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Record W2965514538 · doi:10.1039/c9nr05182a

Experimental determination of single molecule toroic behaviour in a Dy<sub>8</sub> single molecule magnet

2019· article· en· W2965514538 on OpenAlexaff
Qing Zhang, Michael L. Baker, Shiqi Li, M. P. Sarachik, José J. Baldoví, Alejandro Gaita‐Ariño, Eugenio Coronado, Dimitris I. Alexandropoulos, Theocharis C. Stamatatos

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnetism in coordination complexes
Canadian institutionsBrock University
FundersGeneralitat ValencianaH2020 Marie Skłodowska-Curie ActionsArmy Research OfficeUniversitat de ValènciaMinisterio de Economía y CompetitividadEuropean Commission
KeywordsMoleculeMagnetSingle-molecule magnetMaterials scienceDysprosiumCrystallographyChemistryPhysicsNuclear physicsMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

The enhancement of toroic motifs through coupling toroidal moments within molecular nanomagnets is a new, interesting and relevant approach for both fundamental research and potential quantum computation applications. We investigate a Dy8 molecular cluster and discover it has a antiferrotoroic ground state with slow magnetic relaxation. The experimental characterization of the magnetic anisotropy axes of each magnetic center and their exchange interactions represents a considerable challenge due to the non-magnetic nature of the toroidal motif. To overcome this and obtain access to the low energy states of Dy8 we establish a multi-orientation single-crystal micro Hall sensor magnetometry approach. Using an effective Hamiltonian model we then unpick the microscopic spin structure of Dy8, leading to a canted antiferrotoroidic tetramer molecular ground state. These findings are supported with electrostatic calculations that independently confirm the experimentally determined magnetic anisotropy axes for each DyIII ion within the molecule.

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 categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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