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Magnetoconductance oscillations at a nanoparticle film–superconductor interface: a means for probing flux penetration depth

2008· article· en· W1990671987 on OpenAlexafffund
Jeffrey Dunford, Al-Amin Dhirani

Bibliographic record

VenueNanotechnology · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCondensed matter physicsMaterials scienceQuantum tunnellingPenetration depthSuperconductivityPenetration (warfare)ScatteringWeak localizationConductanceMagnetic fluxMagnetic fieldSemiconductorElectronFlux (metallurgy)MagnetoresistancePhysicsQuantum mechanicsOptoelectronics

Abstract

fetched live from OpenAlex

Interfaces between disordered normal materials and superconductors (S) can exhibit 'reflectionless tunnelling' (RT)-a phenomenon that arises from repeated disorder-driven elastic scattering, multiple Andreev reflections, and electron/hole interference. RT has been used to explain zero-bias conductance peaks (ZBCPs) observed using doped semiconductors and evaporated granular metal films as the disordered normal materials. Recently, in addition to ZBCPs, magnetoconductance oscillations predicted by RT theory have been observed using a novel normal disordered material: self-assembled nanoparticle films. In the present study, we find that the period of these oscillations decreases as temperature (T) increases. This suggests that the magnetic flux associated with interfering pathways increases accordingly. We propose that the increasing flux can be attributed to magnetic field penetration into S as [Formula: see text]. This model agrees remarkably well with known T dependence of penetration depth predicted by Bardeen-Cooper-Schrieffer theory. Our study shows that this additional region of flux is significant and must be considered in experimental and theoretical studies of RT.

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 score0.020
Threshold uncertainty score0.965

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.259
Teacher spread0.229 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

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