14.1% CsPbI<sub>3</sub> Perovskite Quantum Dot Solar Cells via Cesium Cation Passivation
Bibliographic record
Abstract
Abstract Surface manipulation of quantum dots (QDs) has been extensively reported to be crucial to their performance when applied into optoelectronic devices, especially for photovoltaic devices. In this work, an efficient surface passivation method for emerging CsPbI3 perovskite QDs using a variety of inorganic cesium salts (cesium acetate (CsAc), cesium idodide (CsI), cesium carbonate (Cs2CO3), and cesium nitrate (CsNO3)) is reported. The Cs‐salts post‐treatment can not only fill the vacancy at the CsPbI3 perovskite surface but also improve electron coupling between CsPbI3 QDs. As a result, the free carrier lifetime, diffusion length, and mobility of QD film are simultaneously improved, which are beneficial for fabricating high‐quality conductive QD films for efficient solar cell devices. After optimizing the post‐treatment process, the short‐circuit current density and fill factor are significantly enhanced, delivering an impressive efficiency of 14.10% for CsPbI3 QD solar cells. In addition, the Cs‐salt‐treated CsPbI3 QD devices exhibit improved stability against moisture due to the improved surface environment of these QDs. These findings will provide insight into the design of high‐performance and low‐trap‐states perovskite QD films with desirable optoelectronic properties.
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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) | 0.002 | 0.001 |
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".