Design and numerical analysis of CIGS-based solar cell with V2O5 as the BSF layer to enhance photovoltaic performance
Bibliographic record
Abstract
Copper indium gallium selenide (CIGS)-based solar cells have exhibited greater performance than the ones utilizing cadmium telluride (CdTe) or hydrogenated amorphous silicon (a-Si: H) as the absorber. CIGS-based devices are more efficient, considering their device performance, environmentally benign nature, and reduced cost. In this article, we proposed a potential CIGS-absorber-based solar cell with an FTO/ZnSe/CIGS/V2O5/Cu heterostructure, with a V2O5 back-surface field (BSF) layer, SnO2:F (FTO) window layer, and ZnSe buffer layer. Using the solar cell capacitance simulator one-dimensional simulation software, the effects of the presence of the BSF layer, the thickness, bulk defect density, and acceptor density of the absorber layer, buffer layer thickness, interfacial defect density, device resistance, and operating temperature on the open-circuit voltage, short-circuit current, fill factor, and efficiency, as well as on the quantum efficiency and recombination and generation rate, of the device have been explored in detail. The simulation results revealed that only a 1 μm-thick-CIGS absorber layer with V2O5 BSF and ZnSe buffer layers in this structure offers an outstanding efficiency of 31.86% with a VOC of ∼0.9 V. Thus, these outcomes of the CIGS-based proposed heterostructure provide an insightful pathway for fabricating high-efficiency solar cells with performance more promising than the previously reported conventional designs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".