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Record W1967135465 · doi:10.1038/srep06250

Controlling the near-surface superfluid density in underdoped YBa2Cu3O6+x by photo-illumination

2014· article· en· W1967135465 on OpenAlexaff
E. Stilp, Andreas Suter, Thomas Prokscha, Z. Salman, E. Morenzoni, H. Keller, Patrick Pahlke, Ruben Hühne, C. Bernhard, Ruixing Liang, W. N. Hardy, D. A. Bonn, Jordan Baglo, R. F. Kiefl

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersPaul Scherrer InstitutSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSuperconductivityCondensed matter physicsCuprateSuperfluidityMeissner effectMaterials scienceNanometreHysteresisPhysics

Abstract

fetched live from OpenAlex

The interaction with light weakens the superconducting ground state in classical superconductors. The situation in cuprate superconductors is more complicated: illumination increases the charge carrier density, a photo-induced effect that persists below room temperature. Furthermore, systematic investigations in underdoped YBa2Cu3O(6+x) (YBCO) have shown an enhanced critical temperature Tc. Until now, studies of photo-persistent conductivity (PPC) have been limited to investigations of structural and transport properties, as well as the onset of superconductivity. Here we show how changes in the magnetic screening profile of YBCO in the Meissner state due to PPC can be determined on a nanometer scale utilizing low-energy muons. The data obtained reveal a strongly increased superfluid density within the first few tens of nanometers from the sample surface. Our findings suggest a non-trivial modification of the near-surface band structure and give direct evidence that the superfluid density of YBCO can be controlled by light illumination.

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)0.002
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 score0.456
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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