MétaCan
Menu
Back to cohort
Record W3024444669 · doi:10.1021/acsomega.0c00279

Anisotropic Magnetic Resonance in Random Nanocrystal Quantum Dot Ensembles

2020· article· en· W3024444669 on OpenAlexfundno aff
A. J. Almeida, Ayaskanta Sahu, David J. Norris, G. N. Kakazeı̆, Haripriya Kannan, Martin S. Brandt, M. Stutzmann, Rui N. Pereira

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsnot available
FundersDivision of Materials ResearchUniversidade do PortoEidgenössische Technische Hochschule ZürichFundação Calouste GulbenkianUniversity of TwenteUniversidade de AveiroYork UniversitySchlumberger FoundationFundação para a Ciência e a TecnologiaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMinisterio de Economía y CompetitividadNational Science Foundation
KeywordsMagnetic dipoleCondensed matter physicsMagnetic anisotropyDipoleAnisotropyForce between magnetsPhysicsMagnetic momentElectron magnetic dipole momentMagnetic energyMagnetic fieldMagnetizationQuantum mechanics

Abstract

fetched live from OpenAlex

Magnetic anisotropy critically determines the utility of magnetic nanocrystals (NCs) in new nanomagnetism technologies. Using angular-dependent electron magnetic resonance (EMR), we observe magnetic anisotropy in isotropically arranged NCs of a nonmagnetic material. We show that the shape of the EMR angular variation can be well described by a simple model that considers magnetic dipole-dipole interactions between dipoles randomly located in the NCs, most likely due to surface dangling bonds. The magnetic anisotropy results from the fact that the energy term arising from the magnetic dipole-dipole interactions between all magnetic moments in the system is dominated by only a few dipole pairs, which always have an anisotropic geometric arrangement. Our work shows that magnetic anisotropy may be a general feature of NC systems containing randomly distributed magnetic dipoles.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.692

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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 designNot applicable
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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueACS OmegaSame topicMagnetic properties of thin filmsFrench-language works237,207