Enhanced efficiency of thin-film solar cells using AgInSe₂ back surface field layer: a SCAPS-1D numerical study
Bibliographic record
Abstract
This study investigates the role of AgInSe₂ (AISe) as a back surface field (BSF) layer in enhancing thin-film photovoltaic (PV) devices with three absorber materials: Cu₂ZnSnS₄ (CZTS), Cu(In,Ga)Se₂ (CIGS), and CuInTe₂ (CIT). Device simulations using SCAPS-1D incorporated a CdS window layer and an AISe BSF layer. Baseline efficiencies without AISe were 17.43% (CZTS), 22.43% (CIGS), and 24.22% (CIT). Introducing AISe significantly boosted power conversion efficiency to 21.83%, 29.19%, and 31.40%, respectively. These improvements stem from enhanced built-in potential and reduced carrier recombination, resulting in higher open-circuit voltage (VOC), short-circuit current density (JSC), and fill factor (FF). Capacitance–voltage analysis and quantum efficiency (QE) profiles further validated the performance gains. Parametric investigations of absorber and BSF properties—thickness, doping, defect density, temperature, and resistance—highlighted their influence on device output. Overall, AISe emerges as a promising BSF material for next-generation thin-film solar cells.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".