Volume Fraction Measurement of Soft (Dairy) Microgels by Standard Addition and Static Light Scattering
Bibliographic record
Abstract
Abstract The volume fraction of the dispersed phase in concentrated soft (dairy) microgels, such as fresh cheese, is directly related to structure and rheology. Measurement or modeling of volume fraction for soft and mechanically sensitive microgel dispersions is problematic, since responsiveness and rheological changes upon mechanical input for these systems limits application of typical functional relationships, i.e., using apparent viscosity. In this paper, we propose a method to measure volume fraction for soft (dairy) microgel dispersions by standard addition and volume-weighted particle size distributions obtained by static light scattering. Relative particle volumes are converted to soft particle volume fraction, based on spiked standard particle volumes. Volume fractions for two example microgel dispersions, namely, differently produced fresh cheeses, were evaluated before and after post-treatments of tempering and mechanical processing. By selecting the size of standard particles based on size ratios and the levels of the mixing ratios/relative fractions, the method could be applied robustly within a wide range of particle sizes (1 to 500 μm) and multimodal size distributions (up to quadmodal). Tempering increased the volume fraction for both example microgel dispersions ( P < 0.05). Subsequent mechanical treatment reduced the volume fraction back to the starting value before tempering ( P < 0.05). Furthermore, it was shown that the increase and successive decrease in apparent viscosity with tempering and mechanical post-treatments is not exclusively due to particle aggregation and breakdown, but to volume changes of each particle. For environmentally responsive soft matter, the proposed method is promising for measurement of volume fraction.
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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 | 0.001 | 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 | 0.000 |
| 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".