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Record W2053588814 · doi:10.1002/cjce.22133

Electrochemical performance of a fluidized bed electrode fuel cell with molten carbonate electrolyte

2014· article· en· W2053588814 on OpenAlexvenueno aff
Jubing Zhang, Zhaoping Zhong, Guilin Piao, Hongmin Yang, Xiaoxiang Jiang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsElectrolyteElectrochemistryElectrodeMaterials scienceFuel cellsFluidized bedCarbonateMolten carbonate fuel cellChemical engineeringMolten saltMetallurgyWaste managementChemistryEngineering

Abstract

fetched live from OpenAlex

Molten carbonate fuel cell (MCFC) has been extensively studied and vigorously developed. However, the shortage in gas‐diffusion electrode limits its widely application to a large extent. Fluidized bed electrode (FBE) has been paid more and more attention all around the world due to its high mass and heat transfer coefficient. In this study, a novel fuel cell combined MCFC and FBE is proposed, which is called fluidized bed electrode fuel cell (FBEFC). Its polarization characteristics are fundamentally investigated under various experimental conditions. The optimal performance of the FBEFC is obtained under the following conditions with an open circuit voltage of 0.987 V and a peak power density of 39.6 mW cm −2 : electrolyte composition, 62 % Li 2 CO 3 : 38 % K 2 CO 3 ; O 2 /CO 2 ratio, 1/2; reaction temperature, 923 K; anode gas flow rate, 402 ml min −1 ; and cathode gas flow rate, 1723 ml min −1

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.002
GPT teacher head0.132
Teacher spread0.130 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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