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Record W2592707455 · doi:10.1039/c7cp00550d

Bubble nucleation and growth in nanochannels

2017· article· en· W2592707455 on OpenAlexafffund
Bo Bao, Seyed Hadi Zandavi, Huawei Li, Junjie Zhong, Arnav Jatukaran, Farshid Mostowfi, David Sinton

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Toronto
FundersAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNanofluidicsNucleationBubbleMaterials scienceNanotechnologyOil shalePhase changeChemical physicsMechanicsChemistryEngineering physicsGeologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

We apply micro- and nanofluidics to study fundamental phase change behaviour at nanoscales, as relevant to shale gas/oil production. We investigate hydrocarbon phase transition in sub-100 nm channels under conditions that mimic the pressure drawdown process. Measured cavitation pressures are compared with those predicted from the nucleation theory. We find that cavitation pressure in the nanochannels corresponds closer to the spinodal limit than that predicted from classical nucleation theory. This deviation indicates that hydrocarbons remain in the liquid phase in nano-sized pores under pressures much lower than the saturation pressure. Depending on the initial nucleation location - along the channel or at the end - two types of bubble growth dynamics were observed. Bubble growth was measured experimentally at different nucleation conditions, and results agree with a fluid dynamics model including evaporation rate, instantaneous bulk liquid velocity, and bubble pressure. Collectively these results demonstrate, characterize, and quantify isothermal bubble nucleation and growth of a pure substance in nanochannels.

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 score0.195
Threshold uncertainty score0.609

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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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

Citations66
Published2017
Admission routes2
Has abstractyes

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