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Record W2805578946 · doi:10.1038/s41535-018-0110-7

Freezing out of a low-energy bulk spin exciton in SmB6

2018· article· en· W2805578946 on OpenAlexafffund
K. Akintola, Anand Pal, S. R. Dunsiger, Alex Fang, M. Potma, Shanta Saha, Xiangfeng Wang, Johnpierre Paglione, J. E. Sonier

Bibliographic record

Venuenpj Quantum Materials · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRare-earth and actinide compounds
Canadian institutionsCanadian Institute for Advanced ResearchKwantlen Polytechnic UniversitySimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityAir Force Office of Scientific ResearchTRIUMFUniversity of MemphisGordon and Betty Moore Foundation
KeywordsCondensed matter physicsMuon spin spectroscopyExcitonHyperfine structureKnight shiftSpin (aerodynamics)Relaxation (psychology)Materials scienceKondo insulatorPhysicsElectronKondo effectAtomic physicsSuperconductivityNuclear physics

Abstract

fetched live from OpenAlex

Abstract The Kondo insulator SmB6 is purported to develop into a robust topological insulator at low temperatures. Yet there are several puzzling and unexplained physical properties of the insulating bulk. It has been proposed that bulk spin excitons may be the source of these anomalies and may also adversely affect the topologically protected metallic surface states. Here, we report muon spin rotation measurements of SmB6 that show thermally activated behavior for the temperature dependence of the transverse-field relaxation rate below 20 K and a decreasing contact hyperfine field contribution to the positive muon Knight shift below 5–6 K. Our data are consistent with the freezing out of a bulk low-energy (~1 meV) spin exciton concurrent with the appearance of metallic surface conductivity. Furthermore, our results support the idea that spin excitons play some role in the anomalous low-temperature bulk properties of SmB6.

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 score0.005

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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