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Record W2763003327 · doi:10.3390/ma10101159

Hydrogen Separation by Natural Zeolite Composite Membranes: Single and Multicomponent Gas Transport

2017· article· en· W2763003327 on OpenAlexafffund
Afrooz Farjoo, Steve M. Kuznicki, Mohtada Sadrzadeh

Bibliographic record

VenueMaterials · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZeoliteHydrogenAdsorptionGas separationKnudsen diffusionPressure swing adsorptionNatural gasClinoptilolitePermeationMethaneDiffusionChemistryMembraneAir separationChemical engineeringMaterials scienceAnalytical Chemistry (journal)ChromatographyThermodynamicsOrganic chemistryOxygenCatalysis

Abstract

fetched live from OpenAlex

Single and multicomponent gas permeation tests were used to evaluate the performance of metal-supported clinoptilolite membranes. The efficiency of hydrogen separation from lower hydrocarbons (methane, ethane, and ethylene) was studied within the temperature and pressure ranges of 25-600 °C and 110-160 kPa, respectively. The hydrogen separation factor was found to reduce noticeably in the gas mixture compared with single gas experiments at 25 °C. The difference between the single and multicomponent gas results decreased as the temperature increased to higher than 300 °C, which is when the competitive adsorption-diffusion mechanism was replaced by Knudsen diffusion or activated diffusion mechanisms. To evaluate the effect of gas adsorption, the zeolite surface isotherms of each gas in the mixture were obtained from 25 °C to 600 °C. The results indicated negligible adsorption of individual gases at temperatures higher than 300 °C. Increasing the feed pressure resulted in a higher separation efficiency for the individual gases compared with the multicomponent mixture, due to the governing effect of the adsorptive mechanism. This study provides valuable insight into the application of natural zeolites for the separation of hydrogen from a mixture of hydrocarbons.

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 score0.016
Threshold uncertainty score0.751

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 head0.011
GPT teacher head0.237
Teacher spread0.225 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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