Lead free <scp> CsSn <sub>0</sub> </scp> <sub>.</sub> <scp> <sub>5</sub> Ge <sub>0</sub> </scp> <sub>.</sub> <scp> <sub>5</sub> I <sub>3</sub> </scp> perovskite solar cell with different layer properties via <scp>SCAPS‐1D</scp> simulation
Bibliographic record
Abstract
Abstract Organic–inorganic halide perovskite solar cells (PSCs) have been extensively studied due to their simple fabrication methods and obvious device efficiency advantages. In this work, the perovskite CsSn 0.5 Ge 0.5 I 3 is used as the light absorption layer, which is doped with Ge 2+ in CsSnI 3 to improve its stability. The polymers of 3‐hexylthiophene (P3HT) with excellent optoelectronic properties and low price, and SnO 2 with high electron extraction ability is selected as charge transport layers. Based on these, a novel PSC structure (FTO/SnO 2 /IDL1/CsSn 0.5 Ge 0.5 I 3 /IDL2/P3HT/Au) has been simulated via solar cell capacitor simulator (SCAPS‐1D). The PSC performance is optimized by adjusting a series of parameters, including the layer thickness, defect density, electron affinity potential energy, and operating temperature, and so forth. The results show that the PSC defects are passivated by adjusting the appropriate parameters, and the final optimized open circuit voltage ( V OC ) is 1.08 V, short‐circuit current density ( J SC ) is 27.37 mA/cm 2 , fill factor (FF) is 83.32%, while the power conversion efficiency (PCE) is increased from the initial 10.89% to 24.63%, which provides theoretical reference for experiments and new ideas for the preparation and development of efficient and environmentally friendly PSCs. Finally, the effect of different metal cathodes with and without hole transport layer (HTL) on PSC performance is compared. The PSCs without HTL are more dependent on battery cathodes, which provided a way to replace precious metals with other electrode materials.
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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) | 0.003 | 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".