标题: Pushing Nonlinear Optical Oxides into the Mid-Infrared Spectral Region Beyond 10 mum: Design, Synthesis, and Characterization of La3SnGa5O14.
作者: Lan, Haichao; Liang, Fei; Jiang, Xingxing; Zhang, Cong; Yu, Haohai; Lin, Zheshuai; Zhang, Huaijin; Wang, Jiyang; Wu, Yicheng
作者机构:[ Lan, Haichao; Lan H] State Key Laboratory of Crystal Materials and Institute of Crystal Materials, Shandong University , Jinan 250100, China.;[ Lian 更多
来源: Journal of the American Chemical Society
出版年:2018
DOI:10.1021/jacs.8b01009
摘要: Mid-infrared (mid-IR) coherent light is crucial for several applications in science as well as daily life, and its development especially in powerful augmentation is constrained by the availability of nonlinear optical (NLO) materials. The development of useful mid-IR NLO materials is limited by the requirements of a wide mid-IR transparent window, high laser damaged threshold (LDT), and strong NLO effect. It is common knowledge that oxides are not suitable mid-IR NLO materials, as their IR absorption cutoff wavelengths are usually <6 mum; however, their LDTs and NLO effects can be large. Herein, we focused on langasite oxides and built structure-composition-property maps that describe the NLO properties in these materials by combining computational property prediction and experimental characterization. Accordingly, rational molecular design was performed, a new member of the langasite family, La3SnGa5O14 (LGSn), was synthesized, and single crystals were grown. The produced material exhibits the widest transparent region (0.27-11.0 mum) among available oxides, the largest LDT (846 MW/cm(2)) among materials that are transparent to 10 mum, and the strongest SHG effect among langasites. The discovery of LGSn facilitates the application of oxides as NLO crystals in the mid-IR spectral region beyond 10 mum. More generally, the developed strategy could be used to guide and accelerate the systematic discovery of functional materials through understanding the key structure-composition-property relationships using the predictive power of computational tools.
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最新影响因子:13.858
资源类型:期刊论文
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