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Record W2075771427 · doi:10.1063/1.2434953

Multilayer route to iron nanoparticle formation in an insulating matrix

2007· article· en· W2075771427 on OpenAlexafffund
Feng Wang, Marek Malac, R.F. Egerton, A. Meldrum, Xiaobin Zhu, Zhigang Liu, Nicole Macdonald, Peng Li, M. R. Freeman

Bibliographic record

VenueJournal of Applied Physics · 2007
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersKillam Trusts
KeywordsMaterials scienceMaghemiteNanoparticleCoercivityTransmission electron microscopySiliconAnnealing (glass)EvaporationAnalytical Chemistry (journal)NanotechnologyCondensed matter physicsComposite materialChemistryMetallurgy

Abstract

fetched live from OpenAlex

Well-protected, isolated bcc-iron nanoparticles embedded in silicon dioxide were prepared by e-beam evaporation and postannealing of multilayers in an ultrahigh vacuum system. The spherical shape and isolation of the particles were confirmed by plan-view and cross-sectional transmission electron microscopy. Oxidation was evaluated from the electron energy-loss near edge structure. In this technique, a postedge peak of 40eV above the iron L3 threshold, originating from backscattering of oxygen atoms, provides a clear indication of iron oxide. The white-line ratio (WLR), measuring the 3d-orbital occupancy, is used to estimate the oxidation-layer thickness. In the samples of large iron nanoparticles (with average diameter larger than 10nm), a very thin surface layer appears to be the oxide maghemite, approximately one atomic layer according to the WLR evaluations. The evolution of the coercivity with particle size, as measured by the magneto-optical Kerr effect, shows that the reversal process is dominated by the surface anisotropy and is also affected by the dipole interaction, particularly in samples with large volume-filling factor.

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 score0.147
Threshold uncertainty score0.330

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 studies0.0000.000
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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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

Citations10
Published2007
Admission routes2
Has abstractyes

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