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Record W4402541511 · doi:10.1038/s41598-024-71724-5

Depth-resolved characterization of Meissner screening breakdown in surface treated niobium

2024· article· en· W4402541511 on OpenAlexafffundabout
Edward Thoeng, Md Asaduzzaman, Philipp Kolb, Ryan M. L. McFadden, G. D. Morris, John O. Ticknor, S. R. Dunsiger, Victoria L. Karner, Derek Fujimoto, Tobias Junginger, R. F. Kiefl, W. A. MacFarlane, Ruohong Li, Suresh Saminathan, Robert Laxdal

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityUniversity of VictoriaTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsNiobiumCharacterization (materials science)Meissner effectMaterials scienceSurface (topology)SuperconductivityPhysicsNanotechnologyCondensed matter physicsMetallurgyMathematicsGeometry

Abstract

fetched live from OpenAlex

We report direct measurements of the magnetic field screening at the limits of the Meissner phase for two superconducting niobium (Nb) samples. The samples are processed with two different surface treatments that have been developed for superconducting radio-frequency (SRF) cavity applications—a “baseline” treatment and an oxygen-doping (“O-doping”) treatment. The measurements show: (1) that the screening length is significantly longer in the “O-doping” sample compared to the “baseline” sample; (2) that the screening length near the limits of the Meissner phase increases with applied field; (3) the evolution of the screening profile as the material transitions from the Meissner phase to the mixed phase; and (4) a demonstration of the absence of any screening profile for the highest applied field, indicative of the full flux entering the sample. Measurements are performed utilizing the $$\beta$$ -detected nuclear magnetic resonance ( $$\beta$$ -NMR) technique that allows depth resolved studies of the local magnetic field within the first 100 nm of the surface. The study takes advantage of the $$\beta$$ -SRF beamline, a new facility at TRIUMF, Canada, where field levels up to 200 mT are available parallel to the sample surface to replicate radio frequency fields near the Meissner breakdown limits of Nb.

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 score0.459
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.227
Teacher spread0.215 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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