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<i>β</i>-NMR Study of a buried Mn δ-doped layer in a silicon host

2014· article· en· W2040985149 on OpenAlexaff
David Cortie, T Buck, Ryan M. L. McFadden, J Xiao, C. D. P. Levy, Martin H. Dehn, M. R. Pearson, G. D. Morris, S. R. Dunsiger, R. F. Kiefl, F. J. Rueß, Andreas Fuhrer, W. A. MacFarlane

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsTRIUMFUniversity of British Columbia
Fundersnot available
KeywordsDopingSiliconMaterials scienceLine (geometry)Resonance (particle physics)Layer (electronics)Condensed matter physicsNuclear magnetic resonanceAtmospheric temperature rangeLine widthAnalytical Chemistry (journal)ChemistryAtomic physicsOpticsNanotechnologyOptoelectronicsPhysicsGeometry

Abstract

fetched live from OpenAlex

Low temperature growth methods were used to encapsulate a buried Mn δ-doping layer into a silicon host. A β-NMR investigation was performed of the magnetic properties in the temperature range 10 – 300 K using spin-polarized 8Li+. A depth-dependent broadening and shift of the NMR resonance was detected that is consistent with internal fields distributed at depths of 10 – 30 nm beneath the surface. At low temperatures, a negative relative shift occurred and the resonance was significantly broadened. At 300 K the line-shape could be described by a single Gaussian line, however, at 10 K the line is best approximated by a two component Lorentzian shape consisting of a broad and narrow component as anticipated for a diluted magnetic alloy. The overall magnitude of the resonance shift at both temperatures is small suggesting a weak interaction between the 8Li+ and the magnetic Mn environment.

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 score0.489
Threshold uncertainty score0.621

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)0.0000.000

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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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