LCPOM: Precise Reconstruction of Polarized Optical Microscopy Images of Liquid Crystals
LCPOM: Precise Reconstruction of Polarized Optical Microscopy Images of Liquid Crystals
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DOI：
10.1021/acs.chemmater.3c02425
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发表时间：
2024-03-28
影响因子：
8.6
通讯作者：
de Pablo，Juan J.
中科院分区：
文献类型：
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作者：
Chen，Chuqiao;Palacio-Betancur，Viviana;de Pablo，Juan J.
当用偏振光学显微镜（POM）检查时，液晶显示干涉颜色和复杂的图案，这取决于材料的微观方向。这种方向可以通过外场的应用来操纵，这一特性为光学显示和传感技术的应用提供了基础。颜色图案本身具有很高的信息量。然而，传统上，液晶光学外观的计算是通过假设使用单波长光源并在单色尺度上进行的。在这项工作中，原始的琼斯矩阵方法被扩展到计算当液晶暴露于多波长源时产生的彩色图像。通过考虑材料特性，包括局部取向、可见光光谱和CIE（国际照明委员会）颜色匹配函数，我们证明了所提出的方法产生的彩色POM图像与实验数据在定量上一致。结果提出了各种系统，包括径向，双极和胆甾滴，其中模拟结果与实验图像进行了比较。系统地考察了液滴尺寸、拓扑缺陷结构和液滴取向的影响。这里介绍的技术生成的图像可以直接与实验进行比较，从而促进了旨在解释LC显微镜图像的机器学习工作，并为能够响应外部刺激产生特定内部微观结构的材料的逆向设计铺平了道路。
When examined with polarized optical microscopy (POM), liquid crystals display interference colors and complex patterns that depend on the material’s microscopic orientation. That orientation can be manipulated by the application of external fields, a feature that provides the basis for applications in optical display and sensing technologies. The color patterns themselves have high information content. Traditionally, however, calculations of the optical appearance of liquid crystals have been performed by assuming that a single-wavelength light source is employed and reported on a monochromatic scale. In this work, the original Jones matrix method is extended to calculate the colored images that arise when a liquid crystal is exposed to a multiwavelength source. By accounting for the material properties, including the local orientation, the visible light spectrum, and the CIE (International Commission on Illumination) color matching functions, we demonstrate that the proposed approach produces colored POM images that are in quantitative agreement with experimental data. Results are presented for a variety of systems, including radial, bipolar, and cholesteric droplets, where results of simulations are compared with experimental images. The effects of the droplet size, topological defect structure, and droplet orientation are examined systematically. The technique introduced here generates images that can be directly compared to experiments, thereby facilitating machine learning efforts aimed at interpreting LC microscopy images and paving the way for the inverse design of materials capable of producing specific internal microstructures in response to external stimuli.