Revealing the physicochemical mechanism for ultrasonic separation of alcohol–water mixtures
Bibliographic record
Abstract
The selective separation of ethanol from ethanol–water mixtures by ultrasonic atomization has been reported recently by Sato, Matsuura, and Fujii [J. Chem. Phys. 114, 2382 (2001)]. In that work, experimental data were reported that confirmed the generation of an ethanol-rich droplet mist and attempted to explain the selective separation in terms of parametric decay instability of the capillary wave formed during sonication. In the present work, an alternate mechanism based on the conjunction theory has been postulated for the process of ultrasonic atomization. This mechanism involves the formation of cavitating bubbles in the liquid during sonication and their eventual collapse at the liquid surface into a cloud of microbubbles that moves upwards in a capillary fountain jet. The selective separation of alcohols has been explained as a corollary effect of the physical mechanism resulting in a surface excess of alcohol molecules formed at the surface of the microbubbles. The alcohol molecules vaporize into the microbubbles and release an alcohol-rich mist on their collapse in regions of high accumulation of acoustic energy.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".