Synergistic Passivation of Bulk and Interfacial Defects Improves Efficiency and Stability of Inverted Perovskite Solar Cells
Bibliographic record
Abstract
Defects both in bulk and at the interfaces serve as charge trapping sites for nonradiative recombination and as ion migration pathways, resulting in degradation of perovskite solar cell efficiency and stability. In this work, a strategy for simultaneous passivation of both bulk and interfacial defects is reported. For bulk passivation polystyrene (PS) is used as an additive in the perovskite precursor which reduces the structural defects by forming larger defect‐free grains. While the F‐PEAI cation is used to passivate the interfacial defects, present at both perovskite HTL/ETL interfaces. Furthermore, by conducting control measurements with just bulk modification (PS), just interface modification (F‐PEAI), and a combination of both, the role of individual defect passivation strategies is decoupled. As a result of simultaneous bulk as well as interfacial passivation, the modified perovskite solar cell shows the highest efficiency of 22.32% with a high V oc of 1.14 V and fill factor of 80%. Moreover, the cells have excellent stability retaining 92% and 99% of their initial efficiency after 1008 h and 560 h under ISOS‐ D1 and D2 storage conditions. These results highlight the importance of simultaneous bulk and interfacial passivation for improving solar cell efficiency and stability.
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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".