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Updated: Oct 14, 2025
Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
Published on: December 8, 2015
Single nanoparticle identification coupled with auto-identify algorithm for rapid and accurate detection of
Shuting Chen1, Weijia Wang2, Shaohua Xu3
1College of Biological Science and Engineering, Fuzhou University, Fuzhou, Fujian, 350108, China; Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, Fuzhou University, Fuzhou, Fujian, 350108, China.
Abstract:
In this work, an auto-identify sensor was constructed for rapid and high-precision detection of L-histidine. The proposed strategy is based on the auto-identify algorithm and the aggregation of alkynyl and azide functionalized gold nanoparticles induced by the Cu+ catalyzed azides and alkynes cycloaddition (CuAAC) reaction. Specially, the color of scattering light spots for the aggregated gold nanoparticle (AuNPs) caused by CuAAC reaction was quite different from that of the monomers. However, L-histidine can bind to Cu2+ and inhibits the production of Cu+, hence preventing the aggregation of AuNPs. Therefore, there is a distinct change of color as the addition of L-histidine under dark-field microscopy. Then, L-histidine can be quantitatively detected by combining the color change with the Meanshift algorithm accurately and automatically. Such proposed method has been successfully applied for the detection of L-histidine in serum sample with satisfying result.
