Effect of Barrier Material Accumulation on the Performance of Bi<sub>2</sub>Te<sub>3</sub>-Based Thermoelectric Generators
Bibliographic record
Abstract
The barrier material placed between thermoelectric (TE) materials and electrodes plays a dominant role in the performance of a thermoelectric generator (TEG). Nickel (Ni) is widely used as a barrier material in Bi 2 Te 3 -based TEGs. Since the electroplating of Ni is an efficient technique in the coating of barrier materials, we observe that the barrier material accumulation on the surface of TE materials greatly restricts the output power and conversion efficiency of TEGs. Specifically, for an interface with accumulated barrier material, the output power can be reduced by more than 30%, while the interfacial strength can be reduced by around 18%. By optimizing the electroplating process, we realize a maximum output power of 0.62 W and a maximum conversion efficiency of 5.1% in a Bi 2 Te 3 -based TEG, which is much higher than that of commercial TEGs. Simultaneously, the bonding strength of the uniform interface reaches 49.36 N, which is 22.6% higher than that of the interface with barrier material accumulation. These results guide the design and fabrication of high-performance TEGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".