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Increase in the density of states in n-type extruded (Bi<sub>(1−x)</sub>Sb<sub>x</sub>)<sub>2</sub>(Te<sub>(1−y)</sub>Se<sub>y</sub>)<sub>3</sub> thermoelectric alloys

2011· article· en· W2035539009 on OpenAlexafffund
Chantal André, D. Vasilevskiy, S. Turenne, R. A. Masut

Bibliographic record

VenueJournal of Physics D Applied Physics · 2011
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeebeck coefficientMaterials scienceThermoelectric effectThermal conductivityCrystalliteExtrusionDimensionless quantityFigure of meritDopingCondensed matter physicsElectrical resistivity and conductivityAnalytical Chemistry (journal)Thermoelectric materialsMetallurgyComposite materialThermodynamicsChemistryOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The concentration and doping of n-type doped (Bi (1− x ) Sb x ) 2 (Te (1− y ) Se y ) 3 thermoelectric alloys produced by powder metallurgy followed by hot extrusion are varied in order to optimize their performance for the generation of electricity. The material is polycrystalline and strongly textured, with an undetermined volumetric fraction of nanoscale subgrains, and its thermoelectric properties are optimal along the extrusion direction. Within the composition range 0 ⩽ x , y ⩽ 0.1 the quaternary (Bi 0.97 Sb 0.03 ) 2 (Te 0.93 Se 0.07 ) 3 shows the highest temperature-averaged dimensionless figure of merit ⟨ ZT ⟩ for applications where T C = 295 K and T H = 420 K. This average ⟨ ZT ⟩ is further optimized for values of carrier concentrations close to n = 3.4 × 10 19 cm −3 . The introduction of substitution elements constituting these quaternary alloys leads to an increase in the electronic equivalent density of states compared with Bi 2 Te 3 . This increase has a direct impact on the Seebeck coefficient, the electronic contribution to the thermal conductivity and the carrier mobility.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
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 score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.003
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2011
Admission routes2
Has abstractyes

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