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Record W2591587579 · doi:10.1149/ma2016-02/39/2960

In-Situ Exsolved Co-Fe Alloy Nanoparticles on Double Layered Perovskite for the Cogeneration of Elethylene and Electricity in Proton Conducting Fuel Cell

2016· article· en· W2591587579 on OpenAlexaff
Subiao Liu, Jing‐Li Luo

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDehydrogenationMaterials scienceChemical engineeringCatalysisInorganic chemistryHydrogenAnodeOxideChemistryOrganic chemistryMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Ethylene plays a crucial role in modern society. Currently, tube furnace steam cracking is the dominant technology in ethylene production, and about 99% of global ethylene production employs the tube furnace pyrolysis method [1]. However, the cracking reactions are highly endothermic, reversible, and severely limited by thermodynamic equilibrium. Moreover, CO and CO2 are largely formed because of the existence of oxygen sources [2]. According to the mechanism of ethane dehydrogenation, if one of the products, hydrogen, can be selectively removed from the reaction system, the conversion is no longer limited by thermodynamic equilibrium, allowing ethane conversion rate to increase at a lower temperature and thus, making the ethylene production more economical. In response to the critical need for a cleaner energy technology, proton conducting solid oxide fuel cell (PCFC) has been investigated as a potential candidate for the ethane dehydrogenation. Desired chemicals (ethylene) and electricity can be produced simultaneously. For a typical PCFC for ethane dehydrogenation, ethane is dehydrogenated to ethylene at anode, protons from hydrogen pass through the proton conducting electrolyte to react with the oxygen ions at cathode. However, anode catalysts reported in PCFC have not met the requirements of excellent electrochemical performance and high ethylene yield. Therefore, it is of great interest to develop new catalysts with excellent catalytic activity and good coking tolerance for the ethane dehydrogenation [3]. Recently, double layered perovskites have been investigated as the anode materials due to their good stability and high mixed ionic and electronic conductivity for the partial oxidation of hydrocarbons [4]. Also, it is known that Co-Fe bi-metallic alloy is an excellent electrochemical catalyst, and has been widely used as a catalyst in fuel cell anode materials. Moreover, both Co and Fe have been utilized as effective alloying elements to enhance the performance of anode materials and the combination of Co-Fe alloy catalyst favors the formation of C2-C4 alkenes [5]. In this work, in-situ exsolved Co-Fe alloy nanoparticles uniformly distributed on a double layered perovskite (Pr0.4Sr0.6)3(Fe0.85Mo0.15)2O7 (CoFe-PSFM) anode backbone by reducing the cubic perovskite Pr0.4Sr0.6Co0.2Fe0.7Mo0.1O3-δ (PSCFM) in a 10% H2/N2 atmosphere was synthesized at 900 °C. It was fabricated as the anode in a BaCe0.7Zr0.1Y0.2O3-δ (BCZY) electrolyte-supported PEFC. The maximum output power densities of 348.84 mW cm−2 in C2H6 and 496.2 mW cm−2 in H2 were achieved at 750 °C. More importantly, a high ethylene yield, increasing from 13.2% at 650 °C to 41.5% at 750 °C with a remarkable ethylene selectivity over 91% and no CO2 emission, was achieved due to the considerably efficient catalysis of the in-situ Co-Fe alloy nanoparticles which were homogeneously distributed on the PSFM backbone. Furthermore, galvanic static test up to 100 h under a constant current load of 0.65 A cm-2 showed no detectable degradation. The results clearly indicate that the CoFe-PSFM anode material possesses high ethane partial dehydrogenation activity, enhanced electro-catalytic activity, and good stability. Based on its remarkable performance in cogeneration of electricity and ethylene in PCFC, CoFe-PSFM ceramic material is an attractive anode for a directly hydrocarbon fueled solid oxide fuel cell (SOFC). Figure 1

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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)0.0010.000

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 head0.029
GPT teacher head0.259
Teacher spread0.230 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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