| 1. |
- Huang, Yuting, et al.
(author)
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Host-Guest Strategy Enabling Nonhalogenated Solvent Processing for High-Performance All-Polymer Hosted Solar Cells
- 2023
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In: Chinese journal of chemistry. - : WILEY-V C H VERLAG GMBH. - 1001-604X .- 1614-7065. ; 41:9, s. 1066-1074
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Journal article (peer-reviewed)abstract
- The power conversion efficiencies (PCEs) of all-polymer solar cells (all-PSCs), usually processed from low-boiling-point and toxic solvents, have reached high values of 18%. However, poor miscibility and uncontrollable crystallinity in polymer blends lead to a notable drop in the PCEs when using green solvents, limiting the practical development of all-PSCs. Herein, a third component (guest) BTO was employed to optimize the miscibility and enhance the crystallinity of PM6/PY2Se-F host film processed from green solvent toluene (TL), which can effectively suppress the excessive aggregation of PY2Se-F and facilitate a nano-scale interpenetrating network morphology for exciton dissociation and charge transport. As a result, TL-processed all-polymer hosted solar cells (all-PHSCs) exhibited an impressive PCE of 17.01%. Moreover, the strong molecular interaction between the host and guest molecules also enhances the thermal stability of the devices. Our host-guest strategy provides a unique approach to developing high-efficiency and stable all-PHSCs processed from green solvents, paving the way for the industrial development of all-PHSCs.
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| 2. |
- Rao, Ziyuan, et al.
(author)
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Machine learning-enabled high-entropy alloy discovery
- 2022
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 378:6615, s. 78-84
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Journal article (peer-reviewed)abstract
- High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 x 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.
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