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Record W4391512611 · doi:10.1002/smll.202311153

CdSe Quantum Dots Enable High Thermoelectric Performance in Solution‐Processed Polycrystalline SnSe

2024· article· en· W4391512611 on OpenAlexaff
Wei Dou, Yaru Gong, Xinqi Huang, Yanan Li, Qingtang Zhang, Yuqi Liu, Qinxuan Xia, Qingyang Jian, Deshang Xiang, Di Li, Dewei Zhang, Shihua Zhang, Pan Ying, Guodong Tang

Bibliographic record

VenueSmall · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsQuantum dotMaterials scienceSeebeck coefficientThermoelectric effectCrystalliteX-ray photoelectron spectroscopyOptoelectronicsThermoelectric materialsPhonon scatteringCondensed matter physicsThermal conductivityPhononElectrical resistivity and conductivityNanotechnologyChemical engineeringComposite materialMetallurgyElectrical engineering

Abstract

fetched live from OpenAlex

Here, a high peak ZT of ≈2.0 is reported in solution-processed polycrystalline Ge and Cd codoped SnSe. Microstructural characterization reveals that CdSe quantum dots are successfully introduced by solution process method. Ultraviolet photoelectron spectroscopy evinces that CdSe quantum dots enhance the density of states in the electronic structure of SnSe, which leads to a large Seebeck coefficient. It is found that Ge and Cd codoping simultaneously optimizes carrier concentration and improves electrical conductivity. The enhanced Seebeck coefficient and optimization of carrier concentration lead to marked increase in power factor. CdSe quantum dots combined with strong lattice strain give rise to strong phonon scattering, leading to an ultralow lattice thermal conductivity. Consequently, high thermoelectric performance is realized in solution-processed polycrystalline SnSe by designing quantum dot structures and introducing lattice strain. This work provides a new route for designing prospective thermoelectric materials by microstructural manipulation in solution chemistry.

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 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 score0.025
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.221
Teacher spread0.210 · 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

Citations20
Published2024
Admission routes1
Has abstractyes

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