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Record W2024735358 · doi:10.1103/physrevb.63.054422

Ideal spin filters: A theoretical study of electron transmission through ordered and disordered interfaces between ferromagnetic metals and semiconductors

2001· article· en· W2024735358 on OpenAlexaff
George Kirczenow

Bibliographic record

VenuePhysical review. B, Condensed matter · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCondensed matter physicsFerromagnetismSpin (aerodynamics)Fermi levelElectronIdeal (ethics)SemiconductorFermi energySpinplasmonicsFermi Gamma-ray Space TelescopeMaterials sciencePhysicsSpin polarizationSpin Hall effectQuantum mechanics

Abstract

fetched live from OpenAlex

It is predicted that certain atomically ordered interfaces between some ferromagnetic metals $(F)$ and semiconductors $(S)$ should act as ideal spin filters that transmit electrons only from the majority spin bands or only from the minority spin bands of the F to the S (and from the S only to the majority spin bands or only to the minority spin bands of the $F)$ at the Fermi energy, even for F with both majority and minority bands at the Fermi level. Criteria for determining which combinations of F, S, and interface should be ideal spin filters are formulated. The criteria depend only on the bulk band structures of the S and F and on the translational symmetries of the S, F, and interface. Several examples of systems that meet these criteria to a high degree of precision are identified. Disordered interfaces between F and S are also studied and it is found that intermixing between the S and F can result in interfaces with spin antifiltering properties, the transmitted electrons being much less spin polarized than those in the ferromagnetic metal at the Fermi energy.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.308
Teacher spread0.294 · 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 designTheoretical or conceptual
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

Citations95
Published2001
Admission routes1
Has abstractyes

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