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Record W2970889834 · doi:10.1149/09101.2673ecst

Theoretical Investigation of CO<sub>2</sub> Reduction at Ni/SDC and La(Sr)FeO<sub>3-δ </sub>Cathodes in Solid Oxide Electrolysis Cells

2019· article· en· W2970889834 on OpenAlexafffund
Bohua Ren, Luis Ricardez‐Sandoval, Eric Croiset

Bibliographic record

VenueECS Transactions · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsElectrolysisOxideElectrochemistryAdsorptionMaterials sciencePerovskite (structure)OxygenCathodeCatalysisDensity functional theoryInorganic chemistryChemical engineeringChemistryPhysical chemistryElectrodeMetallurgyComputational chemistry

Abstract

fetched live from OpenAlex

Electrochemical CO2 reduction in solid oxide electrolysis cell (SOEC) is a promising technology to address the global issue of greenhouse emissions. To further advance the development of catalyst, it is necessary to gain theoretical insights into high temperature CO2 electroreduction mechanisms on perovskite La(Sr)FeO3-δ and conventional Ni/SDC using Density Functional Theory (DFT). To study the effects of interface oxygen vacancy on CO2 electrolysis on Ni/SDC, surface models with and without interface oxygen vacancy were considered. In addition, the most stable La(Sr)FeO3-δ surface model under SOEC operation conditions with 4 oxygen vacancies was also built. CO2 reduction reaction is most favorable for the strongest CO2 adsorption on Ni/SDC (111) surface, while on La0. 5Sr0. 5FeO2.75 (001) surface, this reaction is most favorable for moderate CO2 adsorption. The adsorption configurations of CO2 and CO that would make CO2 electrolysis most likely to occur were determined for each of the surface models.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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 teacher head, not a consensus.

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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

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