Pickering emulsions stabilized by microgels: Three regimes of surface coverage
Bibliographic record
Abstract
Oil-in-water Pickering emulsions can be stabilized by poly- N -Isopropylacrylamide based microgels that adsorb, deform and entangle at the droplets interface. The surface coverage Γ emulsion , defined as the mass of microgels per unit interfacial area, likely plays a key role in the emulsion properties as stability and responsiveness. The objective of the present study is to link Γ emulsion to the concentration of microgels used during the emulsification process. Γ emulsion was monitored by combining droplet size analysis, UV–visible quantification of non-adsorbed microgels and Cryo-SEM visualization of the droplets interface, for a microgel concentration range over almost two decades. We demonstrate the existence of three regimes. At low microgel concentration, in the particle poor regime, the well-known “Limited Coalescence” process takes place. All the microgels adsorb, the droplet size distribution is narrow and the mean droplet size is inversely proportional to the microgel concentration: a constant minimum value of Γ emulsion characterizes this domain. For higher microgel concentrations, microgels partition between the interface and the bulk continuous phase. In this intermediate “Excess” regime, both adsorbed and non-adsorbed microgel increase, proving that microgels compress at the interface to maximize their adsorption. At higher microgel concentrations, a third regime named “Saturation” regime is observed for the first time. Γ emulsion then reaches a constant high plateau value while the drop size distribution becomes polydisperse, showing that the fragmentation process dominates over the coalescence. These findings should open new perspectives to better tailor emulsion properties using deformable particles as stabilizers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".