20.8% Slot‐Die Coated MAPbI<sub>3</sub> Perovskite Solar Cells by Optimal DMSO‐Content and Age of 2‐ME Based Precursor Inks
Bibliographic record
Abstract
Abstract Solar cells incorporating metal-halide perovskite (MHP) semiconductors are continuing to break efficiency records for solution-processed solar cell devices. Scaling MHP-based devices to larger area prototypes is a crucial step towards commercialization. This requires the development and optimization of scalable process technology for these devices. Here, we demonstrate a maximum power conversion efficiency (PCE) of 20.83% for slot-die coated gas-quenched small-area methylammonium lead iodide (MAPbI3) devices. Our ink is based on 2-methoxy-ethanol (2-ME) with the strongly coordinating solvent dimethyl-sulfoxide (DMSO) added in small amounts. We found that the amount of DMSO, as well as the age of the precursor solutions, are determining factors in achieving highly efficient and reproducible devices. Through in-depth insight into the film formation process as a function of DMSO content from in-situ X-ray diffraction experiments, we found that just the right amount of DMSO favorably affects thin film growth. Adding 11.77 mol% of DMSO prevents the formation of a crystalline intermediate phase related to MAPbI3 and 2-ME (MAPbI3 -2-ME), reported here for the first time, and inducing the formation of some (DMSO)2MA2Pb3I8 intermediate phase. These results demonstrate that ink composition and process control are critical to enable reproducible large-scale manufacturing of MHP-based devices for commercial applications.
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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".