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Record W4282931886 · doi:10.1116/6.0001736

High ionic conductivity of ultralow yttria concentration yttria-stabilized zirconia thin films

2022· article· en· W4282931886 on OpenAlexafffund
Wenfei Zhang, Bin Hua, Mengmeng Miao, Ken Cadien, Jing‐Li Luo

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsYttria-stabilized zirconiaMaterials scienceYttriumCubic zirconiaIonic conductivityCrystallinityAnnealing (glass)Grain sizeMicrostructureConductivityThin filmElectrical resistivity and conductivityChemical engineeringComposite materialAnalytical Chemistry (journal)MineralogyNanotechnologyMetallurgyCeramicChemistryOxidePhysical chemistryElectrolyte

Abstract

fetched live from OpenAlex

This paper investigates the ionic conductivity of ultralow yttria concentration (<2 mol. %) yttria-stabilized-zirconia (YSZ) thin films synthesized by atomic layer deposition (ALD). With our ALD recipe, yttria is homogeneously distributed among zirconia, and its concentration is controlled by the pulse time of the yttrium precursor. High conductivity values are observed at test temperatures (400, 500, and 600 °C). 1.6YSZ exhibits a conductivity of 0.02 S cm−1 at 600 °C and an activation energy of 0.98 eV. In order to relate the electrical property, atomic force microscope and x-ray diffraction are used to study the crystallinity and microstructure. The true size effect is considered to be responsible for the outstanding electrical property. Finally, the effects of YSZ thin film thickness and annealing process on their conductivities are studied. The true size effect is weakened by an increase in grain size from annealing or higher thickness, leading to reduced ionic conductivities.

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)0.004
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 score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Vacuum Science & Technology A Vacuum Surfaces and FilmsSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207