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Record W3110547102 · doi:10.1038/s41598-020-77650-6

Competing ferro- and antiferromagnetic exchange drives shape-selective $$\hbox{Co}_3\hbox{O}_4$$ nanomagnetism

2020· article· lv· W3110547102 on OpenAlexafffund
Michael Shepit, Vinod K. Paidi, Charles A. Roberts, J. van Lierop

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Manitoba
FundersArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationU.S. Department of Energy
KeywordsAntiferromagnetismCondensed matter physicsMaterials sciencePhysicsChemistry

Abstract

fetched live from OpenAlex

We have synthesized three different shapes of [Formula: see text] nanoparticles to investigate the relationships between the surface Co[Formula: see text] and Co[Formula: see text] bonding quantified by exploiting the known exposed surface planes, terminations, and coordiations of [Formula: see text] nanoparticle spheres, cubes and plates. Subsequently this information is related to the unusual behaviour observed in the magnetism. The competition of exchange interactions at the surface provides the mechanism for different behaviours in the shapes. The cubes display weakened antiferromagnetic interactions in the form of a spin-flop that occurs at the surface, while the plates show distinct ferromagnetic behaviour due to the strong competition between the interactions. We elucidate the spin properties which are highly sensitive to bonding and crystal field environments. This work provides a new window into the mechanisms behind surface magnetism.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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 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

Citations12
Published2020
Admission routes2
Has abstractyes

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