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Record W2604446067 · doi:10.1002/cjce.22857

Amine‐functionalized CuBTC/poly(ether‐b‐amide‐6) (Pebax<sup>®</sup> MH 1657) mixed matrix membranes for CO<sub>2</sub>/CH<sub>4</sub> separation

2017· article· en· W2604446067 on OpenAlexafffundvenue
Tayebeh Khosravi, Mohammadreza Omidkhah, Serge Kaliaguine, Denis Rodrigue

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMembraneAmine gas treatingEtherSelectivityAmideFourier transform infrared spectroscopyAdsorptionPolymer chemistryChemistryNuclear chemistryMaterials scienceChemical engineeringOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract CuBTC and NH2‐CuBTC metal organic frameworks (MOF) were used to produce mixed matrix membranes (MMM) with a block copolymer of poly(ether‐b‐amide‐6) (Pebax® MH 1657) as the polymer matrix to investigate the effect of amine functionalized group (–NH2) on the CO2/CH4 gas separation performance of the MMM. The MOF were fully characterized by XRD, FTIR‐ATR, TGA, N2, CO2, and CH4 adsorption, while the MMM were characterized using FTIR‐ATR, TGA, DSC, and SEM. Permeability and selectivity of the MMM were studied at different MOF mass contents (5 to 20 g/g) and feed pressures (0.3 to 1.5 MPa) for pure and mixed gases. The CO2 permeability for 20 g/g of both Pebax/CuBTC and Pebax/NH2‐CuBTC MMMs was nearly twice that of the neat polymeric membrane, while the presence of amine functionalized group in NH2‐CuBTC produced an increasing trend of CO2/CH4 ideal selectivity at high NH2‐CuBTC loading and about a 60 % increase was observed at 20 g/g of NH2‐CuBTC.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.236
Teacher spread0.222 · 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

Citations70
Published2017
Admission routes3
Has abstractyes

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