Influence of the optical and recombination losses on the efficiency of CdS/CdTe solar cell at ultrathin absorber layer
Bibliographic record
Abstract
The effect of low thickness of CdTe on optical, front, and back recombination losses and hence on the efficiency of a CdS/CdTe solar cell is studied theoretically in this work. It is found that the optical losses are about 23%–24% and depend weakly on CdTe thickness. The recombination losses are about 28% at dCdTe = 0.45 μm and decrease to 23% at dCdTe = 1.1 μm because the recombination losses have significant effects at thinner layers. The recorded efficiency is in the 9.5%–10.5% range corresponding to the thickness of CdTe of 0.45–1.1 μm and it is considered in good agreement with experimental results. The electron diffusion length is in the range of 1.6–15.8 μm and corresponds to 10−9–10−7 s of the electron lifetime is sufficient to make the current density reaches its maximum value (16.3 mA/cm2) at dCdTe = 1.1 μm with efficiency of 10.5%.When the CdTe thickness is assumed to be 5 μm, which is often used in the fabrication of CdTe-based solar cells, the calculated current density is about 20 mA/cm2 and the corresponding efficiency is 13%. The present results lead to the shrinking of the gap between the theoretical and practical results and contribute to improving the efficiency of CdS/CdTe cells in the future.
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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 | 0.001 | 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".