Improved Performance of Air-Processed Perovskite Solar Cells via the Combination of Chlorine Precursors and Potassium Thiocyanate
Bibliographic record
Abstract
Due to their low cost and high efficiency, hybrid perovskite solar cells (PSCs) have shown the most outstanding competitiveness among third-generation photovoltaic (PV) devices. However, several challenges remain unresolved, among which the limited stability is arguably the main. Chlorine (Cl) has been widely employed to yield PV performances, but the Cl-doping mechanism and its role in mixed-halide PSCs are not entirely understood. Here, we investigate the effect of Cl-doping using different precursors such as formamidinium chloride (FACl), cesium chloride (CsCl), and lead chloride (PbCl 2 ), which lead to the incorporation of Cl at different sites of the perovskite crystal. We demonstrate that the stability and efficiency of air-processed PSCs are strongly affected by Cl bonding into the cationic chloride precursor. Furthermore, adding potassium thiocyanate (KSCN) leads to the maximum efficiency of 18.1%, improving the operational stability with only 18% PCE loss after 520 h, stored under ambient conditions. Incorporating CsCl and KSCN presents an effective approach to further boost the performance and thermal stability of PSCs by tailoring the composition of the perovskite’s composition. Finally, we used the slot-die method to demonstrate that our strategy is scalable for large-area devices that have shown similar performance. Our results show that fully air-processed and stable PSCs with high efficiency for large production and commercialization are achievable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".