Formation of High‐Performance Multi‐Cation Halide Perovskites Photovoltaics by δ‐CsPbI<sub>3</sub>/δ‐RbPbI<sub>3</sub> Seed‐Assisted Heterogeneous Nucleation
Bibliographic record
Abstract
Abstract The performance of perovskite solar cells is highly dependent on the fabrication method; thus, controlling the growth mechanism of perovskite crystals is a promising way towards increasing their efficiency and stability. Herein, a multi‐cation halide composition of perovskite solar cells is engineered via the two‐step sequential deposition method. Strikingly, it is found that adding mixtures of 1D polymorphs of orthorhombic δ‐RbPbI 3 and δ‐CsPbI 3 to the PbI 2 precursor solution induces the formation of porous mesostructured hexagonal films. This porosity greatly facilitates the heterogeneous nucleation and the penetration of FA (formamidinium)/MA (methylammonium) cations within the PbI 2 film. Thus, the subsequent conversion of PbI 2 into the desired multication cubic α‐structure by exposing it to a solution of formamidinium methylammonium halides is greatly enhanced. During the conversion step, the δ‐CsPbI 3 also is fully integrated into the 3D mixed cation perovskite lattice, which exhibits high crystallinity and superior optoelectronic properties. The champion device shows a power conversion efficiency (PCE) over 22%. Furthermore, these devices exhibit enhanced operational stability, with the best device retaining more than 90% of its initial value of PCE under 1 Sun illumination with maximum power point tracking for 400 h.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".