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Record W2963829757 · doi:10.1021/jp411219z

Local Structure of Fe Impurity Atoms in ZnO: Bulk versus Surface

2014· article· en· W2963829757 on OpenAlexafffund
John A. McLeod, Danil W. Boukhvalov, D. A. Zatsepin, R. J. Green, Brett Leedahl, L. Cui, E.Z. Kurmaev, Ivan S. Zhidkov, L. D. Finkelstein, Н. В. Гаврилов, S. O. Cholakh, A. Moewes

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Saskatchewan
FundersBasic Energy SciencesRussian Academy of SciencesWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Science CouncilOffice of ScienceRussian Foundation for Basic ResearchUniversity of SaskatchewanCanadian Light SourceU.S. Department of Energy
KeywordsIsostructuralImpurityFerromagnetismMaterials scienceSubstitution (logic)DopingDensity functional theoryInterstitial defectOxygenCrystallographyPhase (matter)Chemical physicsCondensed matter physicsChemistryComputational chemistryCrystal structure

Abstract

fetched live from OpenAlex

By studying Fe-doped ZnO pellets and thin films with various X-ray spectroscopic techniques, and complementing this with density functional theory calculations, we find that Fe-doping in bulk ZnO induces isovalent (and isostructural) cation substitution (Fe2+ → Zn2+). In contrast to this, Fe-doping near the surface produces both isovalent and heterovalent substitution (Fe3+ → Zn2+). The calculations performed herein suggest that the most likely defect structure is the single or double substitution of Zn with Fe, although, if additional oxygen is available, then Fe substitution with interstitial oxygen is even more energetically favorable. Furthermore, it is found that ferromagnetic states are energetically unfavorable, and ferromagnetic ordering is likely to be realized only through the formation of a secondary phase (i.e., ZnFe2O4), or codoping with Cu.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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.

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

Citations18
Published2014
Admission routes2
Has abstractyes

Explore more

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