Systematic Studies of the High Output Thermoelectric Power Generation
Bibliographic record
Abstract
Authors have methodically investigated the alternative energy technologies based upon thermoelectricity generation. Firstly, its power is systematically investigated under various work conditions in thermoelectric applications. In addition, they have modeled, designed, and constructed the thermoelectric power system. Moreover, they have invented a state-of-the-art table-top instrument that may evaluate several critical thermoelectric characters in situ. Several aspects of the thermoelectric features are characterized in situ that include the efficiency, force response curve, current-voltage (i.e., I-V) curve, power-voltage (P-V) curve, and the power versus temperature (P-T) responses. Furthermore, they have successfully built a high-power heat harvester and have applied to the automotive case study in details. Finally, they have obtained the multi-stack thermoelectric devices that have improved characters; e.g., both the power output and the thermoelectric efficiency have improved in comparison to the devices commercially available. The investigation leads to 19+% efficiency in triple stack devices and 10+% in dual-stack.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".