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Development of Porous Asymmetric Polyamide–Imide Torlon® Membranes for Physical CO2 Absorption and Separation

2014· article· en· W2146615047 on OpenAlexvenueno aff
Sonay Sheikhi, Amir Mansourizadeh

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
Fundersnot available
KeywordsMembranePermeancePolyamidePermeationPhase inversionContact angleChemical engineeringPolymer chemistryMaterials sciencePolymerPorosityPEG ratioPolyethylene glycolAbsorption (acoustics)Absorption of waterGas separationChemistryComposite material

Abstract

fetched live from OpenAlex

Porous flat-sheet polyamide–imide (PAI) membranes were prepared via a phase inversion method to evaluate CO2 absorption performance in the gas-liquid membrane contactors. Different amounts of polyethylene glycol (PEG-600) were introduced into the polymer solution to investigate the structure and performance of resulted membranes. The membranes were characterized in terms of gas permeation, contact angle measurement and CO2 absorption flux. By introducing 6 wt.% PEG into the polymer dope, N2 permeance of the membrane was significantly improved from 482 to 1320 GPU. Mean while, the effect of PEG on the measured water contact angle was in significant. From CO2 absorption test, the developed membrane presented about 90% higher CO2flux compared to the plain membrane at water flow rate of 70 ml/min. In conclusion, by introducing a polymeric non-solvent additive into the polymer dope, it is possible to enhance surface porosity (permeability) of PAI membranes, which is a key factor for CO2 absorption test.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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 head0.011
GPT teacher head0.262
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

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