Rheology and Catastrophic Phase Inversion of Emulsions in the Presence of Starch Nanoparticles
Bibliographic record
Abstract
Emulsions stabilized by solid nanoparticles, referred to as Pickering emulsions, are becoming increasingly important in applications as they are free of surfactants. However, the bulk properties and stability of Pickering emulsions are far from being well understood. In this work, the rheological behavior and catastrophic phase inversion of emulsions in the presence of starch nanoparticles were studied using in-situ measurements of viscosity and electrical conductivity. The aqueous phase consisting of starch nanoparticles was added sequentially in increments of 5% vol. to the oil phase under agitation condition to prepare the emulsions. The emulsions were water-in-oil (W/O) type at low to moderate concentrations of aqueous phase. At a certain critical volume fraction of aqueous phase, catastrophic phase inversion of W/O emulsion to oil-in-water (O/W) emulsion took place accompanied a sharp jump in the electrical conductivity and a sharp drop in the emulsion viscosity. The W/O emulsions were nearly Newtonian at low concentrations of aqueous phase. At high concentrations of aqueous phase, prior to phase inversion, the W/O emulsions exhibited a shear-thickening behavior. The O/W emulsions produced after phase inversion were shear-thinning in nature. The comparison of the experimental viscosity data with the predictions of emulsion viscosity model revealed only partial coverage of droplet surfaces with nanoparticles. With the increase in the concentration of starch nanoparticles (SNPs) in the aqueous phase of the emulsions, the phase inversion of W/O emulsion to O/W emulsion was delayed to higher volume fraction of aqueous phase. Thus SNPs imparted some stability to W/O emulsions against coalescence and phase inversion.
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 | 0.000 |
| 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 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".