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Record W4412755497 · doi:10.1002/smll.202506910

B‐Site Engineering in Ba‐Based Perovskites via Solid‐State Synthesis Unlocks High Power Density in SOFCs

2025· article· en· W4412755497 on OpenAlexaff
Sefiu Abolaji Rasaki, Mmakeng John Otsweleng, Hassan Idris Abdu, Jean Pierre Mwizerwa, Qasim Khan

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCathodeMaterials scienceMicrostructureOxideElectrochemistryChemical engineeringPerovskite (structure)OxygenDopingPower densityFuel cellsThermal stabilitySolid oxide fuel cellNanotechnologyComposite materialElectrodeMetallurgyPhysical chemistryOptoelectronicsChemistryThermodynamicsAnodePower (physics)

Abstract

fetched live from OpenAlex

Abstract This study introduces three novel perovskite‐based cathode materials; Ba 3 CoNb 2 O 9 , Ba 3 FeNb 2 O 9 , and Ba 6 CoFeNb 9 O 30 , synthesized via a solid‐state reaction using camphor as a pore‐former. The suitability of the cathode materials for solid oxide fuel cells (SOFCs) is evaluated at temperatures ≤700 °C. The influence of surface and lattice oxygen content, along with B‐site doping effects, is systematically analyzed in relation to their electrochemical behaviors. At temperatures below 550 °C, Ba 3 CoNb 2 O 9 and Ba 3 FeNb 2 O 9 exhibit ≈1.5 times higher power densities than Ba 6 CoFeNb 9 O 30 , attributed to superior surface area. At higher temperatures (≥600 °C), Ba 6 CoFeNb 9 O 30 demonstrates superior performance due to more efficient surface oxygen utilization. All three materials benefit from high thermal stability and electrochemically active compositions, achieving power outputs surpassing many existing SOFC cathodes. This study highlights the importance of carefully engineered cathode composition and microstructure in developing advanced materials for efficient and stable SOFC operation at reduced temperatures.

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 score0.081
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 studies0.0000.000
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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