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Record W1531943648 · doi:10.1103/physrevb.86.024502

Neutron scattering studies of spin excitations in superconducting Rb<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mrow/><mml:mrow><mml:mn>0.82</mml:mn></mml:mrow></mml:msub></mml:math>Fe<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mrow/><mml:mrow><mml:mn>1.68</mml:mn></mml:mrow></mml:msub></mml:math>Se<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mrow/><mml:mn>2</mml:mn></mml:msub></mml:math>

2012· article· lv· W1531943648 on OpenAlexaff
Miaoyin Wang, Chunhong Li, D. L. Abernathy, Yu Song, Scott Carr, Xingye Lu, Shiliang Li, Z. Yamani, Jiangping Hu, Tao Xiang, Pengcheng Dai

Bibliographic record

VenuePhysical Review B · 2012
Typearticle
Languagelv
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsPhysicsSpin (aerodynamics)Neutron scatteringNeutronNuclear physicsThermodynamics

Abstract

fetched live from OpenAlex

We use inelastic neutron scattering to show that superconducting (SC) rubidium iron selenide Rb${}_{0.82}$Fe${}_{1.68}$Se${}_{2}$ exhibits antiferromagnetic (AF) spin excitations near the in-plane wave vector $Q=(\ensuremath{\pi},0)$ identical to that for iron arsenide superconductors. Moreover, we find that these excitations change from incommensurate to commensurate with increasing energy and occur at the expense of spin waves associated with the coexisting $\sqrt{5}\ifmmode\times\else\texttimes\fi{}\sqrt{5}$ block AF phase. Since these spin excitations cannot come from Fermi surface nesting based on angle resolved photoemission experiments, our results indicate the presence of local moments in SC Rb${}_{0.82}$Fe${}_{1.68}$Se${}_{2}$ that may have a similar origin as the hourglass-like spin excitations in copper oxide superconductors.

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 score0.007
Threshold uncertainty score0.015

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.302
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 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

Citations20
Published2012
Admission routes1
Has abstractyes

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Same venuePhysical Review BSame topicIron-based superconductors researchFrench-language works237,207