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 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".