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Record W4281801796 · doi:10.1002/aenm.202200899

A Novel Solid Oxide Electrolysis Cell with Micro‐/Nano Channel Anode for Electrolysis at Ultra‐High Current Density over 5 A cm<sup>−2</sup>

2022· article· en· W4281801796 on OpenAlexaff
Junwen Cao, Yifeng Li, Yun Zheng, Shubo Wang, Wenqiang Zhang, Xiangfu Qin, Ga Geng, Bo Yu

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsAnodeElectrolysisMaterials scienceElectrolyteCurrent densityElectrolytic cellOxideChemical engineeringHydrogen productionCurrent (fluid)ElectrodeOxygen evolutionNanotechnologyHydrogenElectrochemistryMetallurgyChemistryThermodynamicsPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Solid oxide electrolysis cells (SOECs) are regarded as promising candidates for the next generation of energy conversion device due to their extremely high conversion efficiency. However, the electrolysis current density should be further increased to meet the demands of large‐scale industrial application in the future. Here, a novel anode configuration for SOEC is reported that achieves an ultra‐high current density of 5.73 A cm −2 for at least 4 h. To the best of the authors’ knowledge, such current density surpasses the record of present state‐of‐the‐art research. The unique stability under such high current density is attributed to the novel SOEC constructed with a La 0.6 Sr 0.4 CoO 3− δ anode catalyst nano‐layer to promote oxygen generation, vertically aligned micro channel anode scaffold to accelerate oxygen release, and integrated anode–electrolyte interface to improve the interfacial strength. The novel SOEC successfully demonstrates stable steam electrolysis under an ultra‐high current density of 5.96 A cm −2 at 800 °C under the constant operating voltage of 1.3 V, corresponding to a hydrogen production rate of 2.5 L h −1 cm −2 .

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), Insufficient payload (model declined to judge)
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 score0.106
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.234
Teacher spread0.226 · 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

Citations42
Published2022
Admission routes1
Has abstractyes

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