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Record W4402558747 · doi:10.1139/cjp-2024-0088

Ab initio investigation of the structural, mechanical, electronic, thermodynamic, and optical properties of V<sub>2</sub>FeNiGe<sub>2</sub> and Hf<sub>2</sub>FeNiSb<sub>2</sub> double half-Heusler compounds

2024· article· en· W4402558747 on OpenAlexvenueno aff
Mohamed Diaf, H. Righi, Raouf Beddiaf, Y. Djaballah, H. Rached

Bibliographic record

VenueCanadian Journal of Physics · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHeusler alloys: electronic and magnetic properties
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsPhysicsAb initioCondensed matter physicsThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

In the present work, using ab initio density-functional theory methods based on the Quantum ESPRESSO package, we have investigated the structural, elastic, electronic, and optical properties of 18-electron V 2 FeNiGe 2 and Hf 2 FeNiSb 2 double half-Heusler alloys. The calculated elastic properties suggest a ductile behavior with metallic bonding for V 2 FeNiGe 2 and a brittle behavior with covalent bonding for Hf 2 FeNiSb 2 . The thermodynamic properties (Debye temperature, melting temperature) are also predicted and discussed for the studied alloys. The alloys are found to be semiconducting with indirect band gaps of 0.53 eV for V 2 FeNiGe 2 and 0.47 eV for Hf 2 FeNiSb 2 . We also computed and analyzed their optical properties (dielectric function, optical conductivity, refractive index, absorption index, and reflectance) and our calculations suggest that both materials have high absorption coefficient and optical conductivity in the UV as well as visible region. The results make them potential candidates for the manufacture of opto-electronic 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.197
Teacher spread0.183 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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