Facet Orientation Modulation for High-Performance of Printable Mesoscopic Perovskite Solar Cells
Bibliographic record
Abstract
Carbon-based printable mesoscopic perovskite solar cells (p-MPSCs) offer substantial advantages for industrial production due to their facile fabrication, low cost, and scalability. In p-MPSCs, however, perovskites undergo disordered crystallization with multiple facets within the complex scaffold of mesoporous TiO 2 (mp-TiO 2 )/mesoporous ZrO 2 (mp-ZrO 2 )/mesoporous carbon (mp-C), resulting in film strain accumulation and restricted performance enhancement. To tackle this issue, we propose a facet orientation modulation strategy by introducing potassium sulfamate (ASK) to release strain accumulation in perovskite films. ASK exhibits a strong adsorption capability on the (001) crystal facet through interactions with the octahedral lattice, thereby promoting the formation of the (001) facet and enabling the preferentially oriented growth of perovskite films along this plane. Moreover, ASK effectively reduces the perovskite crystallization rate, allowing sufficient lattice reorganization and thus relieving residual stress during crystal growth. Ultimately, p-MPSCs employing this facet orientation modulation strategy achieved a champion power conversion efficiency (PCE) of 20.10%. ASK-optimized p-MPSCs retained 93% of their initial PCE after 150 days of storage in ambient air at room temperature, exhibiting excellent long-term stability.
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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".