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Record W2945253667 · doi:10.1002/admi.201900547

Progress and Perspectives of Atomically Engineered Perovskite Oxide Interfaces for Electronics and Electrocatalysts

2019· article· en· W2945253667 on OpenAlexafffund
Yunzhong Chen, Robert J. Green

Bibliographic record

VenueAdvanced Materials Interfaces · 2019
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsUniversity of SaskatchewanUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeDanmarks Frie ForskningsfondEuropean Commission
KeywordsMaterials sciencePerovskite (structure)ElectronicsOxideNanotechnologyComplex oxideEngineering physicsChemical engineeringMetallurgyElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The 2D electron gases (2DEGs) at the surfaces and interfaces of SrTiO3‐based homo‐ and heterostructures provide new opportunities for electronics and spintronics. Herein, the recent progresses in the creation of advanced material systems of conducting oxide interfaces with engineered electronic reconstructions and redox reactions, as well as their characterization by a nondestructive resonant X‐ray reflectivity technique, are summarized. Moreover, the development of modulation‐doped high‐mobility oxide 2DEGs and the magnetic‐proximity‐induced spin‐polarized 2DEGs at oxide interfaces are also discussed. Finally, the perspectives on design of conducting oxide interfaces for a new generation of quantum devices and mixed electronic and ionic conducting electrocatalysts are addressed. Atomically engineered oxide interfaces will represent a unique family of quantum materials for future information and energy technologies.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.230
Teacher spread0.223 · 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
GenreReview

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

Citations25
Published2019
Admission routes2
Has abstractyes

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