Liquid crystal-driven interfacial ordering of microplastics: Advancing microplastics characterization below the macro-scale

Global research efforts have developed methods for the detection and
characterization of millimeter and larger plastic particles in the
environment, but detecting and characterizing micrometer-scale and smaller
plastic particles (microplastics or MPs) remains an unresolved challenge.
MPs are difficult to characterize because they are small, have chemically
heterogeneous surfaces that are transformed by environmental weathering,
and are often accompanied by colloidal organic matter. Here, we advance
the characterization of mixtures of MPs in the micrometer size range 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 unique 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 colloidal natural organic matter and have
been weathered using UV light. Additionally, we explore the basis by which
the computer vision methods are able to classify MP samples, generating
fresh insights into the physical processes by which colloidal dynamics and
non-equilibrium interfacial phenomena influence the assembly of colloids
at fluid interfaces. Overall, our results advance efforts 30 to develop
characterization methods for colloidal-scale MPs that are broadly
accessible (e.g., to citizen scientists).