Ionic Liquid-Assisted MAPbI<sub>3</sub> Nanoparticle-Seeded Growth for Efficient and Stable Perovskite Solar Cells
Bibliographic record
Abstract
With the rapid improvement of perovskite solar cells (PSCs), long-life operational stability has become a major requirement for their commercialization. In this work, we devised a pristine cesium–formamidinium–methylammonium (termed as CsFAMA) triple-cation-based perovskite precursor solution into the ionic liquid (IL)-assisted MAPbI 3 nanoparticles (NPs) through a seeded growth approach in which the host IL-assisted MAPbI 3 NPs remarkably promote high-quality perovskite films with large single-crystal domains, enhancing the device performance and stability. The power conversion efficiency (PCE) of the MAPbI 3 NP-seeded growth of MAPbI 3 NPs/CsFAMA-based PSCs is as high as 19.44%, which is superior to those of MAPbI 3 NPs and pristine CsFAMA films as the photoactive layer (9.52 and 17.33%, respectively). The long-term light-soaking and moisture stability of IL-aided MAPbI 3 NPs/CsFAMA-based devices (non-encapsulated) remain above 90 and 80%, respectively, of their initial output after 2 h of light illumination (1 sun) and 6000 h storage at ambient with a relative humidity range of 30–40%. The use of the IL-assisted MAPbI 3 NP-seeded growth for PSCs is a significant step toward developing stable and reliable perovskite photovoltaic devices.
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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".