Abstract
ABSTRACT Submicroplastics (SMPs), pollutants originating from the degradation of plastic materials and industrial processes, pose major analytical challenges because: (i) they vary widely in chemical composition and size and are too small to be identified by conventional optical techniques; (ii) they are ubiquitous in the environment; (iii) they are highly heterogeneous, exhibiting unique size, shape and morphology as a consequence of diverse degradation pathways; and, most importantly, (iv) they undergo continuous transformations in exposure media, as their large specific surface area and strong binding affinity promote the spontaneous formation of eco-coronas and bio-coronas. Corona formation is unavoidable and strongly depends on both exposure conditions and matrix composition. However, the influence of these coronas on the biological effects of SMPs remains largely unexplored. Further efforts are therefore required to develop both generic and matrix-specific analytical strategies for the characterization of SMPs and their associated coronas in environmental and food samples. A wide range of analytical techniques—including imaging, spectroscopy and separation methods—have been proposed for SMP analysis, all aimed at particle extraction and characterization. Nevertheless, these particle-oriented approaches generate multiple heterogeneous datasets that must be integrated to maximize the information extracted while reducing data complexity. At the same time, multiblock chemometric approaches and machine learning (ML) methods remain largely underexploited in this field. DOMANI addresses these challenges through two complementary objectives. The first is to improve the understanding of SMP eco-coronas by profiling, fractionating, and qualitatively and quantitatively characterizing submicroplastics in representative exposure media. The second, more ambitious objective is to develop modelling approaches capable of dynamically profiling and discriminating between clean and contaminated water and food matrices in a fast-result/fast-action framework. This will enable the development of screening tools based on rapid and simplified analyses, to be used before resorting to more time-consuming, costly and environmentally demanding analytical strategies. By combining expertise in analytical chemistry, surface and colloid science, metrology, chemometrics, multiblock data analysis and machine learning, DOMANI aims to establish innovative matrix-oriented strategies for the comprehensive characterization of submicroplastics and their eco-coronas.
Results achieved
The DOMANI project successfully achieved its objectives by developing an interdisciplinary framework for the characterization of submicroplastics (SMPs) and their eco-coronas in environmental and food matrices. Representative reference materials (polystyrene, PET, PE and PP) were selected and thoroughly characterized, while harmonized protocols for both dynamic and static particle characterization were established to ensure measurement reproducibility and comparability across the participating research units. The project developed and optimized complementary analytical methodologies based on Field-Flow Fractionation (FFF), pyrolysis-GC-MS, Dynamic Light Scattering (DLS), Electrophoretic Light Scattering (ELS), FESEM, Centrifugal Liquid Sedimentation (CLS), and Raman spectroscopy. These approaches demonstrated the key role of environmental conditions and eco-corona formation in controlling nanoparticle stability, aggregation, and analytical recovery, providing essential knowledge for the development of robust protocols for nanoplastic characterization. Experimental activities enabled the profiling of submicroplastics in both environmental matrices (freshwater and seawater) and food matrices, with particular emphasis on milk and pollen. The project demonstrated the feasibility of detecting nanoplastics in complex matrices with minimal sample preparation by integrating dynamic and static analytical techniques and by developing innovative online FFF-Raman methodologies. In parallel, shared databases were established and chemometric and machine-learning approaches were implemented to integrate heterogeneous analytical datasets, enabling the classification of contaminated matrices through tiered screening strategies. DOMANI also generated significant scientific outputs and dissemination activities. Two major peer-reviewed publications were produced, reporting the multimodal characterization of eco-corona formation on airborne nanoplastics and the first demonstration of online FFF-Raman coupling for the direct analysis of nanoplastics in food matrices. Project outcomes were further disseminated through numerous presentations at national and international conferences and workshops, contributing to the advancement of analytical methodologies for the study of submicroplastics in environmental and food systems.Project details
Unibo Team Leader: Valentina Marassi
Unibo involved Department/s:
Dipartimento di Chimica "Giacomo Ciamician"
Coordinator:
ALMA MATER STUDIORUM - Università di Bologna(Italy)
Total Eu Contribution: Euro (EUR) 239.690,00
Total Unibo Contribution: Euro (EUR) 119.630,00
Project Duration in months: 24
Start Date:
30/11/2023
End Date:
28/02/2026
