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Record W2033018667 · doi:10.1063/1.1495849

Revealing the physicochemical mechanism for ultrasonic separation of alcohol–water mixtures

2002· article· en· W2033018667 on OpenAlexaff
Deepak M. Kirpalani, F. Toll

Bibliographic record

VenueThe Journal of Chemical Physics · 2002
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsChemistryMicrobubblesUltrasonic sensorSonicationAlcoholChemical physicsMoleculeCapillary actionChemical engineeringCavitationAnalytical Chemistry (journal)ChromatographyMaterials scienceOrganic chemistryThermodynamicsUltrasoundComposite material

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.271
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2002
Admission routes1
Has abstractyes

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