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N, S-co-doped carbon/Co1-xS nanocomposite with dual-enzyme activities for a smartphone-based colorimetric assay of total cholesterol in human serum.

  • Author(s): Li, Jiani;
  • Liu, Tingting;
  • Dahlgren, Randy A;
  • Ye, Hanzhang;
  • Wang, Qi;
  • Ding, Yongli;
  • Gao, Ming;
  • Wang, Xuedong;
  • Wang, Huili
  • et al.

We fabricated a novel N,S-co-doped carbon/Co1-xS nanocomposite (NSC/Co1-xS) using a facile sol-gel approach, which featured a multiporous structure, abundant S vacancies and Co-S nanoparticles filling the carbon-layer pores. When the Co1-xS nanoparticles were anchored onto the surface of N,S-co-doped carbon, a synergistic catalysis action occurred. The NSC/Co1-xS nanocomposites possessed both peroxidase-like and oxidase-mimetic dual-enzyme activities, in which the oxidase-mimetic activity dominated. By scavenger capture tests, the nanozyme was demonstrated to catalyze H2O2 to produce h+, •OH and •O2-, among which the strongest and weakest signals were h+ and •OH, respectively. The multi-valence states of Co atoms in the NSC/Co1-xS structure facilitated electronic transfer that enhanced redox reactions, thereby improving the resultant color reaction. Based on the NSC/Co1-xS's enzyme-mimetic catalytic reaction, a visual colorimetric assay and Android "Thing Identify" application (app), installed on a smartphone, offered detection limits of 1.93 and 2.51 mg/dl, respectively, in human serum samples. The selectivity/interference experiments, using fortified macromolecules and metal ions, demonstrated that this sensor had high selectivity and low interference potential for cholesterol analysis. Compared to standard assay kits and previously reported visual detection, the Android smartphone-based assays provided higher accuracy (recoveries up to 93.6-104.1%), feasibility for trace-level detection, and more convenient on-site application for cholesterol assay due to the superior enzymatic activity of NSC/Co1-xS. These compelling performance metrics lead us to posit that the NSC/Co1-xS-based nanozymic sensor offers a promising methodology for several practical applications, such as point-of-care diagnosis and workplace health evaluations.

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