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Record W2775269038 · doi:10.1002/cjce.23098

Modelling of water transport through mixed‐ion conducting dense ceramics

2017· article· en· W2775269038 on OpenAlexvenueno aff
Selgin Al, Feng Song, Guangru Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsnot available
Fundersnot available
KeywordsMultiphysicsDiffusionBoundary value problemMaterials scienceMass transferFokker–Planck equationConvection–diffusion equationNernst equationMechanicsTransport phenomenaIonCharge (physics)PhysicsThermodynamicsChemistryPartial differential equationFinite element methodPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract This study develops and demonstrates a model that characterizes defect transports, responsible for water transport within dense ceramics, and calculates the diffusion coefficients for those defects. The multi‐species mass transfer processes within yttrium doped barium cerates are modelled by applying the Nernst‐Planck equation to the system. The Nernst‐Planck equation with suitable boundary conditions is adopted to compute defect diffusion coefficients in COMSOL Multiphysics. All related equations, based on charge and defect conservation, are solved numerically and validated experimentally. The model also predicts the concentration distribution of the defects and potential profiles throughout the membranes. The results provided convenient insights about the water transport and charge distribution as a function of membrane thickness.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.244
Teacher spread0.194 · 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 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
Published2017
Admission routes1
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207