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| Hauptseite > Publikationsdatenbank > Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning > print |
| Hauptseite > Publikationsdatenbank > Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning > print |
| 001 | 892610 | ||
| 005 | 20240712113029.0 | ||
| 024 | 7 | _ | |a 10.1038/s41467-021-22472-x |2 doi |
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| 100 | 1 | _ | |a Zhao, Yicheng |0 P:(DE-Juel1)187394 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning |
| 260 | _ | _ | |a [London] |c 2021 |b Nature Publishing Group UK |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C. |
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| 700 | 1 | _ | |a Sun, Shijing |0 0000-0002-6179-1390 |b 3 |
| 700 | 1 | _ | |a Langner, Stefan |0 P:(DE-Juel1)180636 |b 4 |
| 700 | 1 | _ | |a Hartono, Noor Titan Putri |0 0000-0002-0748-0620 |b 5 |
| 700 | 1 | _ | |a Heumueller, Thomas |0 0000-0002-6974-410X |b 6 |
| 700 | 1 | _ | |a Hou, Yi |0 0000-0002-1532-816X |b 7 |
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| 700 | 1 | _ | |a Li, Ning |0 P:(DE-Juel1)180778 |b 9 |
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| 700 | 1 | _ | |a Feng, Yexin |0 0000-0003-1302-7555 |b 16 |
| 700 | 1 | _ | |a Hauch, Jens |0 P:(DE-Juel1)177626 |b 17 |
| 700 | 1 | _ | |a Sargent, Edward H. |0 0000-0003-0396-6495 |b 18 |
| 700 | 1 | _ | |a Buonassisi, Tonio |0 0000-0001-8345-4937 |b 19 |
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| 773 | _ | _ | |a 10.1038/s41467-021-22472-x |g Vol. 12, no. 1, p. 2191 |0 PERI:(DE-600)2553671-0 |n 1 |p 2191 |t Nature Communications |v 12 |y 2021 |x 2041-1723 |
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