The Effect of Copper Coating on the Hot-Side on the Performance of a Thermoelectric Generator Using the Electroforming Method
Bibliographic record
Abstract
Increasing the ability to harvest thermal energy on the hot-side of the thermoelectric generator (TEG) module is a challenge for researchers to increase the electrical conductivity of the module.This study aims to increase the absorption of solar heat on the TEG module by adding carbon and copper layers with the electroforming method.The ultimate goal is to increase the electrical energy generated by TEG.The process of adding carbon by painting, while the copper layer by dyeing.The voltage during immersion varied from 2.5, 3, and 3.5 Volt with copper plating durations of 30, 45, and 60 minutes.The results show an increasing trend of solar thermal absorption during testing under the hot sun.The longer it is immersed in the copper layer, the greater the output power of the TEG module.The safe immersion of the module for 45 minutes in Cu solution brought the best positive effect.As a comparison, the output power produced by the TEG module without copper coating is only about 0.000025 Watt for a light intensity of 881.67 Watt/m 2 .After coating, the power generated was increased by 25.1 times at the same intensity.Measurements of temperature and power generated are measured by applying the LabVIEW software application from National Instrument.
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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 | 0.001 |
| 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".