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Record W1986705566 · doi:10.1002/cjce.20526

CO<sub>2</sub>–CH<sub>4</sub> phase equilibria on modified multi‐walled carbon nanotubes using Gibbs excess energy models based on vacancy solution theory

2011· article· en· W1986705566 on OpenAlexvenueaboutno aff
Farzaneh Ghaznavi, Shohreh Fatemi, Marzieh Joda

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsThermodynamicsGibbs free energyVacancy defectAdsorptionFlory–Huggins solution theoryLangmuir adsorption modelLangmuirPhase (matter)ChemistryMaterials scienceCarbon nanotubeCarbon fibersPhysical chemistryOrganic chemistryPhysicsCrystallographyNanotechnologyPolymer

Abstract

fetched live from OpenAlex

Abstract CO 2 and CH 4 equilibrium adsorption are predicted by Excess Gibbs energy models based on vacancy solution theory, for single and binary mixture on Multi‐Walled Carbon Nanotubes (MWCNTs) functioned by –NH 2 group. The experimental data of single gas adsorption isotherms were obtained at moderate pressures and temperatures using the volumetric method in a static gaseous set up. Firstly, the equilibrium pressures related to the adsorbed amounts, for single gases, were correlated on Wilson and Flory–Huggins activity coefficient equations based on vacancy solution theory and the model parameters were determined by fitting the model on the experimental data. Secondly, the pure component parameters were implemented in extended Wilson and Flory–Huggins equations for CO 2 and CH 4 mixture to predict the gas–solid phase equilibria. The results showed fairly good agreement between the experiments and both Gibbs models. Finally, the studied models were compared with the popular model of Extended Langmuir. The results revealed more accurately and precisely prediction of Wilson and Flory–Huggins against Langmuir model for mixed gas of CO 2 and CH 4 on MWCNT–NH 2 . © 2011 Canadian Society for Chemical Engineering

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 categoriesMeta-epidemiology (narrow)
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 score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.209
Teacher spread0.181 · 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.

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
Published2011
Admission routes2
Has abstractyes

Explore more

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