Emulsion characterization via microfluidic devices: A review on interfacial tension and stability to coalescence
Bibliographic record
Abstract
Emulsions have gained significant importance in many industries including foods, pharmaceuticals, cosmetics, health care formulations, paints, polymer blends and oils. During emulsion generation, collisions can occur between newly-generated droplets, which may lead to coalescence between the droplets. The extent of coalescence is driven by the properties of the dispersed and continuous phases (e.g. density, viscosity, ion strength and pH), and system conditions (e.g. temperature, pressure or any external applied forces). In addition, the diffusion and adsorption behaviors of emulsifiers which govern the dynamic interfacial tension of the forming droplets, the surface potential, and the duration and frequency of the droplet collisions, contribute to the overall rate of coalescence. An understanding of these complex behaviors, particularly those of interfacial tension and droplet coalescence during emulsion generation, is critical for the design of an emulsion with desirable properties, and for the optimization of the processing conditions. However, in many cases, the time scales over which these phenomena occur are extremely short, typically a fraction of a second, which makes their accurate determination by conventional analytical methods extremely challenging. In the past few years, with advances in microfluidic technology, many attempts have demonstrated that microfluidic systems, characterized by micrometer-size channels, can be successfully employed to precisely characterize these properties of emulsions. In this review, current applications of microfluidic devices to determine the equilibrium and dynamic interfacial tension during droplet formation, and to investigate the coalescence stability of dispersed droplets applicable to the processing and storage of emulsions, are discussed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".