He Wan-Li, Cui Yong-Feng, Luo Shi-Guang, Hu Wen-Tuo, Wang Kai-Nan, Yang Zhou, Cao Hui, Wang Dong
Department of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Molecules. 2022 Oct 16;27(20):6938. doi: 10.3390/molecules27206938.
Blue-phase liquid crystal (BPLC) is considered as the next-generation liquid crystal display material, but its practical application is seriously affected by a narrow temperature range and a long research period. In this paper, we used inkjet printing technology to prepare BPLC materials with high throughput, and try to use machine vision technology to test BPLC with high throughput. The "standard curve method" for establishing each printing channel and the "vector matching method" for searching the chromaticity value of the minimum distance were proposed to improve the accuracy of inkjet printing BPLC materials. For a large number of sample-phase images, we propose a machine learning method to identify the liquid crystal phase. In this paper, for the first time, the high-throughput preparation and high-throughput detection of 1080 BPLC samples with five common components by a comprehensive experimental method has been successfully realized. The results are helpful to improve the research efficiency of blue-phase materials and provide a theoretical basis and practical guidance for rapid screening of multi-component BPLC materials.
蓝相液晶(BPLC)被认为是下一代液晶显示材料,但其实际应用受到狭窄温度范围和漫长研究周期的严重影响。本文采用喷墨打印技术高通量制备BPLC材料,并尝试利用机器视觉技术高通量检测BPLC。提出了用于建立各打印通道的“标准曲线法”和用于搜索最小距离色度值的“向量匹配法”,以提高喷墨打印BPLC材料的精度。针对大量样品相图像,提出一种机器学习方法来识别液晶相。本文首次通过综合实验方法成功实现了对含五种常见成分的1080个BPLC样品的高通量制备和高通量检测。研究结果有助于提高蓝相材料的研究效率,为多组分BPLC材料的快速筛选提供理论依据和实际指导。