Optimization of a-Si<sub>1–<i>x</i></sub>Ge<sub><i>x</i></sub>:H single-junction and a-Si:H/a-Si<sub>1–<i>x</i></sub>Ge<sub><i>x</i></sub>:H tandem solar cells with enhanced optical management
Bibliographic record
Abstract
This work aimed at improving the optical management of a-Si:H/a-Si 1–x Ge x :H tandem cells and a-Si 1–x Ge x :H single-junction cells. To improve the optical management, the effects of the a-Si 1–x Ge x :H bandgap, the bandgap graded absorber and the n-type μc-SiO x :H back reflecting layer on the cell performance were investigated. Optical reflection spectra, internal quantum efficiency, external quantum efficiency (EQE), and cell performance were used to evaluate the improvement of the optical properties of solar cells. The tandem cells with a-Si 1–x Ge x :H bandgap of 1.53 eV exhibited sufficient optical absorption from 630 to 900 nm and thus lead to higher J SC . Second, the EQE of a-Si 1–x Ge x :H single-junction cell was significantly enhanced from 630 to 720 nm by employing bandgap graded absorber that relatively improved the J SC by 3.8% despite that the reduction in EQE from 720 to 900 nm compared to the cell without bandgap grading. Moreover, the μc-SiO x :H(n)/Ag back reflector showed higher optical reflection than a-Si:H(n)/Ag did, which relatively improved the J SC by 12.3%. The cell with μc-SiO x :H(n)/Ag back reflector exhibited a comparable J SC and efficiency to the cell with ITO–Ag. The previously mentioned approaches are relevant to enhance the optical management in cells and can be applied to silicon-based thin-film solar cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".