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Record W1995587763 · doi:10.1063/1.3650767

Phase behaviour and phase separation kinetics measurement using acoustic arrays

2011· article· en· W1995587763 on OpenAlexafffund
M. Khammar, John M. Shaw

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Alberta
FundersVirtual Materials Group
KeywordsMaterials scienceAttenuationPorous mediumHeptaneBar (unit)Speed of soundPhase (matter)AcousticsAcoustic wavePorosityAcoustic attenuationOpticsThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Speed of sound and acoustic wave attenuation are sensitive to fluid phase composition and to the presence of liquid-liquid interfaces. In this work, the use of an acoustic array comprising 64 elements as a non-intrusive sensor for liquid-liquid interface, phase separation kinetics measurement in bulk fluids, and local composition measurement in porous media is illustrated. Three benchmark examples: the phase behaviour of methanol + mixed hexanes and methanol + heptane mixtures at 25.0 °C and 1 bar, and Athabasca bitumen + heptane in a synthetic silica porous medium at 22.5 °C and 1 bar, illustrate the accuracy of liquid-liquid interface and potential research and industrial applications of the technique. Liquid-liquid interfaces can be detected independently using both speed of sound and acoustic wave attenuation measurements. The precision of the interface location measurement is 300 μm. As complete scans can be performed at a rate of 1 Hz, phase separation kinetics and diffusion of liquids within porous media are readily tracked. The technique is expected to find application where the fluids or porous media are opaque to visible light and where other imaging techniques are not readily applied, or are too costly. A current limitation is that the acoustic probes must be cooled to less than 315 K in order for them to operate.

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 score0.218
Threshold uncertainty score0.405

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 head0.074
GPT teacher head0.391
Teacher spread0.317 · 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

Citations15
Published2011
Admission routes2
Has abstractyes

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