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Record W2980111056 · doi:10.1002/cjce.23661

Carbon molecular sieve membranes for natural gas purification: Role of surface flow

2019· article· en· W2980111056 on OpenAlexaffvenue
Shaihroz Khan, Kean Wang, Xianshe Feng, Ali Elkamel

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
FundersTotalAbu Dhabi National Oil CompanyShell United States
KeywordsPermeationMembraneSelectivityMolecular sievePermeability (electromagnetism)ChemistryPyrolysisNitrogenChromatographyChemical engineeringAnalytical Chemistry (journal)Organic chemistryAdsorptionCatalysis

Abstract

fetched live from OpenAlex

Abstract Carbon molecular sieve membranes (CMSM) were prepared from the pyrolysis of polyimide films within a temperature range of 600°C‐800°C under nitrogen stream. The membrane samples were characterized and tested for the permeation of He, CH 4 , CO 2 , and N 2 at different pressures and temperatures, respectively. The CMSM700 membrane (pyrolyzed at 700°C) showed an ideal selectivity of ~ 11 for N 2 /CH 4 with a permeability of 2.18 × 10 −15 mol · m/m 2 · s · Pa for N 2 . The separation mechanism for the N 2 /CH 4 pair was shown to be largely molecular sieving rather than surface flow. The membrane showed an ideal selectivity of ~ 500 for the CO 2 /CH 4 pair with a CO 2 permeability of 9.72 × 10 −14 mol · m/m 2 · s · Pa. The permeability of He was lower than that of CO 2 , suggesting that the surface flow played a significant role in the CO 2 permeation. The updated permeability‐selectivity tradeoff curves show that this CMSM membrane compared favourably with other membrane materials reported in the literature for the removal of N 2 and CO 2 from CH 4 for natural gas upgrading.

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 score0.195
Threshold uncertainty score0.364

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 head0.170
Teacher spread0.166 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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