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The Application of Anisotropically Collapsing Gels, Deep Learning, and Optical Microscopy for Chemical Characterization of Nanoparticles and Nanoplastics
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Researchers developed a method to precisely measure the number of carboxyl groups on the surface of individual nanoplastic particles using fluorescence microscopy and gel immobilization. Applied to polystyrene nanoplastics — a model for environmental pollutants — the technique revealed about 38,000 carboxyl groups per particle. Surface chemistry controls how nanoplastics interact with biological tissues and other contaminants, so this kind of precise characterization is essential for understanding their environmental and health behavior.
The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl groups per nanoparticle by combining anisotropically collapsing agarose gels for nanoparticle immobilization with fluorescence microscopy and acid-base titration. We applied this approach to photon-upconversion nanoparticles (UCNPs) coated with poly(acrylic acid) (PAA) and fluorescence-labeled polystyrene nanoparticles (PNs), which serve as models for bioimaging and environmental pollutants, respectively. UCNPs exhibited 152 ± 14 thousand carboxyl groups per particle (∼11 groups/nm2), while PNs were characterized with 38 ± 3.6 thousand groups (∼1.7 groups/nm2). The limit of detection was 6.4 and 1.9 thousand carboxyl groups per nanoparticle, and the limit of quantification was determined at 21 and 6.2 thousand carboxyl groups per nanoparticle for UCNP-PAAs and PNs, respectively. High intrinsic luminescence enabled direct imaging of UCNPs, while PNs required fluorescence staining with Nile Red to overcome low signal-to-noise ratios. The study also discussed the critical influence of nanoparticle concentration and titration conditions on the assay performance. This method advances the precise characterization of surface chemistry, offering insights into nanoparticle structure that extend beyond the resolution of electron microscopy. Our findings establish a robust platform for investigating the interplay of surface chemistry with nanoparticle function and fate in technological and environmental contexts, with broad applicability across nanomaterials.
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The Applicationof Anisotropically Collapsing Gels,Deep Learning, and Optical Microscopy for Chemical Characterizationof Nanoparticles and Nanoplastics
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Researchers developed a method combining anisotropically collapsing agarose gels for nanoparticle immobilization with fluorescence microscopy and deep learning to chemically characterize nanoplastics and other nanoparticles at single-particle resolution using optical microscopy. The approach enables precise quantification of surface functional groups per nanoparticle, demonstrated on polystyrene nanoplastics as environmental contaminant models.
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Researchers combined highly reflective substrates with machine learning to accurately identify individual nanoplastic particles using Raman spectroscopy, a technique that traditionally struggles with particles this small. Their approach achieved over 97 percent accuracy in distinguishing between different types of nanoplastics including polystyrene, polymethyl methacrylate, and polyethylene. The method represents a significant advance in the ability to detect and monitor nanoplastic pollution at the individual particle level.
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