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Record W2121897185 · doi:10.1103/physrevb.73.085205

Suppressed reflectivity due to spin-controlled localization in a magnetic semiconductor

2006· article· en· W2121897185 on OpenAlexaff
F. P. Mena, J. F. DiTusa, D. van der Marel, G. Aeppli, David P. Young, A. Damascelli, J. A. Mydosh

Bibliographic record

VenuePhysical Review B · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of British Columbia
FundersDivision of Materials ResearchNederlandse Organisatie voor Wetenschappelijk OnderzoekSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsCondensed matter physicsScatteringSemiconductorCoulombCharge carrierMagnetic semiconductorSpin (aerodynamics)Spin polarizationParamagnetismPolarization (electrochemistry)Carrier scatteringElectronDopingChemistryPhysicsMaterials scienceOpticsOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

The narrow gap semiconductor FeSi owes its strong paramagnetism to electron-correlation effects. Partial Co substitution for Fe produces a spin-polarized doped semiconductor. The spin polarization causes suppression of the metallic reflectivity and increased scattering of charge carriers, in contrast to what happens in other magnetic semiconductors, where magnetic order reduces the scattering. The loss of metallicity continues progressively even into the fully polarized state, and entails as much as a 25% reduction in average mean-free path. We attribute the observed effect to a deepening of the potential wells presented by the randomly distributed Co atoms to the majority spin carriers. This mechanism inverts the sequence of steps for dealing with disorder and interactions from that in the classic Al'tshuler Aronov approach---where disorder amplifies the Coulomb interaction between carriers---in that here, the Coulomb interaction leads to spin polarization which in turn amplifies the disorder-induced scattering.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.283
Teacher spread0.273 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

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