Effects of starch nanoparticles on phase inversion of Pickering emulsions
Bibliographic record
Abstract
Abstract Understanding the effects of interfacial additives such as surface‐active nanoparticles on phase inversion of Pickering emulsions is important from a practical point of view. In this work, we studied the effects of surface‐active starch nanoparticles on catastrophic phase inversion of Pickering emulsions by continuous addition of a dispersed phase. Two types of experimental‐grade starch nanoparticles were used: hydrophilic starch nanoparticles (HSNP) and hydrophobic starch nanoparticles (HOSNP). The dynamic oil‐water interfacial tension was measured using the pendant drop method at varying starch concentrations in the aqueous phase while the contact angles were measured using the sessile drop method of the Axisymmetric Drop Shape Analysis‐Profile (ADSA‐P). Both types of starch nanoparticles (HSNP and HOSNP) were effective in delaying the phase inversion of emulsions from water‐in‐oil (W/O) type to oil‐in‐water (O/W) type. This delay in phase inversion was directly correlated with the concentration of starch nanoparticles. The interfacial tension decreased as the drop aged at a given starch nanoparticle concentration. The contact angles for both types of starch nanoparticles were within the intermediate wettability range that confirmed the irreversible adsorption of starch nanoparticles at the oil/water interface leading to an increased stability of emulsions.
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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 | 0.000 |
| 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) | 0.001 | 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".