Fabrication of large-scale single-crystal bismuth telluride (Bi<sub>2</sub>Te<sub>3</sub>) nanosheet arrays by a single-step electrolysis process
Bibliographic record
Abstract
Nanolizing of thermoelectric materials is one approach to reduce the thermal conductivity and hence enhance the figure of merit. Bismuth telluride (Bi₂Te₃)-based materials have excellent figure of merit at room temperature. For device applications, precise control and rapid fabrication for the nanostructure of thermoelectric materials are essential issues. In the present study, we demonstrate a one-step electrolysis process to directly form Bi₂Te₃ nanosheet arrays (NSAs) on the surface of bulk Bi₂Te₃ with controllable spacing distance and depth by tuning the applied bias and duration. The single sheet of NSAs reveals that the average thickness and electrical resistivity of single crystalline Bi₂Te₃ in composition are 399.8 nm and 137.34 μΩ m, respectively. The formation mechanism of NSAs has been proposed. A 1.12% efficiency of quantum dot-sensitized solar cells with Bi₂Te₃ NSAs for counter electrode has been demonstrated, indicating that Bi₂Te₃ NSAs from top-down processing with a high ratio of surface area to volume are a promising candidate for possible applications such as thermoelectrics, dye-sensitized solar cells (DSSCs), and lithium-ion batteries.
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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".