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Record W2950818180 · doi:10.1021/cm0708517

Thermoelectric Properties of Re<sub>3</sub>Ge<sub>0.6</sub>As<sub>6.4</sub> and Re<sub>3</sub>GeAs<sub>6</sub> in Comparison to Mo<sub>3</sub>Sb<sub>5.4</sub>Te<sub>1.6</sub>

2007· article· en· W2950818180 on OpenAlexaff
Navid Soheilnia, Hong Xu, Huqin Zhang, Terry M. Tritt, I. P. Swainson, Holger Kleinke

Bibliographic record

VenueChemistry of Materials · 2007
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThermoelectric effectIsostructuralThermoelectric materialsMaterials scienceSeebeck coefficientDopingElectrical resistivity and conductivityFigure of meritSemiconductorThermal conductivityCrystal structureCondensed matter physicsOptoelectronicsCrystallographyThermodynamicsChemistryComposite materialElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Heavily doped narrow gap semiconductors with complex crystal structures are prime candidates for thermoelectric materials. The arsenides Re 3 (Ge,As) 7 are new examples with promising thermoelectric properties, comparable with the isostructural Mo 3 Sb 5.4 Te 1.6, a competitive high-temperature material. Various doping levels may be achieved by using different Ge/As ratios. Re 3 (Ge,As) 7 was prepared by heating the elements in the desired ratios in evacuated silica tubes between 600 and 800 °C. Re 3 GeAs 6 crystallizes in the cubic Ir 3 Ge 7 type, space group Im 3̄ m, with a = 8.73202(8) Å. It exhibits high Seebeck coefficient, high electrical conductivity, and reasonably low thermal conductivity. Moreover, the thermoelectric figure-of-merit ZT = TS 2 σ/κ increases rapidly with increasing temperature, as desired for high-temperature thermoelectrics.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.001

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 head0.243
Teacher spread0.227 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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