Surfactants and other amphiphilic molecules are used
extensively
in household products, industrial processes, and biological applications
and are also common environmental contaminants; as such, methods that
can detect, sense, or quantify them are of great practical relevance.
Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit
distinctive optical responses in the presence of surfactants and have
thus emerged as sensitive, rapid, and inexpensive sensors or reporters
of environmental amphiphiles. However, many existing LC-in-water emulsions
require the use of complicated or expensive instrumentation for quantitative
characterization owing to variations in optical responses among individual
LC droplets. In many cases, the responses of LC droplets are also
analyzed by human inspection, which can miss subtle color or topological
changes encoded in LC birefringence patterns. Here, we report an
LC-based surfactant sensing platform that takes a step toward addressing
several of these issues and can reliably predict concentrations and
types of surfactants in aqueous solutions. Our approach uses surface-immobilized,
microcontact-printed arrays of micrometer-scale droplets of thermotropic
LCs and hierarchical convolutional neural networks (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. In addition, we report
computational capabilities to determine relevant optical features
extracted by the CNN from LC micrographs, which can provide insights
into surfactant adsorption phenomena at LC–water interfaces.
Overall, 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.