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Abstract Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of MPs assemble into distinct 2D patterns at aqueous interfaces of liquid crystal (LC) films with the goal of developing surface‐sensitive methods for identifying MPs. Polyethylene (PE) and polystyrene (PS) microparticles are measured to exhibit distinct aggregation patterns, with addition of anionic surfactant amplifying differences in PS/PE aggregation patterns: PS changes from a linear chain‐like morphology to a singly dispersed state with increasing surfactant concentration whereas PE forms dense clusters at all surfactant concentrations. Statistical analysis of assembly patterns using deep learning image recognition models yields accurate classification, with feature importance analysis confirming that dense, multibranched assemblies are unique features of PE relative to PS. Microscopic characterization of LC ordering at the microparticle surfaces leads to predict LC‐mediated interactions (due to elastic strain) with a dipolar symmetry, a prediction consistent with the interfacial organization of PS but not PE. Further analysis leads to conclude that PE microparticles, due to their polycrystalline nature, possess rough surfaces that lead to weak LC elastic interactions and enhanced capillary forces. Overall, the results highlight the potential utility of LC interfaces for rapid identification of colloidal MPs based on their surface properties. 

Abstract Environmental and health risks posed by microplastics (MPs) have spurred numerous studies to better understand MPs' properties and behavior. Yet, we still lack a comprehensive understanding due to MP's heterogeneity in properties and complexity of plastic property evolution during aging processes. There is an urgent need to thoroughly understand the properties and behavior of MPs as there is increasing evidence of MPs' adverse health and environmental effects. In this perspective, we propose an integrated chemical engineering approach to improve our understanding of MPs. The approach merges artificial intelligence, theoretical methods, and experimental techniques to integrate existing data into models of MPs, investigate unknown features of MPs, and identify future areas of research. The breadth of chemical engineering, which spans biological, computational, and materials sciences, makes it well‐suited to comprehensively characterize MPs. Ultimately, this perspective charts a path for cross‐disciplinary collaborative research in chemical engineering to address the issue of MP pollution.