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Record W2622446411 · doi:10.1121/1.4987297

Towards the accurate characterization of the shell parameters of microbubbles based on attenuation and sound speed measurements

2017· article· en· W2622446411 on OpenAlexaff
Amin Jafari Sojahrood, Qian Li, Hossein Haghi, Raffi Karshafian, Tyrone M. Porter, Michael C. Kolios

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAttenuationAcousticsSpeed of soundNonlinear systemSubharmonicSound pressureMaterials scienceShell (structure)MechanicsNonlinear acousticsRange (aeronautics)Computational physicsPhysicsOptics

Abstract

fetched live from OpenAlex

Measurements of microbubble (MB) shell parameters is a challenging task because of the nonlinear dynamics of MBs. Shell parameter estimations that are typically based on solving linear models will generate inaccurate results, especially at higher pressure excitations. These approaches also often ignore the analysis of sound speed which provides useful information about the bulk modulus of the medium. In addition, the effect of MB-MB interaction is neglected. In this work, the attenuation and sound speed of monodisperse MB populations with mean diameters of 4 to 6 micron and peak concentrations of 1000 to 15000 bubbles/ml are measured for a pressure range of 10 to 100 kPa. The subharmonic pressure threshold of the solution was measured by narrowband excitations spanning from 1 to 4 MHz. The subharmonic generation pressure threshold was used to estimate an initial guess for shell viscosity and surface tension. The experimental results were fitted using numerical simulations of the Marmottant model and our recently developed nonlinear model for attenuation and sound speed. The effect of MB-MB interaction was also implemented using simulations of a lattice of interacting MBs (fitted to the measured sizes of MBs) to take into account the effect of concentration.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.031
GPT teacher head0.241
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations11
Published2017
Admission routes1
Has abstractyes

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