Bibliographic record
Abstract
This research aimed to induce repulsive gelation in Citrem-stabilized O/W emulsions by creating a secondary layer of chitosan around the droplets. A range of chitosan concentrations (0-0.25 wt%) and degrees of deacetylation (DDA 50% and 93%) were used to establish the conditions for repulsive gelation in 36 wt% O/W emulsion. The bilayer emulsions were prepared by the electrostatic deposition of positively charged chitosan on negatively charged Citrem-stabilized droplets at pH 4. The droplet size increased from <0.5 μm for the primary emulsion to 5-10 μm at an intermediate chitosan concentration (0.05-0.15 wt%) due to bridging flocculation and again dropped to 1.7-3.6 μm at higher concentrations (0.2 and 0.25 wt%). The droplet charge changed from -48 mV for the primary emulsion to +41.4 and +54.5 mV after surface saturation by DDA 50 and DDA 93 chitosan, respectively. The strain and frequency-dependent rheology indicated that with an increase in the chitosan concentration, emulsions changed from a viscoelastic liquid for monolayer emulsions to strong attractive gel due to bridging flocculation at an intermediate chitosan concentration. At a higher concentration, repulsive gels were formed at complete coverage due to an increase in the effective oil volume fraction towards close packing resulting from the expansion of the interfacial steric barrier and charge cloud thickness. The overall lipid digestibility during <i>in vitro</i> digestion was 25.7% for monolayer emulsions, which decreased with increased chitosan concentration and reached the lowest at surface saturation (17.5%). It was proposed that the formation of the Citrem-chitosan bilayer controlled lipid digestibility by delaying the action of gastric and pancreatic lipases. Such bilayer emulsion gels can be utilized for structure formation in reduced-fat foods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.005 |
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".