Formamidinium's (FAI) Impact on α‐CsPbI<sub>3</sub> Perovskite Stability in Ambient Air: A Path for Highly Efficient Perovskite–Perovskite Tandem Solar Cells
Bibliographic record
Abstract
Cesium lead triiodide (CsPbI3) perovskites are known for their instability, particularly under ambient conditions, where they often degrade from the preferred black‐phase (α‐phase) to the nonperovskite yellow‐phase (δ‐phase). This phase transition causes a loss of optical performance, which drastically lowers the solar cell's durability and efficiency. To solve this problem, we explored adding formamidinium iodide (FAI) as a CsPbI3 stabilizer agent. By adding FAI, we facilitate the transition from the less stable δ phase to the more stable and optically active α‐phase. This modification enhances the crystallinity of the material, reduces the density of defects, and improves the mobility of charge carriers, all of which improve device performance. Our results show a noticeable increase in solar cell efficiency after FAI incorporation. Theoretical calculations have shown that with single‐junction devices, the PCE was enhanced from 23.12% to 26.9%. Furthermore, the material becomes more stable over time, especially as compared to its original unstable structure. Finally, we integrated CsPbI3 into tandem perovskite–perovskite solar cells for the first time, achieving a ground‐breaking efficiency of 32%. These advancements represent a significant leap forward for perovskite‐based solar technologies. The promising outcomes of this research are under active consideration for commercialization, paving the way for the practical use of CsPbI3‐based solar technologies.
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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 | 0.000 | 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".