Study of Carrier Transport in Bifacial Silicon Heterojunction Solar Cells Under High Air Mass Illumination
Bibliographic record
Abstract
The absorption of rear-side irradiance in bifacial solar cells affects carrier generation rates, recombination rates, and series resistance, increasing the complexity of quantifying bifacial cell performance. In this work, recombination rates and the electronic structure of textured bifacial silicon heterojunction solar cells are modelled in Synopsys TCAD Sentaurus. Front and rear illumination conditions vary in air mass from 1.5 to 10.0, while the effect of shifting spectrum and intensity due to snow and dry-grass albedo are applied to rear-side light. Recombination rates in the depletion region remain constant for different illumination scenarios, while throughout the bulk they are ~1 order of magnitude larger for rear illumination compared to front illumination, resulting in a simulated bifaciality within 0.1% of experiment (97.4%). With increasing air mass, the average c-Si recombination probability varies by <; 0.2%. Cell parasitic absorption, on the other hand, drops significantly with air mass, resulting in efficiency enhancements for higher air masses. From AM1.5 to AM5.0, current loss due to parasitic absorption drops from 7.9% to 5.1% and from 7.1% to 4.8% for front and rear illumination, respectively. Likewise, efficiency increases from 21.9% to 22.3% and 21.4% to 21.7%. For rear side AM1.5G illumination, the effect of albedo has an important influence on rear cell performance, with snowy albedo increasing rear-side maximum power by 2× and efficiency by 10% (relative) compared to dry-grass. Thus, the effect of spectral shifts caused by air mass and albedo can play a significant role in bifacial heterojunction energy yield calculations, particularly as collection efficiencies vary with illumination conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".