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Record W3096215033 · doi:10.1063/5.0015965

Rashba band splitting in two-dimensional Ruddlesden–Popper halide perovskites

2020· article· en· W3096215033 on OpenAlexafffund
Shidong Yu, Guangren Na, Shulin Luo, Oleg Rubel, Lijun Zhang

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMcMaster University
FundersMitacsNational Natural Science Foundation of China
KeywordsRashba effectOctahedronPerovskite (structure)Condensed matter physicsMonolayerHalideMaterials scienceCoupling (piping)PhysicsCrystallographyChemistrySpintronicsCrystal structureFerromagnetismNanotechnologyInorganic chemistry

Abstract

fetched live from OpenAlex

Due to the presence of heavy elements and the dynamic nature of hybrid halide perovskites, the strong spin–orbit coupling effect can give rise to Rashba band splitting in these materials. Despite many reports on the Rashba effect in 3D perovskites like CH3NH3PbI3, little is known about its presence in two-dimensional Ruddlesden–Popper (2DRP) perovskites. In this work, we use first-principle calculations to investigate the magnitude and origin of the Rashba effect in three families of 2DRP perovskites. We demonstrate the correlation between the splitting magnitude and the octahedron distortions. Moreover, different numbers of inorganic layers, spacer cations, and A-site cations have a great influence on the Rashba splitting through different mechanisms. While structures with C6H5C2H4NH3 (PEA) have a significant Rashba splitting only in the monolayer condition, C4H9NH3 (BA) induces large distortion by tilting the CH3NH3 (MA) cations around all octahedrons, giving rise to a larger Rashba splitting with an increasing number of inorganic layers. Our work elucidates the magnitude and origin of the Rashba splitting in 2DRP perovskites and provides guidelines for the manipulation of the Rashba splitting in these materials.

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 score0.260
Threshold uncertainty score0.558

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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