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Record W4405008907 · doi:10.1021/acsaem.4c02169

Influence of the Densification Process on the Thermoelectric Properties of p-Type SnSe Co-doped with Na and Cu as well as Na and Ag

2024· article· en· W4405008907 on OpenAlexafffund
Andrew Golabek, Nikhil K. Barua, Luke T. Menezes, Ehsan Niknam, Zan Yang, Holger Kleinke

Bibliographic record

VenueACS Applied Energy Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsMcMaster UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermoelectric effectDopingMaterials scienceThermoelectric materialsProcess (computing)Chemical engineeringOptoelectronicsComposite materialThermodynamicsComputer sciencePhysicsThermal conductivityEngineering

Abstract

fetched live from OpenAlex

SnSe has been at the forefront of thermoelectric material research ever since its single crystal was reported to exhibit record-breaking peak zT values in excess of 2 along two directions some 10 years ago. Recent reports indicated the importance of the processing parameters for its transport properties, notably including the purification methods to remove oxide impurities. Here, we investigated the different influences of different consolidation methods on the properties including the stability of SnSe using different p -type dopants, comparing hot pressing with spark plasma sintering. Ultimately, we were able to achieve very comparable results with hot pressing as with spark plasma sintering, which bodes well for the upscaling of the process in industrial settings. In fact, the highest zT value of 0.96 at 673 K was obtained for a hot-pressed sample codoped with Na and Ag. Notably, the Ag-containing samples exhibited higher zT values than those with Cu. However, since all samples began to exhibit cracks after the measurements and inconsistently changing thermoelectric properties, the stability of these materials needs to be improved before any potential usage in a thermoelectric device.

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 score0.026
Threshold uncertainty score0.440

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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