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Record W2019442840 · doi:10.1002/app.28862

Semi‐IPN asymmetric membranes based on polyether imide (ULTEM) and polyethylene glycol diacrylate for gaseous separation

2008· article· en· W2019442840 on OpenAlexaff
Ashwani Kumar

Bibliographic record

VenueJournal of Applied Polymer Science · 2008
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsEthylene glycolMembraneMaterials sciencePolyethylene glycolPolymer chemistryPolymerInterpenetrating polymer networkChemical engineeringScanning electron microscopeFourier transform infrared spectroscopyPEG ratioSelectivityPermeanceChemistryComposite materialOrganic chemistryPermeation

Abstract

fetched live from OpenAlex

Abstract Semi‐interpenetrating polymer networks (semi‐IPN) formed with commercial polyether imide (ULTEM®, PEI) and poly (ethylene glycol) diacrylate (PEGDA) were used to make asymmetric membranes. The effect of increasing amount of PEGDA on the bulk and the gas separation properties of semi‐IPN membranes were studied. The formation of IPNs was confirmed by Fourier Transform Infra Red (FT‐IR) spectroscopy. The 5% weight loss temperature decreased and the percent weight loss of the first step increased with increase in the PEGDA content, which indicated the incorporation of more poly (ethylene glycol) (PEG) segments to the semi‐IPNs. The microscopic experiments revealed the change in morphology with change in PEGDA content. The Scanning electron micrographs exhibited typical finger‐like voids in the sub layer, which is characteristic morphology of asymmetric membranes. The increase in PEGDA content up to 5.7 wt % increased the CO 2 /N 2 selectivity of the semi‐IPN after which the selectivity decreased and permeance increased. Although, the increase in the polar poly (ethylene glycol) molecules is expected to render better CO 2 selectivity, the performance of the membrane was found to decrease as PEGDA content exceeded 5.7% for the given ratio. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

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 score0.025
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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