Combining Efficiency and Stability in Mixed Tin–Lead Perovskite Solar Cells by Capping Grains with an Ultrathin 2D Layer
Bibliographic record
Abstract
Abstract The development of narrow‐bandgap ( E g ≈ 1.2 eV) mixed tin–lead (Sn–Pb) halide perovskites enables all‐perovskite tandem solar cells. Whereas pure‐lead halide perovskite solar cells (PSCs) have advanced simultaneously in efficiency and stability, achieving this crucial combination remains a challenge in Sn–Pb PSCs. Here, Sn–Pb perovskite grains are anchored with ultrathin layered perovskites to overcome the efficiency‐stability tradeoff. Defect passivation is achieved both on the perovskite film surface and at grain boundaries, an approach implemented by directly introducing phenethylammonium ligands in the antisolvent. This improves device operational stability and also avoids the excess formation of layered perovskites that would otherwise hinder charge transport. Sn–Pb PSCs with fill factors of 79% and a certified power conversion efficiency (PCE) of 18.95% are reported—among the highest for Sn–Pb PSCs. Using this approach, a 200‐fold enhancement in device operating lifetime is achieved relative to the nonpassivated Sn–Pb PSCs under full AM1.5G illumination, and a 200 h diurnal operating time without efficiency drop is achieved under filtered AM1.5G illumination.
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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 | 0.001 | 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".