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Record W2143340567 · doi:10.1117/1.jbo.19.12.127004

Dynamic light scattering arising from flowing Brownian particles: analytical model in optical coherence tomography conditions

2014· article· en· W2143340567 on OpenAlexafffund
Ivan A. Popov, Andrew Weatherbee, I. Alex Vitkin

Bibliographic record

VenueJournal of Biomedical Optics · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMinistry of Education and Science of the Russian Federation
KeywordsBrownian motionPhysicsOptical coherence tomographyDoppler effectScatteringOpticsSpectral densitySpeckle patternCoherence (philosophical gambling strategy)Doppler broadeningComputational physicsSpectral lineQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

The statistical model of scattered by flowing Brownian particles coherent radiation is suggested. The model includes the random Doppler shifts caused by particle Brownian motion and the speckle fluctuations caused primarily by the flow motion of particles. Analytical expressions are obtained for the correlation function, power spectrum, and spectral width of scattered radiation in the imaging geometry typically used in optical coherence tomography (OCT). It is shown that the spectral density has the Voigt shape, a well-known spectral profile from atomic and molecular spectroscopy. The approach enables the choice of the experimental regimes for the measurement of Brownian particle motion parameters even in the presence of flow. These regimes are characterized by the dominant contribution of Brownian motion in the spectral width of the flow-caused Doppler shift component. Further, the new formalism suggests that prior attempts to extract transverse flow velocity are only valid at near-perpendicular geometry. The impact of the small scattering volume contributing to the OCT signal is also discussed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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