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Record W2133470217 · doi:10.1039/c1cp22837d

CO2 emission free co-generation of energy and ethylene in hydrocarbon SOFC reactors with a dehydrogenation anode

2011· article· en· W2133470217 on OpenAlexafffund
Xian‐Zhu Fu, Jieyuan Lin, Shihong Xu, Jing‐Li Luo, Karl T. Chuang, Alan R. Sanger, A. Krzywicki

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsNova Chemicals (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsDehydrogenationAnodeMaterials scienceElectrolyteHydrocarbonCatalysisSolid oxide fuel cellChemical engineeringProton conductorInorganic chemistryOxideEthyleneElectrochemistryChemistryOrganic chemistryMetallurgyElectrode

Abstract

fetched live from OpenAlex

A dehydrogenation anode is reported for hydrocarbon proton conducting solid oxide fuel cells (SOFCs). A Cu-Cr(2)O(3) nanocomposite is obtained from CuCrO(2) nanoparticles as an inexpensive, efficient, carbon deposition and sintering tolerant anode catalyst. A SOFC reactor is fabricated using a Cu-Cr(2)O(3) composite as a dehydrogenation anode and a doped barium cerate as a proton conducting electrolyte. The protonic membrane SOFC reactor can selectively convert ethane to valuable ethylene, and electricity is simultaneously generated in the electrochemical oxidative dehydrogenation process. While there are no CO(2) emissions, traces of CO are present in the anode exhaust when the SOFC reactor is operated at over 700 °C. A mechanism is proposed for ethane electro-catalytic dehydrogenation over the Cu-Cr(2)O(3) catalyst. The SOFC reactor also has good stability for co-generation of electricity and ethylene at 700 °C.

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 score0.001
Threshold uncertainty score0.003

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)0.0000.000

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations22
Published2011
Admission routes2
Has abstractyes

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