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Record W2791688715 · doi:10.1002/cjce.23170

ZIF‐based water‐stable mixed‐matrix membranes for effective CO<sub>2</sub> separation from humid flue gas

2018· article· en· W2791688715 on OpenAlexvenueno aff
Muhammad Sarfraz, Mohammed S. Ba‐Shammakh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneGas separationPermeationChemical engineeringFlue gasMaterials sciencePolymerImidazolateZeolitic imidazolate frameworkMetal-organic frameworkAdsorptionChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Water stable mixed‐matrix membranes (MMMs) were developed to help control global warming by capturing and sequestrating carbon dioxide (CO 2 ) from humid flue gas originated from burning of fossil fuels. MMMs of different compositions were prepared by doping glassy polymer Ultrason ® S 6010 (US) with nanocrystals of zeolitic imidazolate frameworks (ZIF‐302) in varying degrees. A solution‐casting technique was used to fabricate various MMMs to optimize their CO 2 capturing performance from both dry and wet gases. The prepared composite membranes indicated enhanced filler‐polymer interfacial adhesion, consistent distribution of nanofiller, and thermally stable matrix configuration. CO 2 permeability of the membranes was enhanced as demonstrated by gas sorption and single gas permeation tests carried out under dry and moist circumstances. As compared to neat Ultrason ® membrane, CO 2 permeability and expected CO 2 /N 2 permselectivity of the mixed membrane doped with 40 g/g ZIF‐302 nanocrystals were significantly enhanced. In contrast to the majority of previously reported membranes, key features of fabricated MMMs include their structural stability under humid conditions coupled with better and unaffected gas separation performance.

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 score0.017
Threshold uncertainty score0.663

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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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