Static and dynamic electro-optical properties of liquid crystals mediated by ferroelectric polymer films

Garbovskiy, Yuriy; Evans, Dean R.; Banerjee, Partha P.; Glushchenko, Anatoliy

doi:10.1039/c7ra12443k

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…Several studies have characterized the topology of optical responses of different nematic LC systems using the FD. Garbovskiy et al explored the electro-optical properties of highresistivity nematic LCs that are sandwiched between ferroelectric polymer films; 26 the authors measured the FD of ferroelectric films to indicate the roughness of the boundaries between adjacent domains. The FD has also been used to study the aggregation of inclusions in bulk LCs or at LC interfaces.…”

Section: Introductionmentioning

confidence: 99%

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

et al. 2023

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Chemoresponsive liquid crystals (LCs) can be engineered to generate information-rich optical responses (in the form of space–time color and brightness patterns) when they are exposed to target gas contaminants. We investigate the use of topological descriptors (Euler characteristic, lacunarity, and fractal dimension) for extracting different types of information from these complex optical responses and show that these tools can enable the design of sensors and help gain insights into the physical phenomena governing sensor responses. We provide a holistic perspective of topological descriptors using the theory of Minkowski functionals and fractal analysis, which allows us to understand specific information that each descriptor extracts. We also show how to use the topological descriptors in conjunction with space–time filtration operations and color representations to enrich the information extracted. We demonstrate how these capabilities can be used in flexible ways to facilitate unsupervised machine learning (ML) tasks (clustering and visualization) and supervised tasks (regression and classification). We demonstrate the developments using real, high-throughput experimental data sets for functionalized LC films that are exposed to different gaseous environments. We show that the topological descriptors encode significant information and can be used to detect outliers in high-throughput data and visualize the temporal evolution of structure. Moreover, we show that the topological descriptors can be used to predict contaminant concentrations using simple ML models such as support vector machines; notably, these ML models can achieve accuracies comparable to those of powerful convolutional neural networks but with a much lower computational cost (from hours to seconds) and using less sophisticated computing hardware (CPUs instead of GPUs). This scalability enables the analysis of space–time data at high resolutions.

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Section: Introductionmentioning

confidence: 99%

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Several studies have characterized the topology of optical responses of different nematic LC systems using the FD. Garbovskiy et al explored the electro-optical properties of high-resistivity nematic LCs that are sandwiched between ferroelectric polymer films 26 ; the authors measured the FD of ferroelectric films to indicate the roughness of the boundaries between adjacent domains. The FD has also been used to study aggregation of inclusions in bulk LCs or at LC interfaces.…”

Section: Introductionmentioning

confidence: 99%

Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Jiang

Bao

Smith

et al. 2023

Preprint

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Chemoresponsive liquid crystals (LCs) can be engineered to generate information-rich optical responses (in the form of space-time color and brightness patterns) when exposed to target gas contaminants. We investigate the use of topological descriptors (Euler characteristic, lacunarity, and fractal dimension) for extracting different types of information from these complex optical responses and show that these tools can enable the design of sensors and help gain insights into physical phenomena governing sensor responses. We provide a holistic perspective of topological descriptors using theory of Minkowski functionals and fractal analysis, which allows us to understand specific information that each descriptor extracts. We also show how to use the topological descriptors in conjunction with space-time filtration operations and color representations to enrich the information extracted. We demonstrate how these capabilities can be used in flexible ways to facilitate unsupervised machine learning (ML) tasks (clustering and visualization) and supervised tasks (regression and classification). We demonstrate the developments using real, high-throughput experimental datasets for functionalized LC films that are exposed to different gaseous environments. We show that the topological descriptors encode significant information and can be used to detect outliers in high-throughput data and visualize the temporal evolution of topology. Moreover, we show that the topological descriptors can be used to predict contaminant concentrations using simple ML models such as support vector machines; notably, these ML models can achieve comparable accuracies to those of powerful convolutional neural networks but with a much lower computational cost (from hours to seconds) and using less sophisticated computing hardware (CPUs instead of GPUs). This scalability enables the analysis of space-time data at high resolutions.

show abstract

“…Several studies have characterized the topology of optical responses of different nematic LC systems using the FD. Garbovskiy et al explored the electro-optical properties of high-resistivity nematic LCs that are sandwiched between ferroelectric polymer films 31 ; the authors measured the FD of ferroelectric films to indicate the roughness of the boundaries between adjacent domains. The FD has also been used to study aggregation of inclusions in bulk LCs or at LC interfaces.…”

mentioning

confidence: 99%

Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Data Analysis

Jiang

Bao

Smith

et al. 2023

Preprint

View full text Add to dashboard Cite

Chemoresponsive liquid crystals (LCs) can be engineered to generate information-rich optical responses upon exposure to gas contaminants. We investigate the use of topological descriptors to extract information from these complex responses and with this facilitate sensor design and understand physical phenomena that govern responses. We provide a holistic view of topological descriptors using Minkowski functionals and fractal analysis and show how descriptors can be used for unsupervised (clustering, visualization) and supervised (regression, classification) machine learning (ML) tasks. Specifically, by using high-throughput, experimental data for LC films exposed to diverse contaminants, we show that topological descriptors can be used to effectively detect outliers and predict contaminant concentrations using simple ML models. Notably, these models achieve comparable accuracies to those of powerful convolutional neural networks, but with much lower computational times (from hours to seconds) and using less sophisticated computing hardware. This scalability enables high-resolution, space-time data analysis.

show abstract

Static and dynamic electro-optical properties of liquid crystals mediated by ferroelectric polymer films

Abstract: Liquid crystals (LC) can reveal the fractal dimension of multi-domain ferroelectric films (FF) while these films can control the switching time of FF–LC hybrids.

Cited by 8 publications

References 52 publications

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Data Analysis

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