Metallic Lead Formation in Perovskites: Mechanisms, Suppression, and Future Directions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Lead halide perovskites APbX 3 (A = methylammonium, formamidinium, cesium; X = halogen) have advanced the field of optoelectronics, particularly in solar cells, photodetectors, and light-emitting diodes, due to their outstanding properties. However, a significant challenge remains unresolved: the formation of metallic lead (Pb 0 ), which introduces deep-level defects that trap charge carriers and degrade device performance. The formation of Pb 0 in perovskites can occur after their synthesis under various conditions, including high-energy radiation, light, heat, and moisture, and has also been observed during perovskite crystallization. Thus, it is crucial to understand the underlying mechanisms of Pb 0 formation and its suppression pathways. Recent studies have explored various strategies to suppress Pb 0 formation, including compositional engineering, additive incorporation, and protective passivation layers. In this review, we discuss the origins of Pb 0 formation in perovskites, focusing on the mechanisms driving this process under different environmental conditions, and then strategies for suppressing Pb 0 formation, including compositional engineering and passivation techniques. By addressing these aspects, we seek to identify pathways for enhancing the stability and performance of perovskite-based devices, enabling their widespread adoption in commercial applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".