Enhanced Thermoelectric Performance of SnSe Thin Film via Simultaneous Optimization of Texture and Carrier Concentrations
Bibliographic record
Abstract
ABSTRACT Polycrystalline SnSe thin film materials have gained increasing attention as a promising solution for fabricating microscale, flexible, self‐powered electronic components in the field of thermoelectric (TE) materials and devices. However, it is still a great challenge to simultaneously achieve preferred crystal orientation and optimize carrier concentration for SnSe thin films, which are two crucial factors affecting the TE performance, due to the high volatility of Se. Herein, a simple and scalable method using the magnetron co‐sputtering technique with SnSe 2 and SnSe targets is proposed for preparing highly textured polycrystalline SnSe thin films with appropriate carrier concentration. It was found that during the high‐temperature deposition process, SnSe 2 transforms into SnSe, improving their anisotropy of electronic bands around the valley extrema, inducing localized strain field and stacking faults, and the incorporation of Se facilitates an increase in carrier concentration. The co‐sputtered SnSe thin films show a 45% higher power factor of 2.77 μW cm −1 K −2 compared to that constructed by mono‐sputtered SnSe films with the SnSe target alone. Additionally, localized strain field and stacking faults also serve as centers for phonon scattering, thereby reducing lattice thermal conductivity. Consequently, the estimated zT value of 0.65 at 650 K of the polycrystalline SnSe film reaches a relatively high level.
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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".