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An Information-Theoretic Framework for Quantifying Scattering-State Complexity in Microplastic Light Scattering
Summary
Scientists have developed a new mathematical technique to help identify and distinguish different types of microplastics by analyzing how they scatter light, similar to how you might identify materials by studying their shadows. This matters because better detection tools are a crucial first step toward understanding how much microplastic pollution surrounds us and, eventually, how it might affect our health, though this study focuses on improving detection methods rather than directly measuring health impacts.
Quantitative characterization of microplastic morphology using optical techniques remains challenging due to the complex scattering behavior of irregular particulate systems. In this work, an information-theoretic framework based on Shannon entropy is introduced for analyzing static light scattering (SLS) patterns of polystyrene and polyethylene microplastics. Angular scattering data acquired using a laboratory-built SLS system over an angular range of 10°–170° were transformed into probability distributions to quantify scattering-state organization and angular redistribution of scattered intensity. Experimental results obtained from 15 independent measurements revealed consistently higher entropy values for polystyrene, indicating enhanced angular redistribution and increased global angular scattering distribution. A normalized entropy index was further proposed to enable comparison across independent datasets. Numerical simulations inspired by Mie-type scattering profiles demonstrated a monotonic increase in entropy with increasing angular redistribution, thereby providing qualitative conceptual support for the proposed information-theoretic interpretation. The results demonstrate that Shannon entropy provides a compact statistical descriptor of experimentally measured angular scattering intensity distributions.