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Record W2035182337 · doi:10.1039/c4nr07262f

A new simple method for point contact Andreev reflection (PCAR) using a self-aligned atomic filament in transition-metal oxides

2015· article· en· W2035182337 on OpenAlexaff
Inrok Hwang, Keundong Lee, Hyunwoo Jin, Sunhwa Choi, Eunok Jung, Bae Ho Park

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsToronto Metropolitan University
FundersNational Research Foundation of KoreaKorea Institute of Science and Technology
KeywordsAndreev reflectionSimple (philosophy)Protein filamentMaterials scienceReflection (computer programming)Transition metalCondensed matter physicsMetalSuperconductivityPhysicsChemistryMetallurgyComposite materialComputer science

Abstract

fetched live from OpenAlex

Point contact Andreev reflection (PCAR) has become a standard method for measuring the spin polarization (P) of spintronic materials due to its unique simplicity and the firm physical ground, but it is still challenging to achieve a clean point contact between a superconductor (SC) and a metal (N) for implementing PCAR. In this work, we suggest a much simpler method for PCAR measurement, where a point contact between SC and N is provided by a metallic filament in a transition-metal oxide generated by electrical bias. This method has been successfully demonstrated using a structure composed of Nb/NiO/Pt, where P of the Ni filament was estimated to be about 40%, consistent with the known value of the bulk Ni. In addition, we investigated the dependence of the conductance spectrum on the measurement temperature and the magnetic field. We found that the superconductivity is not fully suppressed until 9 T far above the critical field of Nb, which is associated with the nm-sized constriction of our SC/N junction, much smaller than the coherence length of the SC.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.307
Teacher spread0.275 · 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
GenreMethods

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

Citations9
Published2015
Admission routes1
Has abstractyes

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