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Gas Separation Properties of Mixed Matrix Membranes based on Polyimide and Graphite Oxide

2020· article· en· W2947918320 on OpenAlexaff
Tien Binh Nguyen, Amaya Romero, Antonio Paton, Marialuz sanchez, J.L. Valverde, Serge Kaliaguine, Denis Rodrigue

Bibliographic record

VenueRUIdeRA - Institutional University Repository (University of Castilla-La Mancha) · 2020
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMembraneGrapheneGas separationOxidePolyimideChemical engineeringSelectivityGraphite oxideMaterials scienceMethaneGraphiteChemistryOrganic chemistryComposite materialNanotechnologyLayer (electronics)Catalysis

Abstract

fetched live from OpenAlex

In this work, three different graphene-based materials, namely graphite oxide (GrO), thermally reduced graphite oxide (T-RGrO) and ascorbic acid multi-phase reduced graphene oxide (AMP-RGO), were synthesized and used to produce mixed matrix membranes (MMM) based on Matrimid®5218 for as separation. From the samples produced, a complete set of characterization was performed including XRD, FTIR, TGA and SEM to relate with the gas separation performance using H2, CO2, O2, N2 and CH4. For all the gases studied, the results showed that membrane permeability was inversely proportional to the gas molecular size. This behavior was associated to multi-phase reduced graphite oxide (AMPRGO) being an excellent gas barrier for large gas molecules, especially for CH4. The results showed that the H2/CH4 ideal selectivity increased to 231 which represents a 328% improvement for M/AMP-RGO 0.1 compared to the neat matrix. The CO2/CH4 selectivity was 79.8 for M/AMP-RGO 0.2 wt.% which represents a 344% improvement compared to the neat polymer. These results confirmed that these membranes can be used for methane separation such as in ammonia plants (H2/CH4) or biogas upgrading/natural gas purification (CO2/CH4).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.944

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 head0.013
GPT teacher head0.177
Teacher spread0.164 · 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 designSimulation or modeling
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

Citations14
Published2020
Admission routes1
Has abstractyes

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Same venueRUIdeRA - Institutional University Repository (University of Castilla-La Mancha)Same topicMembrane Separation and Gas TransportFrench-language works237,207