Effect of air mass on carrier losses in bifacial silicon heterojunction solar cells
Bibliographic record
Abstract
We investigate the effect of incident spectra on current loss as a function of depth and voltage into high efficiency textured bifacial silicon heterojunction solar cells. We integrate thin-film ellipsometry measurements with a 3D optical model and a 2D electronic model and validate our model with measurements of external quantum efficiency and Suns-Voc. For front illumination at normal incidence, an increasing air mass of AM1.5 to 10 reduces current density loss due to parasitic absorption in ITO and a-Si:H from 8.1% to 4.0%, and increases recombination loss at maximum power from 4.2% to 4.7%, resulting in an overall increase in collected current (88.2% to 90.5%). Cell performance metrics are summarized as a function of air mass, with efficiency peaking at AM5.0 for front illuminated and rear illuminated cells with an albedo of unity. We further demonstrate the impact of spectra on bifacial efficiency by calculating rear-side performance with the spectral albedo of dry grass. Overall, current-collection and efficiency trends emphasize the importance of considering spectral effects in energy yield models. These results are of particular importance for cell structures with high bifaciality and significant spectral albedo contributions, locations with large proportions of diffuse light, and high air mass locations as in mid-to-high latitudes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".