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Updated: May 16, 2025

Generation of Scalable, Metallic High-Aspect Ratio Nanocomposites in a Biological Liquid Medium
Published on: July 8, 2015
A dual-mode nanoplatform based on Cu2O@Au-Pt nanoenzyme and CHA-HCR DNA framework circuit for sensitive detection of
Shiyu Liu1, Yashu Wei2, Ting Chen1
1Education Department of Guangxi Zhuang Autonomous Region, Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Products, Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, School of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning 530006, China.
Abstract:
A Cu2O@Au-Pt nanoenzyme is prepared and integrated with a DNA nanocircuit to construct an electrochemical/colorimetric dual-mode sensing platform for the sensitive detection of CD122 and CD17, which are biomarkers for immune-related diseases.When CD122 is present, it triggers a catalyzed hairpin assembly (CHA) reaction, forming a double-stranded structure recognized by the DNA framework's "arms" (C-DNA), with methylene blue (MB) adsorbing onto it as an electron receptor. Methylene blue (MB) is adsorbed onto the double-stranded structure and introduced as a cathodic electron receptor. In the presence of the target CD17, it acts as a bridging molecule to engage in a hybridization chain reaction (HCR), producing HCR products that are specifically recognized by the other set of "arms" within the C-DNA, which captures abundant MB and generates a heightened response signal. MB can be electrochemically reduced to its reduced state to cause a change in solution color and an increase in RGB Blue values. This method offers a detection range of 0.0001-10,000 pM, with detection limits for CD122 and CD17 of 23.9 and 36.0 aM (S/N = 3) using the electrochemical method, and 26.2 and 26.6 aM (S/N = 3) in the colorimetric mode, respectively. This study presents a novel, reliable multi-target detection strategy for disease biomarkers.

