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Record W2167426482 · doi:10.1002/polb.10129

Sorption and diffusion of carbon dioxide in wood‐fiber/polystyrene composites

2002· article· en· W2167426482 on OpenAlexaff
Saeed Doroudiani, Charles E. Chaffey, Mark T. Kortschot

Bibliographic record

VenueJournal of Polymer Science Part B Polymer Physics · 2002
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSorptionThermal diffusivitySolubilityPolystyreneDiffusionDesorptionMaterials scienceSaturation (graph theory)Composite materialFiberMass diffusivityLangmuirThermodynamicsAdsorptionChemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this study, sorption and diffusion of carbon dioxide (CO2) in wood‐fiber/polystyrene composites were investigated. The effects of gas pressure and fiber content on the solubility and diffusion coefficients were evaluated. A statistical analysis indicated that pressure is more important than fiber content in determining the solubility and diffusivity of CO2. An increase in saturation pressure causes an increase in the solubility and diffusion coefficients, whereas inclusion of the fibers decreases both of these properties. Models were developed to predict the uptake and diffusion coefficients of CO2 in the composite samples as functions of pressure and fiber content. A theoretical model based on Henry's law and the Langmuir equation compared favorably to the experimental data for CO2 solubility. This dual mode model also described both the transient sorption and desorption data, but only if the concentration‐dependent value of diffusivity was treated as a history‐dependent parameter. © 2002 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 40: 723–735, 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2002
Admission routes1
Has abstractyes

Explore more

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