Gas Quenching under Ambient Conditions for Efficient and Stable Wide-Bandgap Perovskite Solar Cells with Surface Passivation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Wide-bandgap perovskite solar cells play a key role in tandem solar cells, which aim to overcome the Shockley–Queisser limit for single-junction solar cells. In this work, we develop and optimize a gas quenching method under ambient conditions for the fabrication of wide-bandgap (1.77 eV) perovskite films. To improve the performance of PSCs, three different organic spacer cations, including aromatic amino molecules (PEAI), aliphatic amino with long alkyl chain molecules (OAI), and short alkyl chain molecules (BAI), are applied and investigated as surface passivation materials. As a result, the 2D perovskite layers form on top of the 3D perovskite films. The n-i-p devices with PEAI passivation exhibit the highest photovoltaic performance with a champion power conversion efficiency (PCE) of 16.26% along with a high V oc of 1.21 V, exceeding the control device (PCE = 13.42%, V oc = 1.15 V), and maintaining 88% of its initial PCE after 120 min of continuous illumination under a nitrogen atmosphere at room temperature. This work offers a guide for the fabrication of wide-bandgap PSCs under ambient conditions and the choice of organic spacer cations for passivation.
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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".