Hydrazine Hydrochloride Derivatives Balance Precursor Coordination in Tin‐Based Perovskite Solutions
Bibliographic record
Abstract
Abstract Tin halide perovskites are promising alternatives to lead‐based counterparts for photovoltaics, owing to their analogous electronic configurations, eco‐friendliness, and high charge carrier mobilities. However, their performance and reproducibility are limited by degradations in precursor solutions, solids, and devices, as well as defects arising from rapid and uncontrolled crystallization. While extensive research into solvent and additive engineering of precursor solutions has significantly enhanced the performance of Sn and Sn─Pb perovskite solar cells, the precise working mechanisms at the molecular level remain ambiguous. This study combines computational and experimental methods to elucidate the role of hydrazine‐hydrochloride‐based additives, specifically benzylhydrazine hydrochloride (BHC), in Sn‐containing perovskite precursor solutions. BHC enhances the coordinative interactions among dimethyl sulfoxide, tin‐diiodide, and formamidinium iodide, effectively mitigating the oxidation susceptibility of Sn(II) and stabilizing the precursor solution. Additionally, BHC facilitates halide exchange between I − and Cl − , thus reducing the SnI 4 content and improving crystallization dynamics. Experimental results reveal that BHC incorporation enhances both stability and power conversion efficiency of the Sn─Pb‐perovskite solar cells. This study provides critical insights into the design of advanced perovskite additives for high‐performance photovoltaic applications.
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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) | 0.000 | 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) | 0.000 | 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".