Abstract | The hydrophobicity of nanoparticles (NPs) is a key property determining environmental fate, biological partitioning and toxicity. However, methods to characterize surface hydrophobicity are not uniformly applied to NPs and cannot quantify surface changes in complex environments. Existing methods designed to evaluate the hydrophobicity of bulk solids, chemicals, and proteins have significant limitations when applied to NPs. In this study, we modified and evaluated two methods to determine the hydrophobicity of NPs, hydrophobic interaction chromatography (HIC) and dye adsorption, and compared them to the standard octanol-water partitioning protocol for chemicals. Gold, copper oxide, silica, and amine-functionalized silica NPs were used to evaluate methods based on their applicability to NPs that agglomerate and have surface coatings. The octanol water partitioning and HIC methods both measured Au NPs as hydrophilic, but despite having a small size and stable suspension, NPs could not be fully recovered from the HIC column. For the dye adsorption method, hydrophobic (Rose Bengal) and hydrophilic (Nile Blue) dyes were adsorbed to the NP surface, and linear isotherm parameters were used as a metric for hydrophobicity. CuO was determined to be slightly hydrophilic, while SiO2 was hydrophilic and Ami-SiO2 was hydrophobic. The advantages and limitations of each method are discussed, and the dye adsorption method is recommended as the most suitable for application across broad classes of nanomaterials. The dye assay method was further used to measure changes in the surface hydrophobicity of TiO2 NPs after being suspended in natural water collected from the Alsea Rivers watershed in Oregon. TiO2 NPs adsorbed Rose Bengal when suspended in ultrapure water, but adsorbed Nile Blue after being incubated in natural water samples, demonstrating a shift from hydrophobic to hydrophilic properties on the outer surface. The dye adsorption method can be applied to characterize surface hydrophobicity of NPs and quantify environmental transformations, potentially improving environmental fate models.
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