Hot‐Carrier Cooling Regulation for Mixed Sn‐Pb Perovskite Solar Cells
Bibliographic record
Abstract
Abstract The rapid relaxation of hot carriers leads to energy loss in the form of heat and consequently restricts the theoretical efficiency of single‐junction solar cells; However, this issue has not received much attention in tin‐lead perovskites solar cells. Herein, tin(II) oxalate (SnC 2 O 4 ) is introduced into tin‐lead perovskite precursor solution to regulate hot‐carrier cooling dynamics. The addition of SnC 2 O 4 increases the length of carrier diffusion, extends the lifetime of carriers, and simultaneously slows down the cooling rate of carriers. Furthermore, SnC 2 O 4 can bond with uncoordinated Sn 2+ and Pb 2+ ions to regulate the crystallization of perovskite and enable large grains. The strongly reducing properties of the C 2 O 4 2− can inhibit the oxidation of Sn 2+ to Sn 4+ and minimize the formation of Sn vacancies in the resulting perovskite films. Additionally, as a substitute for tin(II) fluoride, the introduction of SnC 2 O 4 avoids the carrier transport issues caused by the aggregation of F – ions at the interface. As a result, the SnC 2 O 4 ‐treated Sn‐Pb cells show a champion efficiency of 23.36%, as well as 27.56% for the all‐perovskite tandem solar cells. Moreover, the SnC 2 O 4 ‐treated devices show excellent long‐term stability. This finding is expected to pave the way toward stable and highly efficient all‐perovskite tandem solar cells.
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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".