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Record W4390120806 · doi:10.1039/d3nr04424f

Probing spin waves in Co<sub>3</sub>O<sub>4</sub> nanoparticles for magnonics applications

2023· article· en· W4390120806 on OpenAlex
Mikhail Feygenson, Zhongyuan Huang, Yinguo Xiao, Xiaowei Teng, Wiebke Lohstroh, Nileena Nandakumaran, Jörg Neuefeind, Michelle Everett, A. Podlesnyak, Germán Salazar‐Alvarez, Seda Ulusoy, Mario Valvo, Yixi Su, Sascha Ehlert, Asma Qdemat, Marina Ganeva, Lihua Zhang, M. C. Aronson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanoscale · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Synthesis of Ferrites
Canadian institutionsUniversity of British Columbia
FundersBrookhaven National LaboratoryOak Ridge National LaboratoryOffice of ScienceBasic Energy SciencesTechnische Universität MünchenVetenskapsrådetU.S. Department of Energy
KeywordsMagnonicsNanoparticleMaterials scienceSpin waveSpin (aerodynamics)NanotechnologyPhysicsCondensed matter physicsNuclear physicsSpin polarizationFerromagnetism

Abstract

fetched live from OpenAlex

to study the shape and size effects on their static and dynamic magnetic proprieties. Using a combination of experimental methods, we probed the magnetic and crystal structures of our samples and directly measured spin wave dispersions using inelastic neutron scattering. We found a weak, but unquestionable, increase in exchange interactions for the cubic nanoparticles as compared to spherical nanoparticle and bulk powder reference samples. Interestingly, the exchange interactions in spherical nanoparticles have bulk-like properties, despite a ferromagnetic contribution from canted surface spins.

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.

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 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 score0.030
Threshold uncertainty score0.823

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)0.0000.001

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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