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Record W2076048120 · doi:10.1007/s12274-014-0538-4

Growth of skyrmionic MnSi nanowires on Si: Critical importance of the SiO2 layer

2014· article· en· W2076048120 on OpenAlexaff
Siwei Tang, Ivan I. Kravchenko, Jieyu Yi, Guixin Cao, Jane Y. Howe, David Mandrus, Zheng Gai

Bibliographic record

VenueNano Research · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsHitachi (Canada)
Fundersnot available
KeywordsSkyrmionNanowireCondensed matter physicsMaterials scienceNanostructureVortexEpitaxyLattice (music)Phase diagramMagnetic fieldCrystal structureLayer (electronics)NanotechnologyPhase (matter)CrystallographyChemistryPhysics

Abstract

fetched live from OpenAlex

MnSi in the B20 structure is a prototypical helimagnet that forms a skyrmion lattice, a vortex-like spin texture under applied magnetic field. We have systematically explored the synthesis of single crystal MnSi nanowires via controlled oxide-assisted chemical vapor deposition and observed a characteristic signature of skyrmion magnetic ordering in the MnSi nanowires. The thickness of the SiO 2 layer on the Si substrate plays the key role in obtaining a high yield of B20 MnSi skyrmion nanowires. A growth mechanism was proposed that is consistent with the existence of an optimum SiO 2 thickness. A growth phase diagram was constructed based on the extensive studies of various growth conditions for various MnSi nanostructures. The persistence of both the helicoidal and skyrmion magnetic ordering in the one-dimensional wires was directly revealed by ac and dc magnetic measurements.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.047
GPT teacher head0.342
Teacher spread0.295 · 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
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

Citations12
Published2014
Admission routes1
Has abstractyes

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