Research on multi-junction solar cells at Fraunhofer ISE: Presentation held at CPV 2017, 13th International Conference on Concentrator Photovoltaic Systems, Ottawa, Canada, May 01 - 03, 2017
Bibliographic record
Abstract
In this work, we investigated the impact of intensive heat loads on concentrator solar cells assemblies. As test samples, we employed lattice-matched and lattice-mismatched triple-junction solar cells made of GaInP/GaInAs/Ge. The thermal loads were induced by different manners. We used drying cabinets, external power supplies, and a combination of both to achieve maximum test temperatures of 180 °C. For the performance analysis, we utilized flash light solar simulators and an electroluminescence (EL) imaging tool. Our experiments revealed a significant difference depending on the applied manner of heating. The highest impact was observed for the pure heat treatment in drying cabinets. This was particularly visible in the spatial EL images, but also in the IV curves. In contrast, running the concentrator solar cells as forward-biased diodes using an external current supply of 2000 mA, which corresponds to 2000 suns, did not lead to any significant changes in EL and IV curves. However, deformation of the front metallization was observed. In conclusions, pure heat treatment can be considered as a cost-efficient alternative to pinpoint weak points in solar cell receivers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".