Formation and stability of water‐in‐oil nano‐emulsions with mixed surfactant using in‐situ combined condensation‐dispersion method
Bibliographic record
Abstract
Abstract Nano‐emulsions (NEs) are non‐equilibrium systems and cannot be formed instantaneously. The small droplet size and high kinetic stability of NEs, compared to conventional emulsions, provide them with advantages for their use in many technological applications. Therefore, energy input, generally from mechanical devices or from the chemical potential of the components, is required for the formation of NEs. In the present work, the formation of water‐in‐oil (w/o) NEs with mixed surfactant (HLB = 9.864) was investigated for enhancing the stability of the water/oil interface. A combined condensation‐dispersion method was used in the present study for the production of NEs. The mechanism of NEs formation was examined and illustrated by observing the droplet size distribution (DSD), polydispersity index (PDI), and kinetic stability of NEs. Highly stable, finely dispersed NEs were produced with smaller droplet sizes and low PDIs. The kinetics of the NEs were studied by observing the variation in droplet size growth with storage time. It was observed that both the mean droplet diameter and the PDI decreases with an increase in the homogenization time and speed (rpm). The Ostwald ripening rate of the NEs increased with a decrease in homogenization time. Polydispersity significantly affects the Ostwald ripening rate of NEs. The IFT and SFT of the w/o NEs were decreased with an increase in the rpm and homogenization time. The decrease in droplet size significantly reduces the IFT and SFT of the NEs. Various instability mechanisms of the NEs were examined by fitting the experimental data to different co‐relations. However, Ostwald ripening was found to be a prominent instability phenomenon over coalescence for the produced NEs. The Ostwald ripening rate was estimated according to the Lifshitz‐Slyozov and Wagner (LSW) theory.
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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".