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Record W3039629772 · doi:10.1063/5.0013656

Direct observation of ferrimagnetic ordering in inverse Heusler alloy Mn2CoAl

2020· article· en· W3039629772 on OpenAlexfundno aff
Zhendong Chen, Wenqing Liu, Peng Chen, Xuezhong Ruan, Jiabao Sun, Ruobai Liu, Cunxu Gao, Jun Du, Bo Liu, Hao Meng, Rong Zhang, Yongbing Xu

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHeusler alloys: electronic and magnetic properties
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of ChinaRoyal SocietyEngineering and Physical Sciences Research CouncilLeverhulme TrustUniversity of Guelph
KeywordsFerrimagnetismSpintronicsCondensed matter physicsMaterials scienceMagnetic momentHeusler compoundMagnetic circular dichroismSpin polarizationCurie temperatureFerromagnetismMagnetizationPhysicsMagnetic fieldElectron

Abstract

fetched live from OpenAlex

Compensated ferrimagnetic Heusler compounds with high spin polarization and a low net magnetic moment are strategically important materials for spin-logic and further energy-efficient spintronic applications. However, the element-resolved magnetic ordering of these compensated ferrimagnets remains an open issue. Here, we report a direct observation of the spin and orbital moments of the B2 phase Mn2CoAl thin film using the synchrotron-based x-ray magnetic circular dichroism technique. An ferrimagnetic ordering between Mn and Co elements and a compensated-ferrimagnet-like small net magnetic moment of only 0.34 μB/f.u. were observed unambiguously in B2 Mn2CoAl. Antiparallel coupling between Mn and Co is attributed to the mixture of the Mn(B) and Al occupation in the B2 phase Mn2CoAl lattice. This work demonstrates great potential of the compensated ferrimagnetic half-metallic inverse Heusler compounds Mn2CoAl for spintronic applications.

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 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 score0.665

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)0.0000.000

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 head0.025
GPT teacher head0.206
Teacher spread0.181 · 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.

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

Citations14
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics LettersSame topicHeusler alloys: electronic and magnetic propertiesFrench-language works237,207