Trace doping leads to texture engineering and boosts thermoelectric performance of Bi0.5Sb1.5Te3-based flexible thin films
Bibliographic record
Abstract
The p -type Bi 0.5 Sb 1.5 Te 3 -based flexible thermoelectric thin films demonstrate exceptional potential for on-chip thermal regulation of next-generation wearable electronics. However, the inferior thermoelectric performance deriving from low carrier mobility, is refraining their wide applications. Taking magnetron-sputtering-prepared Bi 0.5 Sb 1.5 Te 3 -based flexible thermoelectric thin films as examples, this study demonstrates that trace Cu doping can effectively boost the formation of (00 l )-preferred texture, resulting in substantially improved thermoelectric performance. Cu doping preferentially replaces Sb atoms, forming Cu Sb point defects, decreases the surface formation energy of (00 l ) planes, and boosts the formation of (00 l )-preferred texture. The strengthened (00 l )-preferred texture leads to the high carrier mobility of ~23.54 cm 2 V −1 s −1 and excellent electrical conductivity of ~755.05 S cm −1 due to weakened carrier scattering. Consequently, a high room-temperature power factor to ~13.76 μW cm −1 K −2 is achieved. This study demonstrates a trace composition design can effectively boost the formation of (00 l )-preferred texture in Bi 0.5 Sb 1.5 Te 3 -based flexible thermoelectric thin films, contributing to high thermoelectric performance, and extend their application potentials. • Minor Cu-doping can boost the formation of ( 00 l )-preferred texture. • Strengthened texture enhances carrier transport and electrical conductivity. • The thin film with a maximum power factor of ~13.76 μW cm −1 K −2 was achieved. • A device generates a power density of ~121.65 μW cm −2 at ΔT of ~30 K.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".