diffusional mixing to compensate for any incomplete mechanical mixing in the double-barrel syringe and the volume phase transition temperature (VPTT) of the hydrogel is sufficiently high that phase separation does not occur on the time scale of gelation. Hydrogels prepared from precursor polymers with different LCSTs (1 polymer/barrel) also retain transparency, although their internal morphology is significantly less homogeneous. However, if functionalized polymers with different LCSTs are mixed in each barrel (i.e., 2 polymers/barrel, such that a gelling pair of precursors with both low and high LCSTs is present), opaque hydrogels are produced that contain significant inhomogeneities that are enhanced as the temperature is increased; this suggests phase separation of the hydrogel into lower and higher LCST domains. Based on this work, the internal morphology of injectable hydrogels can be tuned by engineering the gelation time and the physical properties (i.e., miscibility) of the precursor polymers, insight that can be applied to improve the design of such hydrogels for biomedical 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) | 0.000 | 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".