Influence of Cavitation on Ethanol Enrichment in an Ultrasonic Atomization System
Bibliographic record
Abstract
Ethanol was separated from aqueous solutions through ultrasonic atomization. Ethanol enrichment was evaluated by determining ethanol concentration in condensates collected from atomized mist and vapor. The amount of collected mist and vapor accorded with the amount of liquid left from the atomization column. In the limited range of ethanol feed concentration below 30 mol%, the ethanol concentration in the condensates was affected by ultrasonic parameters such as frequency and input power. Ethanol enrichment was enhanced at higher frequencies and lower input power. The effect of ultrasonic parameters on ethanol enrichment was interpreted from the viewpoint of cavitation. Potassium iodide oxidation was conducted to examine the occurrence of cavitation, and the number of violently collapsing bubbles. The use of higher frequency and lower input power, which corresponded to enhance ethanol enrichment, resulted in a decrease in KI reactivity. This trend suggests that violently collapsing bubbles enhanced fragmentation of the bulk liquid where no separation mechanism works. Assuming that the surface excess of ethanol plays a significant role in the separation, possible routes of ethanol transfer from liquid to mist or vapor are suggested.
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 | 0.000 | 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".