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Record W2799475858 · doi:10.1002/cjce.23235

Zigzag pore based molecular simulation on the separation of CO<sub>2</sub>/CH<sub>4</sub> mixture by carbon membrane

2018· article· en· W2799475858 on OpenAlexvenueno aff
Yanqiu Pan, He Liu, Wei Wang, Tonghua Wang, Yu Lu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesCentral University Basic Research Fund of ChinaNational Natural Science Foundation of China
KeywordsMolecular sieveAdsorptionZigzagMolecular dynamicsMaterials scienceDiffusionGas separationCarbon fibersPermeationSelectivityMembraneMonte Carlo methodThermodynamicsAnalytical Chemistry (journal)Chemical engineeringChemistryPhysical chemistryChromatographyComputational chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A zigzag‐type pore structure was proposed for the separation of a CO 2 /CH 4 binary mixture by carbon molecular sieve membrane (CMSM). A defect‐free pore and three kinds of defect pores, i.e., random, uniform, and partial defect, were considered to improve the separation performance. Adsorption and diffusion behaviours of pure gases and a gas mixture were simulated by the grand canonical Monte Carlo (GCMC) method and non‐equilibrium molecular dynamics (NEMD) method, respectively. The pressure ranged from 10–100 kPa and the temperature was between 273–348 K during simulation. The calculated isotherms of CO 2 and CH 4 showed an acceptable agreement with the experimental data for the defect‐free pores of 0.67 nm in size at 298 K. Examination on the effect of pore sizes showed that 0.67 nm was the appropriate option for separation. The total selectivity was 20.1 at 298 K and 100 kPa with the pore size of 0.67 nm, which is consistent with the experimental value. The adsorption was determined as the dominant separation mechanism between adsorption and diffusion. Compared with the defect‐free pores, the introduction to the random and uniform defect pores can improve the total selectivities and the random defect provided superior separation performance. An appropriately low temperature and small pore size were beneficial to the separation.

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 score0.141
Threshold uncertainty score0.703

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 head0.006
GPT teacher head0.203
Teacher spread0.196 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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