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Liquid crystal–driven interfacial ordering of colloidal microplastics: Advancing microplastic characterization below the macroscale
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Researchers developed a liquid crystal-based platform to characterize micrometer-scale and smaller microplastic particles by exploiting their spontaneous adsorption and self-organization at liquid crystal-aqueous interfaces. The approach enables detection and differentiation of microplastics below the macroscale, even for particles with chemically complex or weathered surfaces and in the presence of natural organic matter.
Detecting micrometer-scale and smaller plastic particles (microplastics or MPs) in the environment remains an unresolved challenge because they are small, have chemically heterogeneous surfaces as a result of environmental weathering, and are often accompanied by natural organic matter. Here, we advance the characterization of mixtures of MPs by leveraging their spontaneous adsorption and self-organization at liquid crystal (LC)-aqueous interfaces. We show that surface-sensitive interparticle interactions mediated by the LC can drive mixtures of colloidal MPs into assembly patterns that are accurately recognized using computer vision approaches. In particular, we show that we can identify MP composition (polystyrene and polymethyl methacrylate) in complex samples that contain natural organic matter and have been weathered using ultraviolet light. In addition, we explore how computer vision methods classify MP samples, generating fresh insights into the physical processes that control colloidal dynamics and assembly at fluid interfaces. Overall, our results advance efforts to develop characterization methods for colloidal-scale MPs that are broadly accessible (e.g., to citizen scientists).
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Liquid Crystals as Multifunctional Interfaces for Trapping and Characterizing Microplastics
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This study explored the use of liquid crystal (LC) films as multifunctional interfaces for trapping and classifying common microplastics including polyethylene (PE) and polystyrene (PS). PE and PS microparticles exhibited distinct two-dimensional aggregation patterns at the LC interface, with differences amplified by adding low concentrations of surfactant. Machine learning models applied to fractal geometry analysis of the aggregation patterns classified PE versus PS with over 99% accuracy, suggesting a novel approach for microplastic identification.
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Local order parameter that distinguishes crystalline and amorphous portions in polymer crystal lamellae
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