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Record W2772138898 · doi:10.1063/1.5004667

Tuning the metamagnetism in a metallic helical antiferromagnet

2017· article· en· W2772138898 on OpenAlex

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

VenueApplied Physics Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsMetamagnetismMagnetic refrigerationAntiferromagnetismFerromagnetismMaterials scienceCondensed matter physicsDiffusionless transformationParamagnetismIndiumMagnetic shape-memory alloyAusteniteAlloyMartensiteMagnetic fieldMagnetic domainMetallurgyMagnetizationPhysics

Abstract

fetched live from OpenAlex

The antiferromagnetic (AFM)-ferromagnetic (FM) conversion in martensite was observed in Mn/Ni-substitution upon FM elements, such as Fe or Co, in MnNiGe helical antiferromagnets. Here, we report an AFM-FM conversion and consequently a sharp magnetic-field-driven metamagnetic martensitic transformation from paramagnetic (PM) austenite to FM martensite in the Ni- and Mn-substituted MnNiGe alloys with indium, a non-magnetic and large-sized main group element. Accordingly, a giant magnetocaloric effect such that a twofold increase of the magnetic entropy change in MnNi0.92GeIn0.08 and even a nearly threefold increase in the Mn0.92NiGeIn0.08 alloy is obtained with respect to the MnNiGe0.95In0.05 alloy. The origin of AFM-FM conversion and resultantly sharp magnetic-field-induced PM-FM metamagnetic transformation is discussed based on the first-principles calculations and X-ray absorption spectroscopic results.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.637

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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