Compositional Engineering To Improve the Stability of Lead Halide Perovskites: A Comparative Study of Cationic and Anionic Dopants
Bibliographic record
Abstract
The instability of perovskite solar cells is the single greatest barrier to their commercialization. While a number of studies have now looked at the effect of perovskite composition on device stability, many of these have examined only a single compositional variable. With many of these studies having been carried out under different environmental conditions, and still others lacking environmental controls entirely, it is often difficult to compare the relative effect of various cationic or anionic dopants. To address this knowledge gap, we fabricated CH 3 NH 3 PbI 3 -based solar cells where either the methylammonium or iodide ions were replaced with 20 mol % of a dopant ion (ethylammonium, formamidinium, bromide, or chloride). We then assessed their stability either in a controlled 85% relative humidity environment or under 1 sun illumination in air; both conditions have been previously shown to rapidly decompose CH 3 NH 3 PbI 3 . Of the dopants studied, the formamidinium cations imparted the best moisture resistance, and the resulting perovskite displayed the lowest photochemical reactivity. We attribute the improved stability of the formamidinium-doped perovskite to the more delocalized positive charge of the formamidinium cation.
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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 | 0.001 |
| 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".