Investigation of Back Surface Field Layer for High Efficiency Ultrathin In<sub>2</sub>S<sub>3</sub> based CIGS Solar Cell
Bibliographic record
Abstract
Abstract A comprehensive study of a novel structure for In2S3 based CIGS solar cell has been observed. The effects of the absorber layer and temperature with various back surface field (BSF) layers (SnS/SnTe/MoTe2/GeTe) are analyzed with the SCAPS‐1D simulator. Performances of the ultrathin CIGS solar cell enhanced with the proposed structure of ZnO:Al/i‐ZnO/In2S3/CIGS/BSF/Mo and efficiency reached over 24% with 1000 nm CIGS absorber layer. The cell with SnS BSF layer has obtained 24.41% efficiency but shows less stability with temperature variation. On the other hand, the cell with MoTe2 BSF shows better stability at a higher temperature and reached an efficiency of 24.14%. Besides, the cell with SnTe BSF also suitable for ultrathin In2S3/CIGS, which results in an efficiency of 23.27%. However, the cell with GeTe BSF can give just over 18% efficiency, but it shows greater stability with temperature changes. This study highlights the potential of BSF layer in In2S3/CIGS solar cell enhancing the performance and stability of cells by reducing recombination losses. The incorporation of a 50 nm BSF layer allows further thinning of the absorber layer, reducing material consumption in the fabrication process without sacrificing overall efficiency.
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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".