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 requires the development and optimization of scalable process technology and ink formulations that enable reproducible coating results. It is demonstrated that the power conversion efficiency (PCE) of small‐area methylammonium lead iodide (MAPbI3) devices, slot‐die coated from a 2‐methoxy‐ethanol (2‐ME) based ink with dimethyl‐sulfoxide (DMSO) used as an additive depends on the amount of DMSO and age of the ink formulation. When adding 12 mol% of DMSO, small‐area devices of high performance (20.8%) are achieved. The effect of DMSO content and age on the thin film morphology and device performance through in situ X‐ray diffraction and small‐angle X‐ray scattering experiments is rationalized. Adding a limited amount of DMSO prevents the formation of a crystalline intermediate phase related to MAPbI3 and 2‐ME (MAPbI3‐2‐ME) and induces the formation of the MAPbI3 perovskite phase. Higher DMSO content leads to the precipitation of the (DMSO)2MA2Pb3I8 intermediate phase that negatively affects the thin‐film morphology. These results demonstrate that rational insights into the 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".