表征(材料科学)
有机半导体
材料科学
纳米技术
有机太阳能电池
化学物理
有机电子学
无定形固体
纳米尺度
半导体
晶体管
光电子学
化学
聚合物
有机化学
物理
复合材料
电压
量子力学
作者
Martin Seifrid,G. N. Manjunatha Reddy,Bradley F. Chmelka,Guillermo C. Bazan
标识
DOI:10.1038/s41578-020-00232-5
摘要
Organic semiconductors (OSCs) are of fundamental and technological interest, owing to their properties and functions in a range of optoelectronic devices, including organic light-emitting diodes, organic photovoltaics and organic field-effect transistors, as well as emerging technologies, such as bioelectronic devices. The solid-state organization of the subunits in OSC materials, whether molecular or polymeric, determines the properties relevant to device performance. Nevertheless, the systematic relationships between composition, structure and processing conditions are rarely fully understood, owing to the complexity of the organic architectures and the resulting solid-state structures. Characterization over different length scales and timescales is essential, especially for semi-ordered or amorphous regions, for which solid-state NMR (ssNMR) spectroscopy yields nanoscale insight that can be correlated with scattering measurements and macroscopic property analyses. In this Review, we assess recent results, challenges and opportunities in the application of ssNMR to OSCs, highlighting its role in state-of-the-art materials design and characterization. We illustrate how insight is obtained on local order and composition, interfacial structures, dynamics, interactions and how this information can be used to establish structure–property relationships. Finally, we provide our perspective on applying ssNMR to the next generation of OSCs and the development of new ssNMR methods. The structure of organic semiconductor thin films influences their performance in optoelectronic devices. This Review highlights how solid-state NMR techniques can be used to investigate the structure, composition and dynamics of organic semiconductors and, thus, establish structure–property relationships.
科研通智能强力驱动
Strongly Powered by AbleSci AI