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Record W2344900524 · doi:10.1021/acs.langmuir.6b01056

Condensation in Nanoporous Packed Beds

2016· article· en· W2344900524 on OpenAlexaff
Javed Ally, Shahnawaz Molla, Farshid Mostowfi

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsCapillary condensationWettingCondensationCapillary actionCapillary pressureNanoporousMaterials scienceChemical physicsEvaporationChemical engineeringColloidSaturation (graph theory)ChemistryNanotechnologyComposite materialPorous mediumThermodynamicsAdsorptionPhysical chemistryPorosity

Abstract

fetched live from OpenAlex

In materials with tiny, nanometer-scale pores, liquid condensation is shifted from the bulk saturation pressure observed at larger scales. This effect is called capillary condensation and can block pores, which has major consequences in hydrocarbon production, as well as in fuel cells, catalysis, and powder adhesion. In this study, high pressure nanofluidic condensation studies are performed using propane and carbon dioxide in a colloidal crystal packed bed. Direct visualization allows the extent of condensation to be observed, as well as inference of the pore geometry from Bragg diffraction. We show experimentally that capillary condensation depends on pore geometry and wettability because these factors determine the shape of the menisci that coalesce when pore filling occurs, contrary to the typical assumption that all pore structures can be modeled as cylindrical and perfectly wetting. We also observe capillary condensation at higher pressures than has been done previously, which is important because many applications involving this phenomenon occur well above atmospheric pressure, and there is little, if any, experimental validation of capillary condensation at such pressures, particularly with direct visualization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.153

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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