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Record W2995925408 · doi:10.1039/c9nr08634j

Magnetic force fields of isolated small nanoparticle clusters

2019· article· en· W2995925408 on OpenAlexfundno aff
Cristian Iacoviță, Jérôme Hurst, Giovanni Manfredi, Paul-Antoine Hervieux, Bertrand Donnio, Jean‐Louis Gallani, M. V. Rastei

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsnot available
FundersEuropean Office of Aerospace Research and DevelopmentAir Force Office of Scientific ResearchUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiAutoritatea Natională pentru Cercetare StiintificăCentre National de la Recherche ScientifiqueUniversité de StrasbourgConsiliului National al Cercetarii Stiintifice din Invatamantul SuperiorAgence Nationale de la RechercheOttawa Hospital Research Institute
KeywordsNanoparticleMaterials scienceNanotechnologyMagnetic nanoparticlesMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

The usage of magnetic nanoparticles (NPs) in applications necessitates a precise mastering of their properties at the single nanoparticle level. There has been a lot of progress in the understanding of the magnetic properties of NPs, but incomparably less when interparticle interactions govern the overall magnetic response. Here, we present a quantitative investigation of magnetic fields generated by small clusters of NPs assembled on a dielectric non-magnetic surface. Structures ranging from individual NPs to fifth-fold particulate clusters are investigated in their magnetization saturation state by magnetic force microscopy and numerical calculations. It is found that the magnetic stray field does not increase proportionally with the number of NPs in the cluster. Both measured and calculated magnetic force fields underline the great importance of the exact spatial arrangement of NPs, shedding light on the magnetic force field distribution of particulate clusters, which is relevant for the quantitative evaluation of their magnetization and perceptibly for many applications.

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 score0.174
Threshold uncertainty score0.617

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)0.0010.000

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 head0.188
Teacher spread0.182 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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