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Record W2335205056 · doi:10.1021/ef2009265

Rapid Microfluidics-Based Measurement of CO<sub>2</sub> Diffusivity in Bitumen

2011· article· en· W2335205056 on OpenAlex
Hossein Fadaei, Brent Scarff, David Sinton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of TorontoUniversity of Victoria
Fundersnot available
KeywordsSpark plugMicrofluidicsAsphaltDiffusionThermal diffusivityMaterials scienceAnalytical Chemistry (journal)Bar (unit)Molecular diffusionChemistryThermodynamicsNanotechnologyComposite materialChromatographyGeology

Abstract

fetched live from OpenAlex

In this paper, we demonstrate the first application of microfluidics to study diffusive transport in extra heavy oils, such as bitumen. A cross-channel glass microfluidic chip was used to measure the CO 2 diffusion in Athabasca bitumen. The device was initially filled with CO 2 at low pressure (<1.0 bar). A plug of bitumen was injected into the central (50 μm wide and 20 μm deep) channel and, subsequently, exposed to high-pressure CO 2 on both ends. One-dimensional oil swelling in response to CO 2 diffusion was imaged over time. A simple mathematical approach was applied to calculate the diffusion coefficient based on the oil-swelling data. Measurement results are reported here at a range of pressures (1–5 MPa) and room temperature (21 °C). The measured diffusion coefficients in this range are on the order of 10 –10 m 2 /s, in good agreement with the relevant published data using conventional methods. In sharp contrast to conventional methods that require hours or days and ∼0.5 L of sample, the method presented here requires ∼10 min and a 1 nL plug of sample.

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.

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 score0.020
Threshold uncertainty score0.835

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 head0.017
GPT teacher head0.190
Teacher spread0.174 · 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