MétaCan
Menu
Back to cohort
Record W4206446093 · doi:10.1088/1361-648x/ac4c61

Strain and orientation engineering in ABO<sub>3</sub> perovskite oxide thin films

2022· article· en· W4206446093 on OpenAlexaff

Bibliographic record

VenueJournal of Physics Condensed Matter · 2022
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsKensington Health
FundersAustralian Research Council
KeywordsPerovskite (structure)Thin filmStrain engineeringSubstrate (aquarium)OxideEpitaxyOrientation (vector space)Octahedron

Abstract

fetched live from OpenAlex

are widely studied for their properties including ferroelectricity, magnetism, strongly correlated physics, optical effects, and superconductivity. A thriving research direction using such materials is through their integration as epitaxial thin films, allowing many novel and exotic effects to be discovered. The integration of the thin film on a single crystal substrate, however, can produce unique and powerful effects, and can even induce phases in the thin film that are not stable in bulk. The substrate imposed mechanical boundary conditions such as strain, crystallographic orientation, octahedral rotation patterns, and symmetry can also affect the functional properties of perovskite films. Here, the author reviews the current state of the art in epitaxial strain and orientation engineering in perovskite oxide thin films. The paper begins by introducing the effect of uniform conventional biaxial strain, and then moves to describe how the substrate crystallographic orientation can induce symmetry changes in the film materials. Various material case studies, including ferroelectrics, magnetically ordered materials, and nonlinear optical oxides are covered. The connectivity of the oxygen octahedra between film and substrate depending on the strain level as well as the crystallographic orientation is then discussed. The review concludes with open questions and suggestions worthy of the community's focus in the future.

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)0.001
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 score0.548

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations51
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Condensed MatterSame topicFerroelectric and Piezoelectric MaterialsFrench-language works237,207