Apparent Failures in Interpretation of Interfacial Characterization When Formulating Emulsions Stabilized by Cellulose Nanocrystals
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are sustainable particles that are effective at stabilizing emulsions by adsorbing at droplet interfaces and providing a steric barrier to coalescence. However, CNCs have surface charges that reduce the coverage of the emulsion droplets due to the electrostatic repulsion between CNCs. In such cases, adding salt is a typical (and straightforward) way to adjust the formulation so that the charges are screened, allowing increased coverage of the droplets. At the outset of this work, we hypothesized that characterization of the interfacial tension and interfacial shear rheology of the oil-water interface would be correlated to interfacial coverage and therefore predictive of the optimal salt concentration for emulsion stability. Included in the methods section as a useful reference to others is the presentation of a detailed derivation for the equations needed to compute interfacial shear moduli in a custom, double-gap geometry. In contrast to our hypothesis, we found that interfacial tension did not correlate well with emulsion stability and that the native surface-active compounds in corn oil overwhelmed any influence of the CNCs on the interfacial tension. Additionally, we found that interfacial shear rheology (which can be painstakingly difficult to measure) was not a useful tool for formulating these emulsions. This is because at commonly used concentrations of CNCs, the bulk rheology is increased to a much greater degree than that of the interface, making the details of the interfacial rheology unimportant. Finally, we found that at concentrations of CNCs that are typical in industrial processes, characterizing the bulk viscoelastic properties of the aqueous suspending phase without added oil (a relatively simple measurement) is sufficient to predict the influence of NaCl concentration on charge screening between the CNCs and, by extension, increased surface coverage of droplets for greater emulsion stability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".