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 autom...
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| Vydáno v: | Journal of molecular liquids Ročník 408; s. 125243 |
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| Jazyk: | angličtina |
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Elsevier B.V
15.08.2024
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| ISSN: | 0167-7322 |
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| Abstract | 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.
•The structural arrangement or alignment of liquid crystal molecules underpins their functionality in a variety of devices.•This study presents an automated image processing algorithm designed to quantitatively assess liquid crystal alignment.•The algorithm was verified on artificially generated images of pre-defined lines and then tested on microscope images of LCs.•The algorithm was able to identify differences in liquid crystal samples prepared using a variety of alignment methods. |
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| AbstractList | 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.
•The structural arrangement or alignment of liquid crystal molecules underpins their functionality in a variety of devices.•This study presents an automated image processing algorithm designed to quantitatively assess liquid crystal alignment.•The algorithm was verified on artificially generated images of pre-defined lines and then tested on microscope images of LCs.•The algorithm was able to identify differences in liquid crystal samples prepared using a variety of alignment methods. |
| ArticleNumber | 125243 |
| Author | Al Abed, Amr Lim, Senny S. Almasri, Reem M. Ladouceur, François Lovell, Nigel H. |
| Author_xml | – sequence: 1 givenname: Reem M. orcidid: 0000-0003-3530-8323 surname: Almasri fullname: Almasri, Reem M. organization: Graduate School of Biomedical Engineering, UNSW, Sydney, Australia – sequence: 2 givenname: Senny S. surname: Lim fullname: Lim, Senny S. organization: Graduate School of Biomedical Engineering, UNSW, Sydney, Australia – sequence: 3 givenname: Nigel H. orcidid: 0000-0003-1637-1079 surname: Lovell fullname: Lovell, Nigel H. organization: Graduate School of Biomedical Engineering, UNSW, Sydney, Australia – sequence: 4 givenname: François orcidid: 0000-0003-4638-273X surname: Ladouceur fullname: Ladouceur, François organization: School of Electrical Engineering and Telecommunications, UNSW, Sydney, Australia – sequence: 5 givenname: Amr orcidid: 0000-0002-5560-4771 surname: Al Abed fullname: Al Abed, Amr email: amra@unsw.edu.au organization: Graduate School of Biomedical Engineering, UNSW, Sydney, Australia |
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| Cites_doi | 10.3390/molecules27041453 10.1021/ma9711708 10.1063/1.92056 10.1063/1.327978 10.1080/02678298908026417 10.1038/nprot.2014.024 10.1080/02678298908033202 10.1088/0957-4484/27/26/26LT01 10.1080/02678292.2011.652198 10.1039/D0SM02088E 10.1080/02678298908026421 10.1080/15421400590955488 10.1016/j.optmat.2018.05.003 10.1140/epjs/s11734-021-00390-9 10.1143/JJAP.43.5440 10.1016/j.jphotochemrev.2004.10.004 10.1080/713935610 10.1002/jsid.825 10.1038/nmat2010 10.1088/1741-2552/ac8ed6 10.1364/AO.512480 |
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| Keywords | Oblique evaporation Anisotropy Photoalignment Thermotropic LCs Liquid crystals LC alignment Polymer rubbing |
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| SubjectTerms | Anisotropy LC alignment Liquid crystals Oblique evaporation Photoalignment Polymer rubbing Thermotropic LCs |
| Title | Alignment assessment of anisotropic liquid crystals through an automated image processing algorithm |
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