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Record W2171954102 · doi:10.1002/app.10928

Permeation of mixtures of organic liquids through polymeric membranes: Role of liquid–liquid interactions

2002· article· en· W2171954102 on OpenAlexaff
Gérald Perron, Jacques E. Desnoyers, Jaime Lara

Bibliographic record

VenueJournal of Applied Polymer Science · 2002
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPermeationEndothermic processMembraneExothermic reactionEnthalpyChemistryThermodynamicsSolventMixing (physics)Materials scienceOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Abstract The permeation of pure organic liquids and mixtures of organic liquids through commercial butyl, neoprene, and nitrile membranes was studied using dynamic material deformation (swelling) and permeation techniques. The derived parameters, the breakthrough time ( t BT ), steady‐state permeation rate (SSPR), and initial swelling rate (SR), show deviations from additivity for the mixtures, based on the parameters of the pure liquids on a mol fraction basis. In the majority of cases for the three membranes examined, the deviations are independent of the nature of the membranes, and the signs of the deviations for t BT are opposite to those for SSPR or SR, provided that the membranes are not degraded by one of the solvents. An approach that considers only solvent–solvent interactions based on the enthalpy of mixing was used to predict deviations for mixtures. For mixtures where the enthalpy of mixing is large and exothermic, the permeation of the mixture is less than expected, while for systems where the enthalpy of mixing is large and endothermic, the permeation is larger than expected. A simple semiempirical model predicts the sign and magnitude of the permeation of 73% of the system–permeation property combinations investigated, which show significant deviations from ideality. It is interesting to note that the wrong predictions are for systems where the predictions are positive, that is, for SSPR and SR rates with endothermic systems and for t BT with exothermic systems. The exceptions also seem to be for systems that correspond to materials having a high resistance to one of the solvents and a very low resistance to the other solvent. Examples of ternary–mixture permeation data are also given and show that, even if two of the pure components do not permeate through a membrane, the membrane will offer little protection if the third component shows a high affinity for the membrane and if the enthalpies of mixing of this component with the other liquids are endothermic. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 195–215, 2002

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 score0.019
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations12
Published2002
Admission routes1
Has abstractyes

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