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Separation of CO2/CH4 through Carbon Tubular Membranes: Effect of Carbonization Temperature

2014· article· en· W2118128493 on OpenAlexvenueno aff
Wan Norharyati Wan Salleh, Norazlianie Sazali, Ahmad Fauzi Ismail

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonizationMembraneCarbon fibersChemical engineeringChemistryMaterials scienceComposite materialOrganic chemistryAdsorptionBiochemistryComposite number

Abstract

fetched live from OpenAlex

Carbon membranes have received much attention as advance materials in the gas separation technology due to their superior gas permeation performance and thermal and chemical stability. In order to increase the mechanical strength of the membrane, supported carbon membrane were produced using ceramic tube as support layer. Carbon tubular membranes were produced by carbonizing polymeric tubular membrane under different process parameter. In this study, carbon tubular membranes originating from Matrimid were prepared and characterized n term of its gas permeation properties. The preparation method involved dip-coating of the ceramic tubes with a Matrimid-based solution. The carbon tubular membranes were obtained by carbonization of the resultant polymeric tubular membrane under Argon gas flow in the horizontal tube furnace. The effects of the carbonization temperature on the gas permeation performance were investigated. Pure gas permeation tests were performed using CO2 and CH4 at room temperature with pressure 8 bars. The permeance and selectivity data indicate that the highest CO2/CH4 selectivity of 87.30 was obtained for carbon tubular membrane prepared at carbonization temperature of 850 ºC.

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)0.001
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 score0.022
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.234
Teacher spread0.230 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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