MétaCan
Menu
Back to cohort
Record W2604995746 · doi:10.1039/c7nr00908a

Aligned cuboid iron nanoparticles by epitaxial electrodeposition

2017· article· en· W2604995746 on OpenAlexafffund
Karin Leistner, Mingze Yang, Christine Damm, Steffen Oswald, Andreas Petr, V. Kataev, Kornelius Nielsch, K. L. Kavanagh

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsCuboidNanoparticleMaterials scienceCrystallinityNanotechnologyEpitaxyMagnetismNanoscopic scaleDeposition (geology)Iron oxideChemical engineeringMetallurgyComposite materialLayer (electronics)Condensed matter physics

Abstract

fetched live from OpenAlex

Aligned, individual iron square cuboid nanoparticles have been achieved by taking advantage of epitaxial, three-dimensional-island growth on GaAs(001) during electrodeposition at low deposition rates. The nanoparticles exhibit lateral dimensions between 10 and 80 nm and heights below 40 nm. Surface {100} facets predominate with a thin crystalline oxide shell that protects the nanoparticles during prolonged storage in air. The single crystallinity of the iron in combination with structural alignment leads to an in-plane magnetic anisotropy. These immobilized, oriented, and stable nanoparticles are promising for applications in nanoelectronic, sensor, and data storage technologies, as well as for the detailed analysis of the effect of shape and size on magnetism at the nanoscale.

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 score0.039
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueNanoscaleSame topicMagnetic properties of thin filmsFrench-language works237,207