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Record W2620633844 · doi:10.1063/1.4985048

<i>δ</i>-Doping of oxygen vacancies dictated by thermodynamics in epitaxial SrTiO3 films

2017· article· en· W2620633844 on OpenAlexaff
Fengmiao Li, Fang Yang, Yan Liang, Shanming Li, Zhenzhong Yang, Qinghua Zhang, Wentao Li, Xuetao Zhu, Lin Gu, Jiandi Zhang, E. W. Plummer, Jiandong Guo

Bibliographic record

VenueAIP Advances · 2017
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsUniversity of British Columbia
FundersMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMolecular beam epitaxyDopingEpitaxyOxygenOxideMaterials scienceSubstrate (aquarium)Lattice (music)Chemical physicsCondensed matter physicsThermodynamicsChemistryNanotechnologyOptoelectronicsMetallurgyPhysicsGeology

Abstract

fetched live from OpenAlex

Homoepitaxial SrTiO3(110) film is grown by molecular beam epitaxy in ultra-high vacuum with oxygen diffusing from substrate as the only oxidant. The resulted oxygen vacancies (VOs) are found to be spatially confined within few subsurface layers only, forming a quasi-two-dimensional doped region with a tunable high concentration. Such a δ-function distribution of VOs is essentially determined by the thermodynamics associated with the surface reconstruction, and facilitated by the relatively high growth temperature. Our results demonstrate that it is feasible to tune VOs distribution at the atomic scale by controlling the lattice structure of oxide surfaces.

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 score0.002

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)0.0000.000

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 head0.009
GPT teacher head0.247
Teacher spread0.239 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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