MétaCan
Menu
Back to cohort
Record W2034617615 · doi:10.1063/1.4838076

Magnetism, half-metallicity and electrical transport properties of V- and Cr-doped semiconductor SnTe: A theoretical study

2013· article· en· W2034617615 on OpenAlexaff
Yong Liu, S. K. Bose, J. Kudrnovský

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicHeusler alloys: electronic and magnetic properties
Canadian institutionsBrock University
FundersNatural Science Foundation of Hebei Province
KeywordsCondensed matter physicsElectrical resistivity and conductivityCurie temperatureMagnetismDopingFermi levelMaterials scienceSpintronicsMagnetic semiconductorElectronic structureMagnetic momentFerromagnetismPhysics

Abstract

fetched live from OpenAlex

This work presents results for the electronic structure, magnetic properties, and electrical resistivity of the semiconductor SnTe doped with 3d transition metals V and Cr. From the standpoint of potential application in spintronics, we look for half-metallic states and analyze their properties in both rock salt and zinc blende structures using ab initio electronic structure methods. In both cases, it is the Sn-sublattice that is doped with the transition metals, as has been the case with experiments performed so far. We find four half-metallic compounds at their optimized cell volumes. Results of exchange interactions and the Curie temperature are presented and analyzed for all the relevant cases. Resistivity calculation based on Kubo-Greenwood formalism shows that the resistivities of these alloys due to transition metal doping of the Sn-sublattice may vary, in most cases, from typical liquid metal or metallic glass value to 2–3 times higher. 25% V-doping of the Sn-sublattice in the rock salt structure gives a very high resistivity, which can be traced to high values of the lattice parameter resulting in drastically reduced hopping or diffusivity of the states at the Fermi level.

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 score0.017
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicHeusler alloys: electronic and magnetic propertiesFrench-language works237,207