Investigation of the Impact of Different Materials on the Efficiency of Lead-free Perovskite Solar Cell
Bibliographic record
Abstract
A solar cell is an electrical device that converts light energy into electrical energy via the photovoltaic effect. Sometimes called a photovoltaic cell or PV cell. In essence, a solar cell is a p-n junction diode. Solar cells are a type of photoelectric cell, which is characterized as an apparatus that changes its electrical properties in response to light, including resistance, voltage, and current. Organic-inorganic halide-based perovskite solar systems are getting closer to commercialization and have become more efficient. Because lead-based perovskite materials have toxicity issues, the scientific community has recently become interested in lead-free alternatives. A lead-free n-i-p based planar heterostructure perovskite solar cell made of intrinsic-CH3NH3SnI3 methyl ammonium tin iodide (MASnI3) as an i- and p-layer Spiro-OMeTAD with SnO2 for the n layer is optimized for device efficiency using SCAPS numerical simulation. The 3rd layer (electron layer) is modified with an efficiency of 2.03%, with another material SnO2, as the efficiency increased to 2.62%, Voc of 0.6428V, Jsc = 6.44 mA/cm2, and FF of 63.40% are achieved. After that the cell layers of the cell are optimized to achieve the highest efficiency of 10.13%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".