Defining Role of a High-Molecular-Weight Population in Block Copolymers Based on Poly(α-benzyl carboxylate-ε-caprolactone) and Poly(ethylene glycol) on the Formation of Thermoreversible Hydrogels
Bibliographic record
Abstract
Biodegradable thermoreversible hydrogels with sol-to-gel transition temperatures a few degrees below the physiological temperature have great potential for application in depot drug delivery and tissue engineering. This research aimed to investigate the formation of thermoreversible hydrogels from the triblock copolymers of poly(ethylene glycol) and poly(α-benzyl carboxylate-ε-caprolactone) (PBCL–PEG–PBCL) prepared by bulk and solution polymerization methods. For this purpose, PBCL–PEG—PBCL prepared at fixed PBCL-to-PEG ratios but with different polymerization times were characterized for their average molecular weights, molar mass dispersity, and intrinsic viscosity using 1 H NMR and gel permeation chromatography (GPC). The inverse flow method was used to estimate the sol–gel transition temperature of aqueous polymer solutions. Rheological measurements and dynamic light scattering determined the viscoelastic behavior and aggregations of prepared structures in aqueous media as a function of temperature, respectively. The results indicated the production of a high-molecular-weight population with elevated intrinsic viscosity during the synthesis of block copolymers. The size and proportion of this population grew as a function of polymerization time in both bulk and solution polymerization methods. This increase was more gradual when solution polymerization was applied. Based on the indirect evidence provided by GPC analysis, the formation of this high-molecular-weight PBCL–PEG–PBCL population was attributed to the nonlinear architecture (partial cross-linking and/or branching) of the PBCL segment. Interestingly, around 40% mole concentration of this high-molecular-weight PBCL–PEG–PBCL population was required for thermoreversible micellar aggregation and gel formation in aqueous media.
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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".