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Record W2030470455 · doi:10.1063/1.3602088

Magnetocaloric effect in Ni-Mn-Ga thin films under concurrent magnetostructural and Curie transitions

2011· article· en· W2030470455 on OpenAlexafffund
Yuepeng Zhang, Robert A. Hughes, James F. Britten, Paul A. Dube, J. S. Prestón, G. A. Botton, M. Niewczas

Bibliographic record

VenueJournal of Applied Physics · 2011
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsBrockhouse Institute for Materials ResearchMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic refrigerationCurie temperatureMaterials scienceAusteniteCondensed matter physicsFerromagnetismParamagnetismPhase transitionMagnetic fieldMartensiteThermodynamicsMagnetizationMetallurgyMicrostructurePhysics

Abstract

fetched live from OpenAlex

An investigation of the magnetocaloric effect for Ni-Mn-Ga films with a composition chosen to yield the highly advantageous magnetostructural phase transition between the paramagnetic austenitic and the ferromagnetic martensitic phases is presented. The observed effect is particularly strong at low magnetic fields, yielding a maximum negative entropy change of −1.4 J/kg K for a field change of only 0.5 T. It is also observed that the cooling process yields a 40% larger entropy change compared to the heating process. Temperature dependent magnetic, structural, and transport measurements indicate that the entropy peak difference between cooling and heating cycles is associated with a stronger overlap of the Curie transition of the austenitic phase with the magnetostructural phase transition upon cooling. The observed behavior is significant to micro-length-scale spot cooling applications utilizing thin films and large-scale magnetic refrigeration applications where low magnetic fields are favorable.

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 score0.002
Threshold uncertainty score0.004

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)0.0010.000

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 head0.236
Teacher spread0.217 · 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

Citations42
Published2011
Admission routes2
Has abstractyes

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