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Article Tier 2 Detection Methods Marine & Wildlife Nanoplastics Sign in to save

Nanotechnology-enabled analytical tools for Microplastics and nanoplastics in aquatic ecosystems

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Tiny plastic particles in our water, some smaller than a virus, have been hard to detect with older lab tools, making it tough to know how much is really out there or how it might affect us. This review rounds up new nanotech-based detection methods (using gold nanoparticles, light-scattering sensors, and AI) that can spot these nanoplastics at incredibly low levels, faster and with less environmental impact than traditional testing. Better detection tools like these are a key first step toward understanding plastic pollution's health risks and building policies to protect our water supplies.

Microplastics (1 µm–5 mm) and nanoplastics (<1 µm) threaten aquatic ecosystems, necessitating advanced detection beyond conventional methods like FTIR and GC-MS, which lack nanoscale sensitivity and require labour-intensive sample preparation. This review, with >70% focus on particles <1 µm, synthesises indicative concentration ranges derived from 2024–2025 field studies from the Pacific, Arctic, Great Lakes, Mediterranean, Asian rivers, and African lakes, while acknowledging methodological heterogeneity across diverse matrices. It evaluates nanotechnology-enabled tools including surface-enhanced Raman spectroscopy (SERS) and electrochemical nanosensors-for detecting nanoplastics at indicative trace levels (e.g. 5–15 ng/L in recent seawater and wastewater studies). Green nanomaterial synthesis (e.g. plant-derived AuNPs, 15–30 nm) reduces environmental impact by approximately 80% while enabling high-performance substrates. Innovations like AuNP-functionalised graphene SERS substrates, microfluidic sample preconcentration, and Metal-Organic Frameworks (MOF)-based sensors address matrix interferences, sample loss during digestion, and chemical diversity of polymers like polystyrene and PET. These tools enable real-time monitoring across marine, freshwater, and wastewater systems, surpassing traditional approaches in sensitivity and portability. This work introduces the AI-Green Nanotechnology Hybrid (AGNH) Framework a modular system integrating green nanomaterials, edge-AI spectral deconvolution, and hybrid SERS-electrochemical sensors for autonomous, sustainable detection. Unlike prior reviews, it integrates sample preparation optimisation, AI-driven analysis, and green synthesis, supporting ecological risk assessments and policies aligned with UN SDG 14 and the EU Water Framework Directive. Challenges include digestion-induced nanoplastic loss, electrode fouling, and standardisation. Emerging technologies like quantum dot sensors and AGNH-enabled global networks promise innovation, while interdisciplinary collaboration is essential for effective plastic pollution management.

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