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Record W4400651001 · doi:10.1021/acs.cgd.4c00511

Rattling of Ag Atoms Found in the Low-Temperature Phase of Thermoelectric Argyrodite Ag<sub>8</sub>SnSe<sub>6</sub>

2024· article· en· W4400651001 on OpenAlexfundno aff
Seiya Takahashi, Hidetaka Kasai, Chengyan Liu, Lei Miao, Eiji Nishibori

Bibliographic record

VenueCrystal Growth & Design · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceDavid Suzuki Foundation
KeywordsThermoelectric effectMaterials sciencePhase (matter)Seebeck coefficientThermoelectric materialsCrystallographyChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

A rattling of silver (Ag) atoms is identified in the low-temperature phase of Argyrodite compound Ag 8 SnSe 6 through a systematic multitemperature synchrotron radiation powder diffraction study. Both Bragg and diffuse scatterings from the sample can be well explained by the presence of the Ag rattling atoms. The Rietveld refinements of the low-temperature phase were significantly improved by introducing anisotropic thermal motions of Ag1 and Ag3 sites. The diffuse scattering observed in the low-temperature phase can be well explained by a combination of the first-order thermal diffuse and liquid-like scatterings, which arise due to the rattling of Ag1 and Ag3 sites. The rattling of Ag1 and Ag3 sites is restricted in the perpendicular direction of the Se triangles. This behavior is very similar to that of rattling atoms in the tetrahedrite and tennantite. The present findings are consistent with the low thermal conductivity observed in the low-temperature phase and the existence of low-energy excitation due to neutron scattering.

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 score0.001
Threshold uncertainty score0.005

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 head0.016
GPT teacher head0.254
Teacher spread0.239 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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