Microfluidic Technique for the Simultaneous Quantification of Emulsion Instabilities and Lipid Digestion Kinetics
Bibliographic record
Abstract
Quantifying the impact of environmental physicochemical changes on the microstructure of lipid delivery systems is challenging. Therefore, we have developed a methodology to quantify the coalescence of oil-in-water emulsion droplets during lipid digestion in situ on a single droplet level. This technique involves a custom-made glass microfluidic platform, in which oil droplets can be trapped as single droplets, or several droplets per trap. The physicochemical environment can be controlled, and droplet digestion, as well as coalescence, can be visualized. We show that the exchange of the physicochemical conditions in the entire reaction chamber can be reached in under 30 s. Microparticle image velocimetry allowed mapping of the flow profile and demonstrated the tuneability of the shear profile in the device. The extraction of quantitative information regarding the physical characteristics of the droplets during digestion was performed using an automated image analysis throughout the digestion process. Therefore, we were able to show that oil-in-water emulsions stabilized by proteins coalesced under human gastric conditions. This coalescence delayed the overall lipid digestion kinetics. The droplets that coalesced during digestion were hydrolyzed 1.4 times slower than individually trapped droplets. Thus, the microstructural evolution of lipid delivery systems is a crucial factor in lipid digestion kinetics. This novel technique allows the simultaneous quantification of the impact that the physicochemical environment has on both the lipid droplet microstructure and the lipid release patterns.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".