  Identifying and removing microplastics (MPs) from the environment is a global
challenge. This study explores how the colloidal fraction of common MPs behave
at aqueous interfaces of liquid crystal (LC) films. We observed polyethylene
(PE) and polystyrene (PS) microparticles to be captured at the LC interface and
exhibit distinct two-dimensional aggregation patterns. The addition of low
concentrations of surfactant (sodium dodecylsulfate (SDS)) was found to further
amplify the differences in PS/PE aggregation patterns, with PS changing from a
linear chain-like morphology to a singly dispersed state with increasing SDS
concentration and PE forming dense clusters at all SDS concentrations.
Statistical characterization of assembly patterns using fractal geometric
theory-based machine learning and a deep learning image recognition model
yielded highly accurate classification of PE vs PS (>99%). Additionally, by
performing feature importance analysis on our deep learning model, dense,
multi-branched assemblies were confirmed to be unique features of PE relative
to PS. To obtain additional insight into the origin of these key features, we
performed microscopic characterization of LC ordering at the microparticle
surfaces. These observations led us to predict that both microparticle types
should generate LC-mediated interactions (due to elastic strain) with a dipolar
symmetry, a prediction consistent with the observed interfacial organization of
PS but not PE. We conclude that the non-equilibrium organization of the PE
microparticles arises from their polycrystalline nature, which leads to rough
particle surfaces and weakened LC elastic interactions and enhanced capillary
forces. Overall, our results highlight the potential utility of LC interfaces
for surface-sensitive characterization of colloidal MPs.
