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Record W2751172581 · doi:10.1103/physrevb.96.235201

NMR in an electric field: A bulk probe of the hidden spin and orbital polarizations

2017· article· en· W2751172581 on OpenAlexaff
Jorge Ramírez‐Ruiz, Samuel Boutin, Ion Garate

Bibliographic record

VenuePhysical review. B./Physical review. B · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité de Sherbrooke
Fundersnot available
KeywordsPoint reflectionSpintronicsCondensed matter physicsPolarization (electrochemistry)Inversion (geology)PhysicsHomogeneous spaceSpin polarizationElectric fieldSpin (aerodynamics)Symmetry (geometry)Nuclear magnetic resonanceChemistryQuantum mechanicsFerromagnetismElectronMathematicsGeometry

Abstract

fetched live from OpenAlex

The recent discovery of spin and orbital textures in nonmagnetic crystals with inversion symmetry has broadened the scope for spintronics applications. These so-called hidden polarizations are however difficult to probe, in part because they average to zero within each unit cell. In this work, the authors show that a bulk detection of intra-unit cell spin and orbital textures can be achieved with nuclear magnetic resonance by splitting, with an electric current, the resonance peak of inversion partner nuclei. The proposal is illustrated with numerical results for Bi${}_{2}$Se${}_{3}$ and Bi${}_{2}$Te${}_{3}$, and other promising materials are identified on the basis of their crystal symmetries.

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 score0.002
Threshold uncertainty score0.006

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.357
Teacher spread0.339 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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