Stability and optoelectronic property of lead-free halide double perovskite Cs2B'BiI6 (B' = Li,Na and K)

Although lead-based perovskite solar cells have achieved more than 25% power conversion efficiency, the toxicity of lead and instability are still urgent problems faced in industrial application. Lead-free halide double perovskite (DP) materials are promising candidates to resolve these issues. Based on the density functional theory, we explore the geometric stability, thermodynamic stability, mechanical stability, electronic structures, and optical properties of the Cs₂B'BiI₆ (B' =m Li, Na and K) DP materials. By analyzing the tolerance factor and octahedral factor, we find the geometric stabilities of Cs₂NaBiI₆ and Cs₂KBiI₆ DPs are better than Cs₂LiBiI₆. By calculating the total energy, formation energy and decomposition energy, we propose that the most favorable structure of Cs₂B'BiI₆ is the orthorhombic phase, and Cs₂LiBiI₆ is less stable relative to the other two counterparts from an energetic viewpoint. Mechanical stability evaluations reveal that the orthorhombic Cs₂LiBiI₆ material is less stable relative to the isostructural Cs₂NaBiI₆ and Cs₂KBiI₆ DPs. The mechanical property calculations indicate that the Cs₂B'BiI₆ DPs possess good ductility, which can be used as flexible materials. Electronic structures and optical property calculations show that the orthorhombic Cs₂B'BiI₆ DPs have suitable band gap values, weaker exciton binding energies, and excellent optical absorption performance in the visible-light range. Based on the above comprehensive assessments, we can conclude that the orthorhombic Cs₂NaBiI₆ and Cs₂KBiI₆ DPs with good stability are promising candidates for solar cell applications.     

极小景深条件下显微镜大范围聚焦算法

自动聚焦是全自动显微成像中的一项关键技术。为了克服已有聚焦算法对聚焦起始点敏感及聚焦范围过小的问题,从分析大范围聚焦曲线形态入手,依据离焦距离、初始搜索方向和初始搜索范围等参量和曲线形态参数间的制约关系,将聚焦划分为六种类型,极值搜索中结合聚焦函数值的变化信息并通过启发式方式将未知类型转化为基本型,从而实现了大范围聚焦到小范围聚焦的转换。然后在聚焦曲线陡峭区范围内对曲线进行高斯拟合以获取最优焦平面。基于自行研发的全自动显微镜系统对提出的算法进行了验证,实验结果证明了该算法的有效性。     

基于色差向量场的彩色光学显微细胞图像分割

细胞图像分割是医学图像处理领域的研究热点之一。传统的细胞图像分割算法多是基于灰度图像的分割,图像中的颜色信息利用得不充分。在深入分析细胞图像颜色特征的基础上,提出了基于色差向量场分析细胞图像颜色变化规律的方法,相比于经典的彩色空间(HSV、YIQ、CIEL*a*b*),这种方法更能够突出图像中的主体细胞与非细胞区域的差异,而且针对大量图像的普适性更好。然后基于细胞图像的色差向量场,提出了一种循环匹配的分割方法,同时采用色差强度对分割结果进行了进一步的修正。通过对实际采集的彩色细胞图像样本的分割实验验证,该算法比RGVF Snake算法的分割结果更可靠,准确率可以达到95.2%,而且能够实现不同颜色重叠细胞图像的分割。