Effects of Ionic Strength on the Colloidal Stability and Interfacial Assembly of Hydrophobic Ethyl Cellulose Nanoparticles
Bibliographic record
Abstract
Nanoparticle attachment at a fluid interface is a process that often takes place concurrently with nanoparticle aggregation in the bulk of the suspension. Here we investigate systematically the coupling of these processes with reference to the adsorption of aqueous suspensions of ethyl cellulose (EC) nanoparticles at the air-water interface. The suspension stability is optimal at neutral pH and in the absence of salt, conditions under which the electrostatic repulsion among EC nanoparticles is maximized. Nonetheless, hydrophobic attraction dominates particle-interface interactions, resulting in the irreversible adsorption of EC nanoparticles at the air-water interface. The addition of salt weakens the particle-particle and particle-interface repulsive electrostatic forces. This leads to destabilization of the suspension at ionic strengths of 0.05 M or greater but does not affect nanoparticle adsorption. The energy of adsorption, the surface tension and interface coverage at steady state, and the particle contact angle at the interface all remain unchanged by the addition of salt. These findings contribute to the fundamental understanding of colloidal systems and inform the utilization of EC nanocolloids, in particular for the stabilization of foams and 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 | 0.001 |
| 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".