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Record W2753012519 · doi:10.1021/acs.nanolett.7b02882

Building Complex Kondo Impurities by Manipulating Entangled Spin Chains

2017· article· en· W2753012519 on OpenAlexafffund
Deung-Jang Choi, Roberto Robles, Shichao Yan, Jacob A. J. Burgess, Steffen Rolf-Pissarczyk, J. P. Gauyacq, Nicolás Lorente, Markus Ternes, Sebastian Loth

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y CompetitividadEuropean Regional Development FundDeutsche ForschungsgemeinschaftAlexander von Humboldt-Stiftung
KeywordsImpurityKondo effectCondensed matter physicsSpin (aerodynamics)Kondo insulatorKondo modelPhysicsMaterials scienceQuantum mechanicsThermodynamics

Abstract

fetched live from OpenAlex

The creation of molecule-like structures in which magnetic atoms interact controllably is full of potential for the study of complex or strongly correlated systems. Here, we create spin chains in which a strongly correlated Kondo state emerges from magnetic coupling of transition-metal atoms. We build chains up to ten atoms in length by placing Fe and Mn atoms on a Cu 2 N surface with a scanning tunneling microscope. The atoms couple antiferromagnetically via superexchange interaction through the nitrogen atom network of the surface. The emergent Kondo resonance is spatially distributed along the chain. Its strength can be controlled by mixing atoms of different transition metal elements and manipulating their spatial distribution. We show that the Kondo screening of the full chain by the electrons of the nonmagnetic substrate depends on the interatomic entanglement of the spins in the chain, demonstrating the prerequisites to build and probe spatially extended strongly correlated nanostructures.

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 score0.443
Threshold uncertainty score0.746

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 studies0.0010.000
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

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