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Record W2803666521 · doi:10.5539/jmsr.v7n3p1

CO2 Selective, Zeolitic Imidazolate Framework-7 Based Polymer Composite Mixed-Matrix Membranes

2018· article· en· W2803666521 on OpenAlexvenueno aff
Tina Chakrabarty, Pradeep Neelakanda, Klaus‐Viktor Peinemann

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
FundersDivision of Materials ResearchKing Abdullah University of Science and Technology
KeywordsSelectivityMaterials scienceMembranePolymerPermeanceChemical engineeringZeolitic imidazolate frameworkGas separationComposite materialMetal-organic frameworkOrganic chemistryChemistryAdsorption

Abstract

fetched live from OpenAlex

CO2 removal is necessary to mitigate the effects of global warming but it is a challenging process to separate CO2 from natural gas, biogas, and other gas streams. Development of hybrid membranes by use of polymers and metal-organic framework (MOF) particles is a viable option to overcome this challenge. A ZIF-7 nano-filler that was synthesized in our lab was embedded into a designed polymer matrix at various loadings and the performance of the mixed matrix membranes was evaluated in terms of gas permeance and selectivity. Hybrid membranes with various loadings (20, 30 and 40 wt%) were developed and tested at room temperature by a custom made time lag equipment and a jump in selectivity was observed when compared with the pristine polymer. A commercially attractive region for the selectivity CO2 over CH4 was achieved with a selectivity of 39 for 40 wt% particle loading. An increase in selectivity was observed with the increase of ZIF-7 loadings. Best performance was seen at 40% ZIF-7 loaded membrane with an ideal selectivity of 39 for CO2 over CH4. The obtained selectivity was 105% higher for CO2 over CH4 than the selectivity of the pristine polymer with a slight decrease in permeance. Morphological characterization of such developed membranes showed an excellent compatibility between the polymer and particle adhesion.

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)0.005
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 score0.005
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.034
GPT teacher head0.361
Teacher spread0.328 · 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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