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Record W2774947121 · doi:10.1002/cjce.23108

Experimental evaluation and theoretical prediction of CO<sub>2</sub> &amp; CH<sub>4</sub> permeation in PSF/PDMS composite membrane under the influence of membrane swelling

2017· article· en· W2774947121 on OpenAlexvenueno aff
Malik Shoaib Suleman, Kok Keong Lau, Yin Fong Yeong

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
Fundersnot available
KeywordsPermeancePermeationMembraneSwellingPolydimethylsiloxaneMaterials scienceChemical engineeringComposite materialComposite numberPolymer chemistryChromatographyChemistry

Abstract

fetched live from OpenAlex

Abstract A PSF/PDMS composite membrane was developed by film casting of polydimethylsiloxane (PDMS) over polysulphone (PSF) support. The CO 2 /CH 4 separation performance of membranes was evaluated under dry and wet conditions to evaluate the influence of water swelling on the separation performance of the developed membrane. Gas permeance in developed pure and composite membranes was theoretically predicted considering the effect of swelling on gas permeation in the membrane. Swelling in the membrane was confirmed by comparing the cross‐sectional morphology of the developed membranes before and after wet tests. Water swelling in the membrane affected the separation performance of PSF, and CO 2 permeance decreased from 48.79 to 2.37 GPU at 1000 kPa. However, the PSF/PDMS composite membrane resisted the effect of water swelling, and CO 2 permeance increased from 39.9 to 40.87 GPU at 1000 kPa. CO 2 and CH 4 permeation were fitted with the dual mode model to predict the permeation in developed membranes theoretically. Permeation under wet feed conditions was not well‐explained by the existing dual mode model. Thus, a modified dual mode model was presented with the best prediction of gas permeation under the effect of water swelling in the membrane.

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 score0.263
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.234
Teacher spread0.221 · 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
Published2017
Admission routes1
Has abstractyes

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