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Record W7116083208 · doi:10.1021/acscatal.5c07349

N-Doped-Induced Local Covalency Elevation for Enhancing Cathodic Performance of Solid Oxide Electrolysis Cells

2025· article· en· W7116083208 on OpenAlexaff

Bibliographic record

VenueACS Catalysis · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsMinistry of Agriculture
FundersNational Key Research and Development Program of ChinaShanghai Jiao Tong UniversityScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsOxideElectrolysisCathodeElectrochemistryCurrent densityPolarization (electrochemistry)Density functional theoryCathodic protectionConductivity

Abstract

fetched live from OpenAlex

The Ni-based cathode is central to the performances of solid oxide electrolysis cells (SOECs), yet it suffers from poor oxygen ion conductivity, sluggish electron transport, and inefficient CO 2 /H 2 O activation. This study explores a heteroatom-doping strategy to comprehensively address the ionic, electronic, and molecular issues in solid oxide cells. When operated in SOEC mode, the maximum power density of the N-doped Ni/CGO (NiO/CGON) cathode achieved a 29.6% improvement over its undoped Ni/CGO, along with a 27.3% reduction in polarization resistance. Moreover, a 31.3% increase in maximum current density was obtained along with considerable stable operation over 150 h at an industrial-scale current density of 0.5 A/cm 2 . Combined electrochemical measurements, in situ diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, and density functional theory (DFT) simulations reveal that N-doped-induced local covalency elevation via the formation of Ce–O/N bonds substantially promotes the oxygen ion and electron conductivity and creates the synergistic Lewis acid–base sites for simultaneous activation of both CO 2 and H 2 O, thereby collectively addressing the ionic, electronic, and molecular issues in SOCs in one simple method.

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 score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

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