# Radio Frequency Detection and Classification of Microplastics in Water

arXiv: https://arxiv.org/abs/2609.20507 · HTML: https://mlsanity.com/paper/2609.20507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Abstract (arXiv)

Micro- and nano-plastic particles (MPs/NPs) are ubiquitous environmental contaminants whose increasing abundance and potential health impacts have created an urgent need for rapid, label-free detection methods. As particle size decreases to the low-micrometer range, conventional optical and spectroscopic techniques become increasingly challenging because of limited throughput and/or complex sample preparation. In this work, we present a machine learning (ML)-assisted radio-frequency (RF) dielectric spectroscopic cytometry (DiSC) platform for the label-free detection and classification of MPs. Eight types of  10  μm nominal-diameter MP particles suspended in deionized (DI) water were characterized at four frequencies spanning  0.2-9 GHz . The measured alterations in RF scattering parameters (S-parameters), referenced to the carrier medium, were used to train supervised ML models for material classification, including the identification of MPs in mixed samples and saline-water environments. For eight MP classes suspended in DI water, the proposed method achieved macro-average F1-score, precision, and recall values exceeding  0.71 . Furthermore, PET classification performance was largely maintained in saline carrier media containing 3.3%  and  6.6%  sea salt. These results demonstrate the feasibility of ML-assisted RF DiSC for rapid, single-particle MP classification in aqueous environments. Future work will focus on improving classification performance through enhanced RF calibration, increased spectral coverage, larger training datasets, and validation using environmentally aged and biologically contaminated microplastics.