Development of Nanostructured Bi<sub>2</sub>Te<sub>3</sub> with High Thermoelectric Performance by Scalable Synthesis and Microstructure Manipulations
Bibliographic record
Abstract
Nanostructuring of thermoelectric (TE) materials leads to improved energy conversion performance; however, it requires a perfect fit between the nanoprecipitates’ chemistry and crystal structure and those of the matrix. We synthesize bulk Bi 2 Te 3 from molecular precursors and characterize their structure and chemistry using electron microscopy and analyze their TE transport properties in the range of 300–500 K. We find that synthesis from Bi 2 O 3 + Na 2 TeO 3 precursors results in n-type Bi 2 Te 3 containing a high number density ( N v ∼ 2.45 × 10 23 m –3 ) of Te-nanoprecipitates decorating the Bi 2 Te 3 grain boundaries (GBs), which yield enhanced TE performance with a power factor (PF) of ∼19 μW cm –1 K –2 at 300 K. First-principles calculations validate the role of Te/Bi 2 Te 3 interfaces in increasing the charge carrier concentration, density of states, and electrical conductivity. These optimized TE coefficients yield a promising TE figure of merit ( zT ) peak value of 1.30 at 450 K and an average zT of 1.14 from 300 to 500 K. This is one of the cutting-edge zT values recorded for n-type Bi 2 Te 3 produced by chemical routes. We believe that this chemical synthesis strategy will be beneficial for future development of scalable n-type Bi 2 Te 3 based devices.
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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".