Determination of individual <i>I</i>(<i>V</i>) characteristics of each sub-cell of a triple junction device
Bibliographic record
Abstract
Very high conversion efficiency is reached with triple junction solar devices integrated in concentrator photovoltaic (CPV) modules. However, reduction of the active area for micro-CPV applications increases the perimeter/area ratio, enhancing losses linked to the edges. It is therefore important to characterize the perimeter influence on the final conversion efficiency. For this purpose, I(V) characterization under dark and/or light could be used as a test of the sidewalls influence. We have designed an experiment to perform I(V) curves using the light of three lasers with adjustable powers at 405, 785, and 980 nm, preferentially absorbed by the top, middle or bottom junction of the device, respectively. This experiment was applied to commercial devices made from a stack of GaInP/GaAs/Ge. In parallel we have developed a numerical calculation modeling the device to reproduce the behaviors observed during I(V) experiments. Junction parameters and influence of leakage resistances are deduced from the fit of experimental results with the numerical calculation. The I(V) experiment as well as the numerical calculation are presented in details. It is also underlined that, combining both experiment and calculation, the I(V) characteristic of each junction as if it was isolated can be determined.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".