Differential scanning calorimetry analysis of W/O emulsions prepared by miniature scale magnetic agitation and microfluidics
Bibliographic record
Abstract
Abstract Understanding and modelling the mechanism of destabilisation of complex and opaque water‐in‐oil emulsions is very challenging. The purpose of our current study is to develop experiments to restrict the number of destabilisation mechanisms that take place in order to better understand their role in the evolution of model emulsions. We focused particularly on the effects of droplet size increase and of droplet sedimentation, which can be observed when the emulsion ages. The evolution of the water in oil emulsion was characterised by following the displacement of the water freezing transition with time by Differential Scanning Calorimetry. We present the results obtained on two different systems: first on emulsions prepared in a miniature cell with an integrated magnetic stirrer designed for experiments on ground and under microgravity conditions (FASES program). In addition, the first results obtained with a microfluidic device, in order to generate dispersed water droplets of uniform size and determine the most probable freezing temperature of the water droplets as a function of their size, are displayed.
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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 | 0.001 |
| 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 | 0.001 |
| 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".