DOI: 10.5061/dryad.qfttdz0vv
title: Liquid crystal-driven interfacial ordering of microplastics: Advancing microplastics characterization below the macro-scale

description: Global research efforts have developed methods for the detection and
 characterization of millimeter and larger plastic particles in the
 environment, but detecting and characterizing micrometer-scale and smaller
 plastic particles (microplastics or MPs) remains an unresolved challenge.
 MPs are difficult to characterize because they are small, have chemically
 heterogeneous surfaces that are transformed by environmental weathering,
 and are often accompanied by colloidal organic matter. Here, we advance
 the characterization of mixtures of MPs in the micrometer size range by
 leveraging their spontaneous adsorption and self-organization at liquid
 crystal (LC)-aqueous interfaces. We show that surface-sensitive
 interparticle interactions mediated by the LC can drive mixtures of
 colloidal MPs into unique assembly patterns that are accurately recognized
 using computer vision approaches. In particular, we show that we can
 identify MP composition (polystyrene and polymethyl methacrylate) in
 complex samples that contain colloidal natural organic matter and have
 been weathered using UV light. Additionally, we explore the basis by which
 the computer vision methods are able to classify MP samples, generating
 fresh insights into the physical processes by which colloidal dynamics and
 non-equilibrium interfacial phenomena influence the assembly of colloids
 at fluid interfaces. Overall, our results advance efforts 30 to develop
 characterization methods for colloidal-scale MPs that are broadly
 accessible (e.g., to citizen scientists).
description: # Liquid crystal-driven interfacial ordering of microplastics: Advancing
 microplastics characterization below the macro-scale Dryad
 DOI: [https://doi.org/10.5061/dryad.qfttdz0vv](https://doi.org/10.5061/dryad.qfttdz0vv) This repository contains code examples and four image/video datasets for microplastic assembly activity analysis. * deepPolyNet_(1).ipynb * GradCAM___High_Throughput_(1).ipynb * Morphology_ML_Analysis_(1).ipynb * Multi-label_classificaiton_(1).ipynb * PS-PMMA_dataset.zip PS-PMMA Project includes essential image datasets and code for analyzing PS-PMMA microplastic assembly patterns. Due to file size constraints, the complete image dataset is available via a separate Google Drive link. We have shown the example images in the repo. The dataset comprises 8,400 images distributed across 42 distinct classes, with 200 images per class. Each class is defined by a unique combination of four experimental conditions: 1. Concentration: 0 (control, no microplastic), 20, 200, 400, and 800 mg/L 2. Composition: PS, PS75%/PMMA25%, PS50%/PMMA50%, PS25%/PMMA75%, PMMA, and No MP 3. UV Treatment Time: 0, 3, 5, and 10 days 4. NOM Presence: Yes (NOM added) or No (no NOM) The repository provides training code for both single-label and multi-label classification using the deepPolyNet model. Additionally, it includes: a. Grad-CAM++ feature importance analysis for model interpretation b. Morphology-property feature analysis scripts, as illustrated and discussed in Figure 2 of the accompanying documentation or manuscript. Citation: Please use the following citation: Mukherjee, Fiona; Shi, Anye; You, Fengqi; Abbott, Nicholas (2025). Liquid Crystal-Driven Interfacial Ordering of Microplastics: Advancing Microplastics Characterization Below the Macro-scale [Dataset]. Dryad. [https://doi.org/10.5061/dryad.qfttdz0vv](https://doi.org/10.5061/dryad.qfttdz0vv)