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Record W3182664121 · doi:10.3390/app11146308

Study of Possible Frequency Dependence of Small AC Fields on Magnetic Flux Trapping in Niobium by Polarized Neutron Imaging

2021· article· en· W3182664121 on OpenAlexaff
W. Treimer, Tobias Junginger, Oliver Kugeler

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of VictoriaTRIUMF
Fundersnot available
KeywordsNiobiumTrappingCondensed matter physicsSuperconductivityMagnetic fluxFlux pinningMagnetic fieldFlux (metallurgy)Materials scienceAnnealing (glass)Nuclear magnetic resonancePhysicsHigh-temperature superconductivity

Abstract

fetched live from OpenAlex

Reducing the size of ambient magnetic flux trapping during cooldown in superconducting radio-frequency niobium cavities is essential to reaching the lowest power dissipation as required for continuous wave application. Here, it is suggested that applying an alternating magnetic field superimposed to the external DC field can potentially reduce the size of trapped flux by supporting flux line movement. This hypothesis is tested for the first time systematically on a buffered chemically polished (BCP) niobium sample before and after high temperature annealing, a procedure which is known to reduce flux pinning. External low-frequency (Hz-range) magnetic fields were applied to the samples during their superconducting transition and the effect of varying their amplitude, frequency and offset was investigated. A few results can be highlighted: The influence of the frequency and magnitude of the AC fields on the flux trapping in the untreated Nb sample cannot be neglected. The trapped flux seems to be homogeneously distributed, unlike the flux trapping in, e.g., lead (Pb), which is a type I superconductor. After annealing, the Nb sample shows practically no dependency of flux trapping on external AC fields. The trapped magnetic flux was measured by polarized neutron imaging, and calculations of trapped fields show good agreement with experimental results.

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 score0.247
Threshold uncertainty score0.348

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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
Published2021
Admission routes1
Has abstractyes

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