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Record W2610855937 · doi:10.1002/cjce.22881

Preparation and morphology study of carbon molecular sieve membrane derived from polyimide

2017· article· en· W2610855937 on OpenAlexaffvenue
Subrata Mondal, Ali Elkamel, Donald Reinalda, Kean Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
FundersAbu Dhabi National Oil Company
KeywordsMaterials sciencePyrolytic carbonChemical engineeringMolecular sieveMicroporous materialMicrostructureMorphology (biology)Amorphous solidPolyimideCarbon fibersPyrolysisRaman spectroscopyFourier transform infrared spectroscopyAdsorptionMesoporous materialGraphiteMembraneAmorphous carbonHighly oriented pyrolytic graphitePorosityLayer (electronics)NanotechnologyCrystallographyOrganic chemistryComposite materialChemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Carbon molecular sieve (CMS) membranes were prepared by pyrolysis of polyimide films at three different temperatures (600, 900, and 1100 °C). The physico‐chemical structure and morphology of the derived samples were characterized by a number of techniques. FTIR spectra and SEM/EDX revealed changes in surface chemistry and overall compositions. Raman spectroscopy indicated the co‐existence of ordered graphite and amorphous domains. TEM and XRD suggested that the samples possess a ‘turbostratic’ structure. With the increase of the pyrolytic temperature, the evolution of the microstructure was clearly evidenced. It was observed qualitatively in TEM images and reflected by the d ‐spacing. The surface area, pore volume, and size distribution of micropores/small micropores were derived from the standard N 2 and CO 2 isotherms, respectively, and compared for their roles in determining mesopore, micropore, and small micropores. These surface characteristics were analyzed and compared with the gas adsorption/permeation properties.

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 score0.047
Threshold uncertainty score0.422

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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