MétaCan
Menu
Back to cohort
Record W2023658138 · doi:10.1063/1.2200354

Effects of Zr doping on stoichiometric and reduced ceria: A first-principles study

2006· article· en· W2023658138 on OpenAlexaff
Zongxian Yang, Tom K. Woo, Kersti Hermansson

Bibliographic record

VenueThe Journal of Chemical Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDopingDensity functional theoryVacancy defectMaterials scienceNucleationAtom (system on chip)StoichiometrySupercellIonElectronic structureCrystallographyChemical physicsInorganic chemistryChemistryComputational chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

The Zr doping in CeO(2) may change the reduction properties and therefore the redox properties of CeO(2). Using first-principles density functional theory with the inclusion of on-site Coulomb interaction for a 96-atom supercell, these effects are studied by comparing the differences in atomic structures, electronic structures, and reduction energies of the doped CeO(2) and those of the nondoped CeO(2). It is found that (1) Zr doping of the ceria structure results in important modifications involving nonequivalent O atoms; (2) the oxygen anions (still four-coordinated) next to the doping center show considerably lower reduction energies (by 0.6 eV) and larger displacements ("higher mobilities"); (3) an O vacancy is most easily created close to the Zr centers, therefore the Zr-doping centers might serve as nucleation centers for vacancy clustering; and (4) the electrons left by the released oxygen localize on two Ce cations neighboring the vacancy, which results in the reduction of two Ce(4+) ions.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.249
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations156
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Chemical PhysicsSame topicCatalytic Processes in Materials ScienceFrench-language works237,207