Efficiency Measurements and Simulations of GaInP∕InGaAs∕Ge Quantum Dot Enhanced Solar Cells at up to 1000-Suns Under Flash and Continuous Concentration
Bibliographic record
Abstract
Quantum dot (QD) enhanced GaInP/InGaAs/Ge solar cells are presented and characterized under flash and continuous solar simulators. InAs QD within the middle sub‐cell increase the carrier generation due to absorption in the range 900–940 nm. These QD‐enhanced solar cells routinely achieve production efficiencies of ∼40%, and this set of research samples obtain a peak efficiency of >38% under flash solar simulators. Continuous solar simulator testing is performed to test the QD‐enhanced solar cells under thermal loads similar to concentrated photovoltaic systems, in which cells demonstrate excellent reliability. Numerical simulations of the QD‐enhanced solar cells are performed using an effective medium to model the additional absorption due to the QD layers. Temperature dependence of the QD‐enhanced solar cells are modeled, in which temperature‐dependent bandgap narrowing changes the dark current and the semiconductor absorption profiles. Comparison between the experimental results and numerical model show good agreement under flash testing. Further work is needed to improve the match between simulation and experiment under continuous solar simulators.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".