Spectral effects on the energy harvesting efficiency of 2- and 4-terminal tandem photovoltaics
Bibliographic record
Abstract
In this work, we investigate how a varying spectral irradiance and top cell bandgap affect the energy yield of 2T and 4T perovskite//silicon tandem solar cells under outdoor operating conditions. For the comparison, we first validate an optoelectronic model employing a 1-year outdoor data set for a 4T mechanical stacked GaAs on Silicon tandem device. We then use our verified model to simulate perovskite//silicon tandem devices with a varying perovskite top cell bandgap for a location in Golden, Colorado, USA. We introduce a spectral binning method to efficiently reduce and improve the visualization of the 1-min-resolved environmental data while maintaining the simulation accuracy. Our findings reveal that, for a device that is current-matched under standard testing conditions, the annual spectral deviation reduces the energy harvesting efficiency by only 2%$_\mathrm{rel}$. When additional realistic losses for the 4T are taken into account, 2T devices are shown to have an energy-harvesting efficiency that is at parity or higher. Deviations in the top cell bandgap of more than 0.1 eV from current matching result in a reduced energy-harvesting efficiency of more than 5%$_\mathrm{rel}$ for the 2T tandem device.
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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 | 0.001 |
| Open science | 0.001 | 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".