Alignment assessment of anisotropic liquid crystals through an automated image processing algorithm

Liquid crystals (LCs) are recognised as an emerging type of responsive and functional materials that exhibit diverse technological applications. The spatial arrangement or alignment of LC molecules creates an optical anisotropy, which influences the propagation of light. This study presents an automated image processing algorithm designed to quantitatively assess LC alignment. The algorithm calculates the image structure tensor and computes a score ranging from 0 to 1 to represent the uniformity of LC alignment. The tool was verified on both artificially generated images of lines with pre-defined orientations and then tested on microscope images of LCs. For the latter, test samples of deformed helix ferroelectric LCs were constructed using three different alignment methods. Analysis of microscopic images of samples prepared utilising photoalignment, polymer rubbing, and oblique evaporation alignment techniques, determined average alignment scores (mean +/- standard error of the mean) of 0.44 +/- 0.03, 0.26 +/- 0.1, and 0.17 +/- 0.04, respectively, in comparison to commercially sourced control samples with a score of 0.64 +/- 0.03 (n = 5 each). This study paves the way for automated objective assessment of LC alignment, a critical aspect to ensure the optimal performance, reliability, and efficiency of various devices and sensors relying on LC technology.