Prediction of the Amount of Ice Formation in the Water Dispersed Phase of a W/O Emulsion
Bibliographic record
Abstract
This paper deals with the investigation of the effect of solute concentration on the amount of ice formed within some materials such as NaCl + water and glycerol + water solutions dispersed into a W/O emulsion. The investigations were carried out by inserting sample emulsions into a Differential Scanning Calorimeter (DSC-131 evoSetaram, France). The sample emulsions were submitted to a steady cooling-heating program at constant heating rate, = 2.5 K/min between + 20 and – 60 °C in order to get the freezing of water during cooling and its melting during heating. The proportion of ice formed, P was then calculated from the area of the recorded melting signal related to the amount of water frozen. The values obtained were compared to the ones deduced from thermodynamics treatments that needs the knowledge of the conditions of freezing of the solutions in the equilibria domain ice + solution completed by the extension of the equilibrium curve ice + solution in the metastable area of the solutions respect to the salt. The results obtained are in good agreement for glycerol solutions. For NaCl solutions the results obtained from DSC determination show lower values due to the difficulties to determine precisely the ice melting energies deduced from the melting signals. In that case the theoretical determination from the phases diagram appears to be more reliable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".