Optimal ultrasonication process time remains constant for a specific nanoemulsion size reduction system
Bibliographic record
Abstract
This paper theorizes the existence of a constant optimum ultrasound process time for any size-reduction operation, independent of process parameters, and dependent on product parameters. We test the concept using the case of 'ultrasonic preparation of oil-in-water nanoemulsions' as model system. The system parameters during ultrasonication of a hempseed oil nanoemulsion was evaluated by a response surface methodology, comprising lecithin and poloxamer-188 as surfactants. Results revealed that the particle size and emulsion stability was affected significantly (p < 0.05) by all product parameters (content of hempseed oil-oil phase, lecithin and polaxamer-surfactants); but was not significantly (p > 0.05) affected by process parameter ('ultrasonication process time'). Next, other process parameters (emulsion volume and ultrasonic amplitude) were tested using kinetic experiments. Magnitude of particle size reduction decreased with increasing 'ultrasonication process time' according to a first order relationship, until a minimum particle size was reached; beyond which ultrasonication no longer resulted in detectable decrease in particle size. It was found that the optimal ultrasonication process time (defined as time taken to achieve 99% of the 'maximum possible size reduction') was 10 min, and was roughly constant regardless of the process parameters (sample volume and ultrasonic amplitude). Finally, the existence of this constant optimal ultrasonication process time was proven for another emulsion system (olive oil and tween 80). Based on the results of these case studies, it could be theorized that a constant optimum ultrasonication process time exists for the ultrasonication-based size-reduction processes, dependent only on product parameters.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".