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Record W4378348116 · doi:10.1063/5.0146952

Quantifying the effects of dissolved nitrogen and carbon dioxide on drying pressure of hydrophobic nanopores

2023· article· en· W4378348116 on OpenAlexafffund
Hikmat Binyaminov, Janet A.W. Elliott

Bibliographic record

VenueThe Journal of Chemical Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistryNucleationSupercritical fluidSurface tensionThermodynamicsWork (physics)Water vaporCurvatureNanoporeCarbon dioxideMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of a dissolved gas on the behavior of liquid in cylindrical nanopores are investigated in the framework of Gibbsian composite system thermodynamics and classical nucleation theory. An equation is derived relating the phase equilibrium of a mixture of a subcritical solvent and a supercritical gas to the curvature of the liquid-vapor interface. Both the liquid and the vapor phases are treated nonideally, which is shown to be important for the accuracy of the predictions in the case of water with dissolved nitrogen or carbon dioxide. The behavior of water in nanoconfinement is found to be only affected when the gas amount is significantly more than the saturation concentration of these gases at atmospheric conditions. However, such concentrations can be easily reached at high pressures during intrusion if there is sufficient gas present in the system, especially considering gas oversolubility in confinement. By including an adjustable line tension term in the free energy equation (-44 pJ/m for all points), the theory can make predictions in line with the few data points available from recent experimental work. However, we note that such a fitted value empirically accounts for multiple effects and should not be interpreted as the energy of the three-phase contact line. Compared to molecular dynamics simulations, our method is easy to implement, requires minimal computational resources, and is not limited to small pore sizes and/or short simulation times. It provides an efficient path for first-order estimation of the metastability limit of water-gas solutions in nanopores.

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 score0.036
Threshold uncertainty score0.243

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 head0.010
GPT teacher head0.224
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

Citations6
Published2023
Admission routes2
Has abstractyes

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