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Record W2606587287 · doi:10.1002/cjce.22868

Effect of a heat pretreatment on the structure and properties of carbon supports for carbon membranes

2017· article· en· W2606587287 on OpenAlexvenueno aff
Shanshan Liu, Bing Zhang, Yuan Jiang, Yonghong Wu, Tonghua Wang, Jieshan Qiu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
FundersProgram for New Century Excellent Talents in UniversityNatural Science Foundation of Liaoning ProvinceNational Natural Science Foundation of China
KeywordsMembraneCarbon fibersMicrostructurePyrolysisScanning electron microscopeChemical engineeringMaterials sciencePorosityBoilingComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A heat pretreatment was developed to modify the microstructure and properties of the phenolic resin‐based carbon supports for the preparation of carbon membranes. The pore size distribution, porosity, surface functional groups, microstructure, and morphology of the supports were characterized by bubble pressure method, boiling method, infrared spectroscopy, X‐ray diffraction, and scanning electron microscopy, respectively. Furthermore, the optimum preparation conditions of the as‐prepared supports were validated by the fabrication of supported carbon membranes. The results show that the heat pretreatment at 280 °C is helpful for the supports to tolerate the high temperature of subsequent pyrolysis and improve the adhesion to surface carbon membrane layers. When supported carbon membranes were prepared by the heat pretreated supports, spin‐coating of 4 times, and pyrolysis at 650 °C, the ideal selectivities of H 2 /N 2 and O 2 /N 2 can reach 52.8 and 8.0, respectively.

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 score0.054
Threshold uncertainty score0.243

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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