Effect of carbon containing SiN<sub>x</sub> antireflection coating on the screen‐printed contact and low illumination performance of silicon solar cell
Bibliographic record
Abstract
ABSTRACT Screen‐printed metal contact formation through a carbon containing antireflection coating was investigated for silicon solar cells by fabricating conventional carbon‐free SiNx and carbon‐rich SiCxNy film. An appreciable difference was found in the average shunt resistance (Rsh), which was about an order of magnitude higher for SiCxNy‐coated solar cells relative to the counterpart SiNx‐coated solar cells. Series resistance (Rs) and fill factor (FF) were comparable for both antireflection coatings but the starting efficiency of SiCxNy‐coated cell was ~0·2% lower because of slightly inferior surface passivation. However, SiCxNy‐coated solar cells showed less degradation under lower illumination (<1000 W/m2) compared with the SiNx‐coated cells due to reduced FF degradation under low illumination. Theoretical calculations in this paper support that this is a direct result of high Rsh. Detailed photovoltaic system and cost modeling is performed to quantify the enhanced energy production and the reduced levelized cost of electricity due to higher shunt resistance of the SiCxNy‐coated cells. It is shown that Rsh value below 30 Ω (7000 Ω cm2 for 239 cm2 cell) can lead to appreciable loss in energy production in regions of low solar insolation. Copyright © 2011 John Wiley & Sons, Ltd.
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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".