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Record W2048148554 · doi:10.1063/1.4811090

The effect of vacancies on the microwave surface resistance of niobium revealed by positron annihilation spectroscopy

2013· article· en· W2048148554 on OpenAlexaff
Alexander Romanenko, C J Edwardson, P. G. Coleman, P. J. Simpson

Bibliographic record

VenueApplied Physics Letters · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsNiobiumVacancy defectPositron annihilation spectroscopyAnnihilationSpectroscopyMaterials scienceCondensed matter physicsPhotonSuperconductivityPositronDopingAtomic physicsElectronPhysicsNuclear physicsPositron annihilationMetallurgyOptics

Abstract

fetched live from OpenAlex

Using variable-energy positron annihilation spectroscopy, we demonstrate that a different near-surface vacancy concentration accompanies drastic differences in surface resistance of superconducting niobium cavities for particle acceleration. Our data suggest that vacuum baking at 120 °C leads to the doping of a near-surface layer with vacancy-hydrogen complexes, and that higher vacancy-type defect concentration distinguishes electropolished from chemically etched cavities. Our findings may help to explain a strong dependence of cavity performance on heat and chemical treatments, and may be of interest to other physics fields including cavity quantum electrodynamics (QED), microresonators, and single photon detectors.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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