Structural, Morphological, Optical Properties and Modelling of Ag Doped CuO/ZnO/AZO Solar Cell
Bibliographic record
Abstract
Copper (II) oxide (CuO) has attained significant attention from researchers because of its unique chemical and physical properties. Ag-doped CuO thin films have been produced on the soda glass substrate (SLG) by spin coating technique at different doping ratios. Structural, morphological, and optical properties of thin films produced depending on altered silver ratios have been examined through X-ray diffraction (XRD) spectroscopy, scanning electron microscopy (SEM), and UV-vis absorption spectroscopy, respectively. Band gaps of prepared undoped and 1% Ag-doped CuO thin films have been measured as 1.90eV and 1.63eV, respectively. Ag/undoped CuO and Ag-doped CuO/ZnO/AZO solar cells have been modelled, and their photovoltaic parameters have also been calculated using the SCAPS-1D simulation program. This work aims to investigate the photovoltaic parameters that would improve the efficiency of a solar cell. The effect of Ag atoms on the efficiency of CuO solar cells has been investigated depending on the acceptor density (Na), the interface defect density (Nt), and the operating temperature. Ag-doped CuO solar cells have shown the highest efficiency for Nt=1010 cm-3 and Na=6, 5x1016 cm-3 values. It has been well observed that as the operating temperature increases, the solar cells’ power conversion efficiency decreases. The highest charge generation rates in the undoped and Ag-doped solar cells have been determined as 1.49×1022 1/cm3.s and 1.51×1025 1/cm3.s, respectively. All the results, either theoretical or experimental, have been presented in this work and have been compared for a conclusion that has been made in detail.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".