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Record W2740077568 · doi:10.1109/jsen.2017.2734692

Dual Active Resonator for Dispersion Coefficient Measurement of Asphaltene Nano-Particles

2017· article· en· W2740077568 on OpenAlexaff
Mohammad Abdolrazzaghi, Mojgan Daneshmand

Bibliographic record

VenueIEEE Sensors Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDispersion (optics)Analytical Chemistry (journal)ResonatorHeptaneMaterials sciencePhysicsChemistryOpticsThermodynamicsOptoelectronicsChromatography

Abstract

fetched live from OpenAlex

Dispersion and diffusion of Asphaltene particles in n-Heptane is measured to investigate the solution characteristics of different concentrations in wide range of 0.000625-0.625 (%wt). Planar split ring resonator (SRR) is armed with active circuitry (active resonator) to enhance the quality factor over 3 K to achieve ultra-high resolution sensing and track minute variations in microfluidic tube. Parallel SRRs as dual active resonator are performing independently, both in sensing and resolution, at 1.03 GHz (fL) and 1.149 GHz (fH) as the sensing tool in non-contact mode to monitor the spread of model oil (short pulse) in n-Heptane (continuous flow). With the frequency shifts (ΔfL, ΔfH), average flow rate, and the inner diameter of the tube, the molecular diffusion coefficient, and then the dispersion coefficient can be rapidly derived according to two-window solution of Taylor-Aris dispersion analysis. Samples with higher concentration of Asphaltene are shown to have faster spread and larger dispersion in the flow. Dispersion coefficients of the samples cover the range of 5.2-7 × 10-4mm2/s in great agreement with conventional methods.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.260
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2017
Admission routes1
Has abstractyes

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