A non-centrosymmetric fluoride borate, Ba3Sr4(BO3)3F5, has been synthesized by solid state reaction techniques, and a single crystal was grown by the top-seeded solution growth method using NaF as flux. Single-crystal X-ray diffraction analysis shows the structure to be hexagonal with space group P63 mc (No. 186) and the following cell parameters: a = b = 10.8953(16) Å, c = 6.9381(15) Å, and Z = 2. It exhibits an intricate 3-dimensional framework of interconnecting Ba(1)O(1)3O(2)4F(2) polyhedra, Sr(1)O(2)4F(1)2F(3) polyhedra, Sr(2)O(2)6F(1)3F(3) polyhedra, and isolated BO3 triangles. The infrared spectrum indicates that the Ba3Sr4(BO3)3F5 crystal contains characteristic BO3 3− groups as its basic structural units, which is consistent with the crystallographic study. The transparency spectrum shows that the as-grown crystal is transparent in the wavelength range of 270 to 2550 nm, the highest transmittance exceeds 85%, and the UV cutoff wavelength is about 210 nm.The thermal analysis shows that it is chemically stable and melts congruently at 1174.3 °C.

We report herein a new approach for the synthesis of organothiophosphates from phosphonates and thiols through electrochemical reaction. The reactions were conducted without the addition of oxidant, transition-metal base, or base at room temperature. This system has a good substrate scope and functional group tolerance. Aryl and alkyl thiols worked well with phosphonates to afford the corresponding organothiophosphates in good yields.

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The specific characteristics of remote sensing images, such as large directional variations, large target sizes, and dense target distributions, make target detection a challenging task. To improve the detection performance of models while ensuring real-time detection, this paper proposes a lightweight object detection algorithm based on an attention mechanism and YOLOv5s. Firstly, a depthwise-decoupled head (DD-head) module and spatial pyramid pooling cross-stage partial GSConv (SPPCSPG) module were constructed to replace the coupled head and the spatial pyramid pooling-fast (SPPF) module of YOLOv5s. A shuffle attention (SA) mechanism was introduced in the head structure to enhance spatial attention and reconstruct channel attention. A content-aware reassembly of features (CARAFE) module was introduced in the up-sampling operation to reassemble feature points with similar semantic information. In the neck structure, a GSConv module was introduced to maintain detection accuracy while reducing the number of parameters. Experimental results on remote sensing datasets, RSOD and DIOR, showed an improvement of 1.4% and 1.2% in mean average precision accuracy compared with the original YOLOv5s algorithm. Moreover, the algorithm was also tested on conventional object detection datasets, PASCAL VOC and MS COCO, which showed an improvement of 1.4% and 3.1% in mean average precision accuracy. Therefore, the experiments showed that the constructed algorithm not only outperformed the original network on remote sensing images but also performed better than the original network on conventional object detection images.