(474e) Decoding Optical Responses of Contact-Printed Arrays of Liquid Crystals Using Machine Learning: Detection of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity

Surfactants are amphiphilic molecules that contain hydrophilic head groups and hydrophobic tail groups [1]. Given this unique structure, surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications, and are also common environmental contaminants [2-5]; as such, methods that can detect, sense, or quantify them are of great practical relevance.
State-of-the-art methods for detecting natural and synthetic amphiphiles include mass spectrometry and high-performance liquid chromatography [4, 6]. While these methods provide excellent sensitivity and selectivity, they often require complex sample preparation procedures, costly laboratory infrastructure, and highly trained personnel.

To address these limitations, new approaches have been developed in recent studies, including many optical, electrochemical, and mechanical-based techniques [4, 7]. Among these new approaches, aqueous emulsions of thermotropic liquid crystals (LCs) [8-13] have gained growing attention because they can exhibit distinctive optical responses in the presence of surfactants. When coupled with machine learning techniques such as convolutional neural networks (CNNs), LC-in-water emulsions have emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles [14-18]. However, many existing CNN-based LC sensing methods require the use of expensive instrumentation (e.g., flow cytometry [14]) or computational resources (e.g., videos [18]) for quantitative characterization, owing to variations in optical responses among individual LC droplets with different sizes in an LC-in-water emulsion.

Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of the abovementioned issues and can predict concentrations and types of model surfactants in aqueous solutions. Our approach uses microcontact-printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical CNNs to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. The microcontact-printed LC droplets are surface-immobilized and uniformly sized, reducing variations in optical responses. The proposed hierarchical CNN contains two levels of 2D CNNs that are trained on different data representations. The selection of the data representation for each level relies on the detection focus. The first level is intended to differentiate defect patterns, and therefore grayscale images, which highlight the contrast between light and shade and contain sufficient information for defect pattern classification. The second level, on the other hand, is forced to learn patterns that are less distinguishable and thus require color information (e.g., RGB). Overall, we show that the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles.

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