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Record W2259678347 · doi:10.1126/science.aad1824

Nematicity in stripe-ordered cuprates probed via resonant x-ray scattering

2016· article· en· W2259678347 on OpenAlexafffund
Andrew Achkar, M. Zwiebler, Christopher McMahon, Feizhou He, Ronny Sutarto, Isaiah Djianto, Zhihao Hao, Michel J. P. Gingras, M. Hücker, Genda Gu, A. Revcolevschi, H. Zhang, Young‐June Kim, J. Geck, D. G. Hawthorn

Bibliographic record

VenueScience · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsCanadian Institute for Advanced ResearchCanadian Light Source (Canada)University of TorontoPerimeter InstituteUniversity of Waterloo
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Research ChairsCanadian Institute for Advanced ResearchCanada Foundation for InnovationU.S. Department of Energy
KeywordsCuprateCondensed matter physicsSuperconductivityScatteringPhysicsOrder (exchange)Symmetry (geometry)Angle-resolved photoemission spectroscopySymmetry breakingAtomic orbitalElectronic structureQuantum mechanicsElectron

Abstract

fetched live from OpenAlex

In underdoped cuprate superconductors, a rich competition occurs between superconductivity and charge density wave (CDW) order. Whether rotational symmetry-breaking (nematicity) occurs intrinsically and generically or as a consequence of other orders is under debate. Here, we employ resonant x-ray scattering in stripe-ordered superconductors (La,M)2CuO4 to probe the relationship between electronic nematicity of the Cu 3d orbitals, structure of the (La,M)2O2 layers, and CDW order. We find distinct temperature dependences for the structure of the (La,M)2O2 layers and the electronic nematicity of the CuO2 planes, with only the latter being enhanced by the onset of CDW order. These results identify electronic nematicity as an order parameter that is distinct from a purely structural order parameter in underdoped striped cuprates.

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 score0.449
Threshold uncertainty score0.271

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 studies0.0000.000
Scholarly communication0.0000.002
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 head0.011
GPT teacher head0.244
Teacher spread0.232 · 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

Citations85
Published2016
Admission routes2
Has abstractyes

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