CdSe Quantum Dots Enable High Thermoelectric Performance in Solution‐Processed Polycrystalline SnSe
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".