Microwave Cytometry with Machine Learning for Shape-Resolved Microplastic Detection

TL;DR

This paper presents a portable microwave cytometry system combined with machine learning to accurately detect and characterize the shape of microplastics, overcoming previous limitations of shape assumptions in environmental sensing.

Contribution

The study introduces a shape-aware microwave cytometry method using machine learning, enabling electronic-only, high-throughput microplastic detection without optical input.

Findings

Achieved <8% average error in shape measurement of microparticles.

Successfully derived dielectric permittivity of ellipsoid particles.

Removed the need for shape assumptions in microplastic sensing.

Abstract

Microplastics are increasingly recognized as a global environmental health threat, yet their detection and characterization remain constrained by the cost, form factor, and throughput of existing analytical tools. Portable micro/nanotechnology-based sensors are emerging to address this need, but most rely on the assumption of spherical particle geometry in their operating principle, limiting their relevance for environmental analysis. Here, we overcome this limitation by advancing microwave cytometry with machine learning-enabled shape recognition. Microwave cytometry is a flow-through electronic platform that integrates microwave resonator responses with low-frequency impedance signals to capture the dielectric signatures of individual particles. Using microscopy-derived shape measurements as ground truth, we trained a random forest model to decode these information-rich waveforms. Once trained, the system operates without optical input, enabling electronic-only determination of particle geometry. We demonstrate extraction of the major and minor axes of ellipsoidal microparticles with <8% relative error on average and use these predictions to derive the dielectric permittivity of ellipsoid particles. This approach removes long-standing shape assumptions in microplastic sensing and establishes a pathway toward portable, high-throughput, morphology-aware detection technologies.