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Record W2087342545 · doi:10.1063/1.4901460

Thermoelectric properties of Sn- and Pb-doped Tl9BiTe6 and Tl9SbTe6

2014· article· en· W2087342545 on OpenAlexafffund
Quansheng Guo, Meghan Chan, Bryan A. Kuropatwa, Holger Kleinke

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeebeck coefficientThermoelectric effectMaterials scienceElectrical resistivity and conductivityThermal conductivityDopantAnalytical Chemistry (journal)DopingThermoelectric materialsHot pressingMineralogyMetallurgyChemistryComposite materialThermodynamicsOptoelectronics

Abstract

fetched live from OpenAlex

A variety of substitutions in Tl9BiTe6 and Tl9SbTe6 with Sn and Pb, amounting to 14 different samples, were performed by melting the stoichiometric amounts of elements at 923 K, followed by slow cooling. The pulverized powders were sintered using the hot-pressing technique. All samples were of single phase according to the powder X-ray diffraction patterns. Thermoelectric property measurements were performed to investigate the effects of Sn- and Pb-doping on the electrical conductivity, Seebeck coefficient, and thermal conductivity. Increasing the concentration of the dopants caused increases in electrical and thermal conductivity, while decreasing the Seebeck coefficient. Tl9Bi0.90Pb0.10Te6 and Tl9Bi0.85Pb0.15Te6 exhibited the highest power factor. The changes in lattice thermal conductivity were minor and did not follow a clear trend. Competitive ZT values were obtained for Tl9Bi0.95Sn0.05Te6, Tl9Bi0.95Pb0.05Te6, Tl9Sb0.97Sn0.03Te6, and Tl9Sb0.95Pb0.05Te6, namely 0.95, 0.94, 0.83, and 0.71 around 500 K, respectively. Higher dopant concentrations led to lower ZT values.

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 score0.012
Threshold uncertainty score0.359

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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