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<sup>8</sup>Li βNMR studies of Epitaxial Thin Films of the 3D topological Dirac semimetal Sr<sub>3</sub>SnO

2023· article· en· W4362475289 on OpenAlexaff
W. A. MacFarlane, Mohamed Oudah, Ryan M. L. McFadden, Dennis Huang, Aris Chatzichristos, Derek Fujimoto, Victoria L. Karner, R. F. Kiefl, C. D. P. Levy, Ruohong Li, Iain McKenzie, G. D. Morris, M. R. Pearson, Monika Stachura, John O. Ticknor, Edward Thoeng, H. Nakamura, H. Takagi

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsTRIUMFUniversity of British Columbia
Fundersnot available
KeywordsOverlayerSemimetalCondensed matter physicsMagnetismSurface statesMaterials scienceDirac (video compression format)ChemistryBand gapPhysicsSurface (topology)

Abstract

fetched live from OpenAlex

Abstract The inverse perovskite Sr 3 SnO is a 3D cubic Dirac semimetal with a very small energy gap[1]. Its unusual electronic structure confers a variety of novel properties, such as chiral topological surface states, and very strong itinerant electron orbital magnetism. Remarkably, when doped it also becomes superconducting[2]. In the lowest carrier density samples, the Fermi level lies close to the Dirac points, and orbital magnetism is maximal. Here we report the results of ion-implanted 8 Li + β NMR in Au-capped epitaxial thin films of Sr 3 SnO as a function of carrier content. In addition, we stop the 8 Li in the Au overlayer to seek proximal evidence of the chiral surface state. In high magnetic field (6.55 T), we find remarkably little contrast in spin-lattice relaxation (SLR) between low carrier density Sr 3 SnO and the Au overlayer. In the inverse perovskite layer, 1/ T 1 ∼ 0.14 s -1 , slightly faster than Au at 300 K, while in the overlayer, there is a small but systematic enhancement in 1/ T 1 compared to a control film of Au. The resonance in the Sr 3 SnO layer is broad with a long tail towards negative shift without resolved quadrupolar splitting.

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)0.001
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 score0.017
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.270
Teacher spread0.228 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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