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Record W2746384065 · doi:10.11159/ffhmt17.195

Effect of Humidity on the Permeability of Alcohols in Hydroxylpropyl Xylan Films

2017· article· en· W2746384065 on OpenAlexaffvenue
Fatemeh Bayati, Yaman Boluk, Phillip Choi

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPermeability (electromagnetism)XylanHumidityMaterials scienceChemical engineeringComposite materialChemistryOrganic chemistryThermodynamicsEngineeringPhysicsMembrane

Abstract

fetched live from OpenAlex

Inverse gas chromatography (IGC) was used to study the effect of humidity on the permeability of selected low molecular weight alcohols (methanol, ethanol, propanol and butanol) through films made up of a hydrophilic polymer hydroxypropyl xylan (HPX). The thin films used consisted of a neat HPX film and a HPX film with sorbitol, a commonly used food grade plasticizer, (HPX/Sorbitol) and a HPX film with sorbitol and cellulose nanocrystal (CNC) (HPX/Sorbitol/CNC). Diffusivity and solubility of the alcohols at infinite dilution were measured at relative humidity (RH) values of 0%, 30%, 50% and 70% of the carrier gas (helium). Permeability was then calculated using the measured diffusivity and solubility. Diffusion coefficients of the alcohols showed a maximum at about 50% RH in all films. And solubility coefficients of the alcohols were more or less insensitive to the RH. Permeability coefficients of the alcohols exhibited a maximum at around 50% RH as well suggesting that diffusion, not dissolution, dominated the permeability behaviour. The RH dependence of diffusion, thereby permeability, was attributed to the combined effects of plasticizing, swelling, water clustering and alcohol-water clustering. The measured weight-fraction-based Henry's constants suggested that water contents in the films were relatively low, suggesting that water clustering might only take place in the neat HPX film at 70% RH.

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 score0.037
Threshold uncertainty score0.355

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 science0.0010.000
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 head0.019
GPT teacher head0.244
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

Citations0
Published2017
Admission routes2
Has abstractyes

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