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Record W2333834903 · doi:10.1149/05027.0015ecst

Synthesis and Characterization of Nanosized (DyO<sub>1.5</sub>)<sub>x</sub>(WO<sub>3</sub>)<sub>y</sub>(BiO<sub>1.5</sub>)<sub>1-x-y</sub> for Lower Temperature SOFC Application

2013· article· en· W2333834903 on OpenAlexfundno aff
Ashley A. Lidie, Kang Taek Lee, Eric D. Wachsman

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsnot available
FundersRyerson UniversityAmerican Society for Engineering Education
KeywordsCalcinationCoprecipitationMaterials scienceCathodeIonic conductivityAnalytical Chemistry (journal)FluoriteIonic bondingConductivityPhase (matter)Nuclear chemistryMineralogyInorganic chemistryIonPhysical chemistryChemistryElectrolyteMetallurgyElectrodeCatalysisChromatography

Abstract

fetched live from OpenAlex

The high ionic conductivity (DyO1.5)x(WO3)y(BiO1.5)1-x-y (DWSB) material was synthesized using a coprecipitation method. Adding the acid solution containing Dy(NO3)3 and Bi(NO3)3 to the basic solution containing (NH4)10W12O41•5H2O, a DWSB precursor was formed. Upon calcination, nano-sized (DyO1.5)x(WO3)y(BiO1.5)1-x-y powder with the appropriate fluorite phase is generated. This nanopowder was incorporated into a composite cathode with La0.80Sr0.20MnO3-δ (LSM) using the glycine-nitrate process to coat the DWSB with the LSM. The two phases are mixed at the nano-scale and no reactivity between the two phases is observed. The ASR values for this cathode (0.17 Ω-cm2 at 600⁰C) is reduced by a half order of magnitude compared with the same composition mixed with solid state methods (0.45 Ω-cm2 at 600⁰C). Furthermore, the ASR values are significantly reduced from composite cathodes of LSM and common ionic conductors, namely LSM-GDC (1.3 Ω-cm2 at 600⁰C) and LSM-YSZ (1.5 Ω-cm2 at 600⁰C).

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 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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207