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Record W4414797485 · doi:10.1088/1361-648x/ae0f6c

Fractional magnetization plateaus in the Shastry–Sutherland lattice material Er<sub>2</sub>Be<sub>2</sub>GeO<sub>7</sub>

2025· article· en· W4414797485 on OpenAlexafffund
M. Pula, S. Sharma, J. Gautreau, K. P. Sajilesh, Amit Kanigel, Clarina dela Cruz, Tristan N. Dolling, Lucy Clark, G. M. Luke

Bibliographic record

VenueJournal of Physics Condensed Matter · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsTRIUMFMcMaster University
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetizationMagnetismFerromagnetismLattice (music)Magnetic hysteresisSaturation (graph theory)

Abstract

fetched live from OpenAlex

Abstract The experimental study of magnetism on the Shastry–Sutherland lattice (SSL) has been ongoing for more than two decades, following the discovery of the first SSL material SrCu2(BO3)2. However, investigation of SSL systems is often complicated by the requirements of high magnetic fields (e.g. > 20 T for SrCu2(BO3)2) or the presence of itinerant electrons (e.g. REB4). In this paper, we present the magnetic properties of the SSL material Er2Be2GeO7. Like SrCu2(BO3)2, Er2Be2GeO7 exhibits fractional magnetization plateaus. However, unlike SrCu2(BO3)2, Er2Be2GeO7 exhibits long-range order below ∼ 1 K, and the plateaus are accessible using commercial laboratory equipment, occurring for fields < 1 T. The fractions of magnetization present are closest to 1 / 4 , 4 / 9 , and 1 / 2 of the saturation magnetization. Hysteresis is observed in the 4 / 9 and 1 / 2 plateau phases. The lack of itinerant electrons, chemical disorder, and the low fields required to access the fractional magnetization plateaus promise Er2Be2GeO7 as a prime candidate for the study of frustrated magnetism on the SSL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes2
Has abstractyes

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