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Record W4402229364 · doi:10.1021/acsami.4c11319

Microstructural Instability and Its Effects on Thermoelectric Properties of SnSe and Na-Doped SnSe

2024· article· en· W4402229364 on OpenAlexafffund
Chun-Wan Timothy Lo, Shaochang Song, Yu‐Chih Tseng, Terry M. Tritt, Jozsef Bogdan, Yurij Mozharivskyj

Bibliographic record

VenueACS Applied Materials & Interfaces · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsNatural Resources CanadaMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceThermoelectric effectMicrostructureDopingTemperature cyclingSeebeck coefficientThermoelectric materialsInstabilityThermalLattice (music)Electrical resistivity and conductivityCondensed matter physicsComposite materialThermal conductivityOptoelectronicsThermodynamicsElectrical engineering

Abstract

fetched live from OpenAlex

Effects of thermal cycling on the microstructure and thermoelectric properties are studied for the undoped and Na-doped SnSe samples using X-ray computed tomography and property measurements. It is observed that thermal cycling causes significant cracks to develop, which decrease both the electrical and lattice thermal conductivities but do not affect the thermopower. The zT values are drastically reduced after the repeated heat treatment. It is important to account for density changes during cycling to obtain accurate values of the thermal conductivity. Even before thermal cycling, the spark-plasma sintered (SPS) samples have a significant number of microcracks. The orientation of cracks within the SPS pellets and their effect on the microstructure are influenced by the presence of a Na-rich impurity. The SnSe and Sn 0.995 Na 0.005 Se samples without the impurity develop cracks and exhibit grain growth parallel to the pellet surface, which is also the plane of the 2D SnSe layers. The Sn 0.97 Na 0.03 Se sample containing the impurity develops cracks that are orthogonal to the pellet surface. Such an orientation of cracks in Sn 0.97 Na 0.03 Se inhibits grain growth. All samples appear mechanically unstable after thermal cycling.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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.

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 routes2
Has abstractyes

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