For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively. Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient. It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics. Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data. Complementary modeling demonstrates nano- and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.

中文翻译：

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水中的纳米塑料：人工智能辅助的 4D 物理化学表征和快速原位检测


我们首次提出了一种急需的水生系统中纳米塑料原位实时检测技术。我们展示了一种人工智能辅助的纳米数字在线全息显微镜（AI辅助纳米DIHM），它可以在静止和动态天然水中几毫秒内自动将纳米塑料和微塑料与非塑料颗粒同时分类，无需样品制备。 AI 辅助的纳米 DIHM 分别将安大略湖和圣劳伦斯河中 2% 和 1% 的水性颗粒识别为纳米/微塑料。 Nano-DIHM 提供单个颗粒或纳米/微塑料簇的物理化学特性，包括尺寸、形状、光学相位、周长、表面积、粗糙度和边缘梯度。它将纳米/微塑料与有机物、无机物、生物颗粒和涂层异质簇的混合物区分开来。该技术可以对水性纳米/微塑料进行 4D 跟踪以及 3D 结构和空间研究。独立透射电子显微镜、质谱和纳米颗粒跟踪分析验证了纳米 DIHM 数据。互补模型表明纳米塑料和微塑料在水中具有显着不同的分布模式，这会影响它们的运输和命运，使纳米 DIHM 成为精确纳米/微塑料生命周期分析和热点修复的强大工具。