Ethyl Cellulose Nanoparticles at the Alkane–Water Interface and the Making of Pickering Emulsions
Bibliographic record
Abstract
Pickering emulsions stabilized by nanoparticles have recently received great attention for their remarkable stability, in part a consequence of irreversible adsorption. In this study, we generate Pickering oil-in-water emulsions stabilized by ethyl cellulose (EC) nanoparticles without the addition of surfactants. Over a range of ionic strength and EC nanoparticle concentrations, a series of dynamic interfacial tension (IFT) measurements complemented by extended DLVO theoretical computations are conducted to quantitatively describe the behavior of EC nanoparticles at the interface of water with different alkanes. Regardless of ionic strength, there is no barrier against the adsorption of EC nanoparticles at the alkane-water interfaces studied and the particles tightly cover these interfaces with near maximal coverage (i.e., 91%). Remarkably, the rate of approach to maximum coverage of the alkane-water interface by EC nanoparticles during the later stages of adsorption is accelerated in the presence of salt at concentrations below the critical coagulation concentration (CCC), unlike the air-water interface. Above the CCC, alkane-water interfaces behave similar to air-water interfaces, showing decay in the adsorption flux which is attributed to an increase in surface blocking originating from the attachment of nanoparticles to nanoparticles already adsorbed at the interface. These findings shed light on particle-particle and particle-interface colloidal interactions at and near fluid-fluid interfaces, thereby improving our ability to use hydrophobic EC nanoparticles as emulsion stabilizers.
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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) | 0.000 | 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".