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Record W2562763347 · doi:10.1021/acsnano.6b05666

Condensation in One-Dimensional Dead-End Nanochannels

2016· article· en· W2562763347 on OpenAlexafffund
Junjie Zhong, Seyed Hadi Zandavi, Huawei Li, Bo Bao, Aaron H. Persad, Farshid Mostowfi, David Sinton

Bibliographic record

VenueACS Nano · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCondensationMaterials scienceKnudsen numberMicroscale chemistryNanoporousChemical physicsCapillary condensationPorous mediumNanotechnologyThermodynamicsPorosityChemistryAdsorptionComposite materialPhysics

Abstract

fetched live from OpenAlex

Phase change at the nanoscale is at the heart of many biological and geological phenomena. The recent emergence and global implications of unconventional oil and gas production from nanoporous shale further necessitate a higher understanding of phase behavior at these scales. Here, we directly observe condensation and condensate growth of a light hydrocarbon (propane) in discrete sub-100 nm (∼70 nm) channels. Two different condensation mechanisms at this nanoscale are distinguished, continuous growth and discontinuous growth due to liquid bridging ahead of the meniscus, both leading to similar net growth rates. The growth rates agree well with those predicted by a suitably defined thermofluid resistance model. In contrast to phase change at larger scales (∼220 and ∼1000 nm cases), the rate of liquid condensate growth in channels of sub-100 nm size is found to be limited mainly by vapor flow resistance (∼70% of the total resistance here), with interface resistance making up the difference. The condensation-induced vapor flow is in the transitional flow regime (Knudsen flow accounting for up to 13% of total resistance here). Collectively, these results demonstrate that with confinement at sub-100 nm scales, such as is commonly found in porous shale and other applications, condensation conditions deviate from the microscale and larger bulk conditions chiefly due to vapor flow and interface resistances.

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 score0.042
Threshold uncertainty score0.472

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 head0.019
GPT teacher head0.219
Teacher spread0.201 · 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

Citations67
Published2016
Admission routes2
Has abstractyes

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