Using low-field nuclear magnetic resonance to investigate the effect of composition, mechanical treatments, and storage on the stability of emulsions
Bibliographic record
Abstract
This study investigates the application of low-field nuclear magnetic resonance (LF-NMR) to evaluate emulsion stability, focusing on formulation, mechanical treatments, and storage. The results showed that Emulsion 2 (water: 72.73 %, oil: 18.18 %, and egg yolk: 9.09 %) showed 4.68 % decrease in the longest peak relaxation time (T 24 ) and 14.58 % reduction in T 24 peak ratio than Emulsion 1 (water: 85.71 %, oil: 9.52 %, and egg yolk: 4.76 %). Mechanical treatments (high-speed mixing and high-pressure homogenization) increased T 24 peak area ratio (>64 %) due to particle size reduction (<200 μm) and enhanced stability. Conversely, centrifuging the mechanically treated samples increased T 24 peak ratio (Emulsion 1: 61.95 %, Emulsion 2: 21.88 %), indicating phase separation. After one day of storage, single-component relaxation time (T 2w ) doubled, reflecting weaker hydrogen bonding. A peak ratio's relationship with storage time (R 2 > 0.99) was developed to predict emulsion stability. This study demonstrates LF-NMR's potential for predicting phase behavior and optimizing emulsion formulations and processing technologies. • LF-NMR revealed interactions, mobility, and stability in liquid systems. • T 2 revealed proton mobility, interactions, and water/lipid states in the systems. • T 2w raise showed weaker binding forces and higher mobility in the aqueous phase. • LF-NMR detected phase separation affected by formulation, treatments, and storage. • LF-NMR is a fast, non-destructive tool for predicting phase behavior and optimizing processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".