Self-Assembly and Physicochemical and Rheological Properties of a Polysaccharide−Surfactant System Formed from the Cationic Biopolymer Chitosan and Nonionic Sorbitan Esters
Bibliographic record
Abstract
The natural cationic polysaccharide chitosan was mixed with the nonionic surfactants sorbitan monolaurate, sorbitan monooleate, or sorbitan triooleate to produce a biopolymer-surfactant system with unique properties. The mixtures of chitosan and surfactant formed emulsion-like solutions and/or creams. The known properties of the components were considered (i.e., hydrophile-lipophile balance, molecular weight, structure, and density), and various physicochemical and rheological properties of the mixtures were measured. Specifically, the critical micelle concentration of the sorbitan esters in a chitosan solution was measured using both surface tension and fluorescence-based methods. The concentration-dependent morphologies of the aggregates within the chitosan-surfactant solutions were evaluated by optical microscopy and dynamic light scattering. A schematic depicting the possible molecular arrangement of chitosan and surfactant within the various formulations was produced from consideration of the experimental findings. The degree of interaction between chitosan and the individual surfactants was assessed by FTIR analysis. The rheological properties of the chitosan-surfactant emulsions were also investigated and found to be related to the observed morphologies. Overall, clear composition-property relationships were established for these chitosan-surfactant systems which have potential applications in the food and pharmaceutical industries.
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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".