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Record W4361011085 · doi:10.1002/cjce.24902

Diffusion coefficients and phase equilibria of carbon dioxide–ionic liquid, <scp>1‐</scp>butyl‐3‐methylimidazolium hexafluorophosphate ([bmim][<scp>PF<sub>6</sub></scp>]) system

2023· article· en· W4361011085 on OpenAlexvenueno aff
Melek Sekerci‐Cetin, Omer Baris Emek, Emine Elif Yildiz, Betül Ünlüsü

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsIonic liquidHexafluorophosphateDiffusionCarbon dioxideThermodynamicsChemistryMole fractionPhase (matter)Analytical Chemistry (journal)Physical chemistryChromatographyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract This article presents the mutual diffusion coefficients of a carbon dioxide–ionic liquid, [bmim][PF6], system at temperatures of 313.15 and 323.15 K and pressures of 5 and 8 MPa. In order to estimate the diffusion coefficients, we have carried out experiments to find time‐dependent carbon dioxide solubilities in the ionic liquid and then fit a transport model to the data. In a system containing high pressure carbon dioxide and ionic liquid, carbon dioxide dissolves in the liquid until its equilibrium mole fraction is reached. During this process, the position of the liquid–vapour interface and the density of the liquid phase change. To account for the variation in liquid density, an equation fit to the experimental density data is included in the transport model. To track the moving interface, the volume‐of‐fluid method is used. The diffusivities at dilute concentration and at thermodynamic phase equilibrium are determined and compared with the literature values and those obtained from correlations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.201
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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