MétaCan
Menu
Back to cohort
Record W2329871509 · doi:10.1149/05701.2527ecst

Microstructural Modeling and Effective Properties of Infiltrated SOFC Electrodes

2013· article· en· W2329871509 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsInfiltration (HVAC)Monte Carlo methodComposite numberMaterials scienceElectrodeComposite materialChemistryMathematics

Abstract

fetched live from OpenAlex

A modeling framework for the microstructural modeling of infiltrated SOFC electrodes is presented. The model numerically reconstructs infiltrated electrodes through a sedimentation algorithm for the backbone generation and a novel Monte Carlo packing algorithm for the random infiltration. Effective properties are evaluated by means of Monte Carlo geometric analysis and finite volume method as a function of the loading and of the particle size of infiltrated particles. Infiltration into ion-conducting and composite backbones is analyzed in this study. Simulations show that the infiltration can lead to an increase in TPB density of about two orders of magnitude if compared with conventional composite electrodes. In addition, infiltration into monocomponent backbones can lead to a TPB density about twice the TPB achievable when infiltrating composite backbones. On the other hand, a critical loading of nanoparticles must be reached in monocomponent backbones while in a composite backbone the infiltration is always beneficial.

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.

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 score0.013
Threshold uncertainty score0.264

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 science0.0000.000
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 head0.005
GPT teacher head0.163
Teacher spread0.158 · 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