Minimizing Performance Loss in Blade‐Coated Large‐Area Perovskite Solar Cells Via Semi‐Sealed Gas Quenching
Bibliographic record
Abstract
Abstract Gas‐quenching of perovskite wet films is widely used in upscaling perovskite solar cells (PSCs). However, due to uneven and turbulent gas stream generated by traditional approaches through air knife or air gun, it is a challenge to induce homogeneous nucleation and produce high‐quality perovskite films suitable for large‐area PSCs. Here this work presents a semi‐sealed gas quenching (SSGQ) strategy that produces homogeneous low‐velocity large‐area high‐pressure gas flow to extract low‐boiling‐point solvents effectively, while leaving behind perovskite intermediates undisturbed that then turn into large crystalline grains. As a result, the SSGQ‐processed perovskite films exhibit improved crystallinity and reproducibility, suppressed defect density and residual stress, as well as compact buried interface and large‐scale uniformity. Such blade‐coated large‐area (1.0 cm 2 ) PSCs with carbon and metal electrodes achieve high power conversion efficiencies (PCEs) of 19.5% and 23.3% (20.5% and 24.2% for 0.04 cm 2 ), both with the lowest PCE loss of ≤1.0% among reported works. This work presents a scalable and affordable approach for fabricating high‐quality perovskite films and high‐performance perovskite photovoltaics, paving the way to PSC commercialization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".