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Record W4401123816 · doi:10.3390/membranes14080167

Revisiting the Effect of the Resistance to Gas Accumulation in Constant Volume Systems on the Membrane Time Lag

2024· article· en· W4401123816 on OpenAlexafffund
Peter Leszczynski, S. Lashkari, Boguslaw Kruczek

Bibliographic record

VenueMembranes · 2024
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsBurnaby HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLagTime lagConstant (computer programming)Volume (thermodynamics)Lag timeResistance (ecology)MembraneThermodynamicsMechanicsChemistryMaterials scienceEnvironmental scienceComputer scienceBiological systemBiologyPhysicsEcologyBiochemistry

Abstract

fetched live from OpenAlex

The time-lag method is commonly used to determine membrane permeability, diffusivity and solubility in a single gas permeation experiment in a constant volume system. An unwritten assumption on which this method relies is that there is no resistance to gas accumulation in the downstream receiver of the system. However, this is not the case, even with the specially designed receiver used in this study when, in addition to tubing, the receiver utilizes an additional accumulation tank. The resistance to gas accumulation originates from a finite diffusivity (Knudsen diffusion) of gases in tubing, which are magnified by “resistance-free” accumulation tank(s). As a result of the resistance to gas accumulation, the time lag of the membrane is underestimated, which leads to an overestimation of gas diffusivity in the membrane. The experimentally predicted resistances in different configurations of the receiver, expressed by the difference in the time lag at two different receiver locations, were several times greater than the theoretically predicted values. A high molecular PPO membrane was used to demonstrate this effect. The time lags measured at different locations differed by as much as 30%. The diffusivity of nitrogen in a PPO of 4.04 × 10−12 m2/s determined at the optimum configuration of the receiver is at least 50% lower than the literature-reported values.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.239
Teacher spread0.228 · 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 designSimulation or modeling
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
Published2024
Admission routes2
Has abstractyes

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