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- W4382334287 abstract "Preconcentration of the target compound is a critical step that ensures the accuracy of the subsequent chemical analysis. In this work, we present a straightforward yet effective liquid-liquid extraction approach based on surface nanodroplets (i.e., nanoextraction) for offline analysis of highly diluted sample solutions. The extraction and sample collection were streamlined in a 3-m microcapillary tube. The concentration of the target analyte in surface nanodroplets was significantly increased compared to the concentration in the sample solution, reaching several orders of magnitudes. A limit of detection (LOD) was decreased by a factor of $sim 10^3$ for an organic model compound in Fourier-transform infrared spectroscopy (FTIR) measurements and $sim 10^5$ for a model fluorescent dye in fluorescence detection. The quantitative analysis of the organic compound was also achieved in a wide concentration region from $10^{-3}$ M to $10^{-4}$ M. The total volume of surface nanodroplets can be manipulated to further enhance extraction efficiency, according to the principle that governs droplet formation by solvent exchange. Additionally, our method exhibited significantly improved sensitivity compared to traditional dispersive liquid-liquid microextraction (DLLME). The LOD of the fluorescent dye and the organic model compound obtained with DLLME was 3 orders of magnitude and 20 times higher than the LOD achieved through nanoextraction approach. The nanoextraction developed in this work can be applied to preconcentrate multi-compounds from river water samples, without clear interference from each other. This can further extend its applicability for the detection and quantification of target analytes in complex aqueous samples by common analytical instruments." @default.
- W4382334287 created "2023-06-28" @default.
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- W4382334287 date "2023-06-26" @default.
- W4382334287 modified "2023-09-25" @default.
- W4382334287 title "Nanoextraction from a flow of a highly diluted solution for much-improved sensitivity in offline chemical detection and quantification" @default.
- W4382334287 doi "https://doi.org/10.48550/arxiv.2306.14860" @default.
- W4382334287 hasPublicationYear "2023" @default.
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