Physical Gelation of Chitosan in the Presence of β-Glycerophosphate: The Effect of Temperature
Bibliographic record
Abstract
When adding beta-glycerophosphate (beta-GP), a weak base, to chitosan aqueous solutions, the polymer remains in solution at neutral pH and room temperature, while homogeneous gelation of this system can be triggered upon heating. It is therefore one of the rare true physical chitosan hydrogels. In this study, physicochemical and rheological properties of chitosan solutions in the presence of acetic acid and beta-GP were investigated as a function of temperature in order to gain a better understanding of the gelation mechanisms. The gel structure formed at high temperature was only partially thermoreversible upon cooling to 5 degrees C because of the existence of remaining associations, confirmed by the spontaneous recovery of the gel after breakup at low temperature. Increasing temperature had no effect on the pH values of this system, while conductivity (and calculated ionic strength) increased. Values from the pH measurements were used to estimate the degree of protonation of each species as a function of temperature. The decreasing ratio of -NH3+ in chitosan and -OPO(O-)2 in beta-GP suggested reduced chitosan solubility along with a diminution of ionic interactions such as ionic bridging with increasing temperature. On the other hand, the increased ionic strength as a function of temperature, in the presence of beta-GP, enhanced screening of electrostatic repulsion and increased hydrophobic effect, resulting in favorable conditions for gel formation. Therefore, our study suggests that hydrophobic interactions and reduced solubility are the main driving force for chitosan gelation at high temperature in the presence of beta-GP.
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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".