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Record W3211376451 · doi:10.1063/5.0064849

Anharmonicity in partially filled skutterudites Yb<i>x</i>Co4Sb12

2021· article· en· W3211376451 on OpenAlexafffund
Yanyun Hu, James R. Salvador, Ning Chen, Ahmet Alatas, Young‐June Kim

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRare-earth and actinide compounds
Canadian institutionsCanadian Light Source (Canada)University of Toronto
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of SaskatchewanNational Research Council CanadaCanada Foundation for InnovationU.S. Department of Energy
KeywordsAnharmonicityExtended X-ray absorption fine structureSkutteruditePhononMaterials scienceScatteringCondensed matter physicsAbsorption (acoustics)Atmospheric temperature rangePhysicsAbsorption spectroscopyOpticsThermal conductivityThermodynamicsThermoelectric materials

Abstract

fetched live from OpenAlex

We report an experimental investigation of Yb filler ion dynamics in partially filled skutterudite samples YbxCo4Sb12 (x=0.26 and x=0.4). From extended x-ray absorption fine structure (EXAFS) measurements at the Yb L2-edge, the mean-square displacements of the Yb fillers were extracted over a wide temperature range from 10 to 500 K. We found that the low-temperature dynamics of Yb fillers are described well using a harmonic oscillator approximation with Einstein temperatures of 70±5 K for both samples. This finding is consistent with the observed low-energy phonon mode at 5.3(5) meV in the inelastic x-ray scattering (IXS) data. However, the temperature dependence deviates from the harmonic approximation at high temperatures due to significant cubic anharmonicity, which was revealed in the EXAFS cumulant analysis. Our study also demonstrates that complementary information on filler dynamics in skutterudites can be acquired using EXAFS and IXS.

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 score0.416
Threshold uncertainty score0.635

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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
Published2021
Admission routes2
Has abstractyes

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