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Record W2086597009 · doi:10.1002/pen.21007

Synthetic 6FDA‐ODA copolyimide membranes for gas separation and pervaporation: Correlation of separation properties with diamine monomers

2008· article· en· W2086597009 on OpenAlexaff
Shude Xiao, Xianshe Feng, Robert Y. M. Huang

Bibliographic record

VenuePolymer Engineering and Science · 2008
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMoietyMonomerMembranePervaporationPolymer chemistryPermeationDiamineSelectivityEtherSulfoneBenzophenoneMaterials scienceGas separationOrganic chemistryPolymerChemistry

Abstract

fetched live from OpenAlex

Abstract Copolyimides were synthesized from 4,4′‐(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and 4‐aminophenyl ether (ODA) with 4‐aminophenyl sulfone (DDS), 4,4′‐methylenedianiline (MDA), 4,4′‐bis(3‐aminophenoxy)diphenyl sulfone (BADS), 4,4′‐bis(3‐aminophenoxy) benzophenone (BABP), and 2,6‐bis(3‐aminophenoxyl) benzonitrile (DABN) as the third monomer. Surface free energies and interfacial free energies were calculated for comparison of the membrane hydrophilicity. Gas permeation was carried out with N 2 , O 2 , H 2 , He, and CO 2 , and the moiety contributions to membrane selectivity were calculated. DDS and BADS moieties contribute negatively to the selectivities toward O 2 /N 2 , H 2 /N 2 , and He/N 2 , and the DABN moiety is favorable for improving CO 2 /N 2 selectivity. Water permeation and dehydration of isopropanol were performed, and the linear moiety contribution method was applied to study the effects of the monomer structures on the temperature and feed concentration dependencies of the permeation flux. The steric effects of DDS and BADS moieties, as well as the interactions of BABP and DABN moieties with water, account for the differences in pervaporation properties of the membranes. POLYM. ENG. SCI., 2008. © 2008 Society of Plastics Engineers

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 score0.190
Threshold uncertainty score0.472

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 head0.015
GPT teacher head0.212
Teacher spread0.198 · 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
Published2008
Admission routes1
Has abstractyes

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