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Record W3125069060 · doi:10.1002/cjce.24040

Measuring the permeabilities of binary gas mixtures with a novel time‐lag technique

2021· article· en· W3125069060 on OpenAlexaffvenue
Zhou He, Kheng Lim Goh, Xianshe Feng, Kean Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Waterloo
FundersKhalifa University of Science, Technology and Research
KeywordsPermeationMembraneChemistryChromatographyMolecular sieveBinary numberThermodynamicsChemical engineeringAnalytical Chemistry (journal)Organic chemistryAdsorption

Abstract

fetched live from OpenAlex

Abstract Measuring permeation properties of gas mixtures is an important but challenging issue. This work reports on a novel time‐lag technique by selectively depositing (ie, desublimation) one of the gas components in a binary mixture at the downstream side using a liquid nitrogen cold trap, thereby allowing for determination of the permeation properties of both permeating components from the pressure response in the permeate chamber. The measurement technique was demonstrated for the permeation of several binary gas mixtures (H 2 /CO 2 , and H e /CO 2 with various compositions) through a carbon molecular sieve membrane. It was found that the true permeation properties of a binary mixture can be significantly different from the ideal values based on the permeation of pure gas components. This method is time and cost efficient and works well for binary gas mixtures where the difference in the boiling points of the gas components is sufficiently large so that one of the components in the permeate can be selectively desublimated at the permeate side.

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 score0.007
Threshold uncertainty score0.224

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 head0.198
Teacher spread0.187 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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