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Record W3089301772 · doi:10.1073/pnas.2012980117

Tunable layered-magnetism–assisted magneto-Raman effect in a two-dimensional magnet CrI <sub>3</sub>

2020· article· en· W3089301772 on OpenAlexafffund
Wencan Jin, Zhipeng Ye, Xiangpeng Luo, Bowen Yang, Gaihua Ye, Fangzhou Yin, Hyun Ho Kim, Laura Rojas, Shangjie Tian, Fu Yang, Shaohua Yan, Hechang Lei, Kai Sun, Adam W. Tsen, Rui He, Liuyan Zhao

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersArmy Research OfficeFundamental Research Funds for the Central UniversitiesU.S. ArmyRenmin University of ChinaNational Natural Science Foundation of ChinaDivision of Materials ResearchCanada First Research Excellence FundNational Science Foundation
KeywordsMagnetismRaman spectroscopyCondensed matter physicsMultipletMagneto opticalAntiferromagnetismPhononMagnetRaman scatteringPolarization (electrochemistry)Magnetic fieldCoupling (piping)Materials sciencePhysicsChemistryOpticsSpectral lineQuantum mechanics

Abstract

fetched live from OpenAlex

Significance The two-dimensional (2D) magnetic semiconductor CrI 3 hosts a variety of strong and tunable magneto-optical effects and allows for the development of novel magneto-optical devices. While the elastic magneto-optical effects in CrI 3 are well understood, its recently discovered inelastic magneto-Raman effect remains to have case-specific interpretations varying upon the thickness of CrI 3 . We perform comprehensive Raman measurements on 2D CrI 3 with polarization, temperature, layer number, and magnetic field dependence. We resolve a Davydov-split–induced N -fold multiplet in N -layer CrI 3 and reveal the distinct magneto-Raman behaviors of individual phonons within the multiplet. Our results discover a layered-magnetism–coupled phonon scattering mechanism that explains the rich magneto-Raman effect in CrI 3 of arbitrary thickness and elucidates the spin–phonon coupling physics in layered magnets.

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)0.002
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 score0.028
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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

Citations40
Published2020
Admission routes2
Has abstractyes

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