4 research outputs found

    Highly Sensitive and Automated Surface Enhanced Raman Scattering-based Immunoassay for H5N1 Detection with Digital Microfluidics

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    Digital microfluidics (DMF) is a powerful platform for a broad range of applications, especially immunoassays having multiple steps, due to the advantages of low reagent consumption and high automatization. Surface enhanced Raman scattering (SERS) has been proven as an attractive method for highly sensitive and multiplex detection, because of its remarkable signal amplification and excellent spatial resolution. Here we propose a SERS-based immunoassay with DMF for rapid, automated, and sensitive detection of disease biomarkers. SERS tags labeled with Raman reporter 4-mercaptobenzoic acid (4-MBA) were synthesized with a core@shell nanostructure and showed strong signals, good uniformity, and high stability. A sandwich immunoassay was designed, in which magnetic beads coated with antibodies were used as solid support to capture antigens from samples to form a beads–antibody–antigen immunocomplex. By labeling the immunocomplex with a detection antibody-functionalized SERS tag, antigen can be sensitively detected through the strong SERS signal. The automation capability of DMF can greatly simplify the assay procedure while reducing the risk of exposure to hazardous samples. Quantitative detection of avian influenza virus H5N1 in buffer and human serum was implemented to demonstrate the utility of the DMF-SERS method. The DMF-SERS method shows excellent sensitivity (LOD of 74 pg/mL) and selectivity for H5N1 detection with less assay time (<1 h) and lower reagent consumption (∼30 μL) compared to the standard ELISA method. Therefore, this DMF-SERS method holds great potentials for automated and sensitive detection of a variety of infectious diseases

    Label-Free Surface-Enhanced Raman Spectroscopy Detection of DNA with Single-Base Sensitivity

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    Direct, label-free detection of unmodified DNA is a great challenge for DNA analyses. Surface-enhanced Raman spectroscopy (SERS) is a promising tool for DNA analyses by providing intrinsic chemical information with a high sensitivity. To address the irreproducibility in SERS analysis that hampers reliable DNA detection, we used iodide-modified Ag nanoparticles to obtain highly reproducible SERS signals of single- and double-strand DNA in aqueous solutions close to physiological conditions. The phosphate backbone signal was used as an internal standard to calibrate the absolute signal of each base for a more reliable determination of the DNA structure, which has not been achieved before. Clear identification of DNA with single-base sensitivity and the observation of a hybridization event have been demonstrated

    Extraction of Absorption and Scattering Contribution of Metallic Nanoparticles Toward Rational Synthesis and Application

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    Noble metal nanoparticles have unique localized surface plasmon resonance (LSPR), leading to their strong absorption and scattering in the visible light range. Up to date, the common practice in the selection of nanoparticles for a specific application is still based on the measured extinction spectra. This practice may be erroneous, because the extinction spectra contain both absorption and scattering contribution that may play different roles in different applications. It would be highly desirable to develop an efficient way to obtain the absorption and scattering spectra simultaneously. Herein, we develop a method to use the experimentally measured extinction and scattering signals to extract the absorption and scattering spectra that is in excellent agreement with that simulated by discrete dipole approximation (DDA). The heating curve measurement on the three types of gold nanorods, with almost the same extinction spectra but different absorption and scattering contribution, convincingly reveals an excellent correlation between the heating effect and the absorption strength rather than the extinction strength. The result demonstrates the importance to obtain the scattering and absorption spectra to predict the potential application for different types of nanoparticles, which in turn will screen efficiently nanoparticles for a specific application
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