MétaCan
Menu
Back to cohort
Record W2140081099 · doi:10.1039/c4tc00443d

Shape-dependent magnetism of bimetallic FeNi nanosystems

2014· article· en· W2140081099 on OpenAlexafffund
Nafiseh Moghimi, Fatemeh Rahnemaye Rahsepar, Saurabh Srivastava, Nina F. Heinig, K. T. Leung

Bibliographic record

VenueJournal of Materials Chemistry C · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceBimetallic stripMagnetismAlloyCondensed matter physicsPhase (matter)AnisotropyNanoparticleSaturation (graph theory)Density functional theoryNanotechnologyMetallurgyComputational chemistryMetalOpticsChemistry

Abstract

fetched live from OpenAlex

Shape-dependent magnetic properties of a bimetallic system have been studied for FeNi nanoparticles with well-defined concave cubic and octahedron shapes. The alloy composition was chosen to be close to that of an Invar FeNi alloy (with 35% Ni content) but with the coexistence of both bcc and fcc phases, in order to investigate the role of phase combinations in controlling the magnetic properties for the first time. Different saturation magnetization and coercivities were observed for the resulting FeNi alloy nanoparticles, and these differences have been correlated with surface anisotropy and formation of different two-phase combinations. The role of two-phase combinations in governing the magnetic properties has also been studied for both bulk and nanoalloys by large-scale Density Function Theory (DFT) calculations using VASP, which provides a new complementary approach to understanding the magnetic properties of alloy materials.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0090.000

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 head0.196
Teacher spread0.190 · 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.

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

Citations24
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Materials Chemistry CSame topicMagnetic properties of thin filmsFrench-language works237,207