(Invited) Tubular Thermoelectric Generator for Enhanced Power Generation
Bibliographic record
Abstract
Thermoelectric generators are interesting energy harvesting option due to its clean nature, silent operation and it harnesses the power from otherwise wasted heat. While most of the research has been directed toward high thermoelectric performance based material innovation, we have focused on its architecture and how to connect hot and cold ends when they are far apart. From that perspective, we have devised an effective integration strategy to roll-up otherwise ultra-thin layers of thermoelectric materials to form tubular architecture and to integrate them in array for high power thermoelectric generator development. We have used widely used thermoelectric materials such as bismuth telluride (Bi2Te3) and antimony telluride (Sb2Te3). We also offer an analytical methodology to deduce the effective mechanics of the strain associated with the thermoelectric materials and the used polymeric materials (as stressor and as support layers). Experimentally we have shown seamless 4 cm (and further expandable) tubular arrays of thermoelectric piles. Such a long length allows us to connect to far apart hot and cold end for higher power generation as we also maintain higher temperature difference for longer time. We also compare its effectiveness with solid slab and wire of the same tube from its performance and cost perspective. At the initial stage we report up to 5 μW (8 pairs of p and n-type thermopiles) through a temperature difference of 60 °C. We also show how this can be improved further and potential integration strategy with 2D material system.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".