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Record W4283315511 · doi:10.1139/cjp-2021-0227

Computational delving into conceivable thermoelectric and spintronic applications of NH<sub>4</sub>AF<sub>3</sub> (A = Fe and Co) ferromagnets

2022· article· en· W4283315511 on OpenAlexvenueno aff
A. A. Mubarak, Saad Tariq, Farida Hamioud, Bushra Kanwal

Bibliographic record

VenueCanadian Journal of Physics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHeusler alloys: electronic and magnetic properties
Canadian institutionsnot available
Fundersnot available
KeywordsSpintronicsFerromagnetismThermoelectric effectIonic bondingCondensed matter physicsMaterials scienceElectronic structureWIEN2kThermoelectric materialsPhysicsThermodynamicsIon

Abstract

fetched live from OpenAlex

In this investigation, the structural, mechanical, electronic, magnetic, and thermoelectric properties of fluoroperovskite NH 4 AF 3 (A = Fe and Co) compounds were determined utilizing the WIEN2k code. The structural properties such as tolerance factor, enthalpy, and elastic stability criterion predict that the studied compounds are mechanically and thermodynamically stable. Furthermore, the mechanical properties show that NH 4 FeF 3 is more stretchable and rigid in nature than NH 4 CoF 3 . Interestingly, both compounds exhibit the anisotropic, intermediate type of bonding effect, higher plastic deformation limit, and brittle nature. The high melting temperature of the present compounds indicates the possible usage of these compounds in high-temperature electronic applications. The electronic calculations show that NH 4 FeF 3 and NH 4 CoF 3 are half-metallic and ferromagnetic compounds with a combination of ionic and covalent bonds between the different atoms. The thermoelectric parameters predict that electrons are the main charge carriers for the present compound with p-type character. Our study suggests that these plausible applications of the compounds are very versatile and include heat sensors, spintronic devices, optoelectronic sensors, and magnetic tunnel junction devices.

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 score0.050
Threshold uncertainty score0.596

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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