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Nanozymes have drawn considerable attention because of their lower cost, higher stability and convenient preparation compared to protein enzymes. In the present work, Mo, S co-doped carbon quantum dots (Mo-CQDs) as a peroxidase mimic were used to fabricate a cascade colorimetric biosensor to detect cholesterol. The Mo-CQDs possess a robust peroxidase-like activity, and they can easily catalyze 3,3,5,5-tetramethylbenzidine (TMB) to produce an oxidized TMB in the presence of HO. The Mo, S doping in the carbon quantum dots (CQDs) notably boosts the yield of CQDs and may facilitate the electron transfer between TMB and HO, which further enhances the catalytic activity of CQDs. The colorimetric biosensor based on Mo-CQDs and cholesterol oxidase exhibited excellent selectivity and high sensitivity for cholesterol in the range of 0.01-1.0 mM along with a detection limit as low as 7 μM. The total cholesterol concentration in the serum sample was measured with satisfactory results and read out by the naked eye, indicating the potential application in clinical diagnosis and portable test kits.
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http://dx.doi.org/10.1039/c9tb01731c | DOI Listing |
Anal Chem
September 2025
Institute of Biological Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria.
The discovery of solute precursors of crystalline materials, such as biominerals, recently challenged the classical nucleation theory (CNT). One emerging method for investigating these early-stage intermediates in solution is dissolution dynamic nuclear polarization (dDNP)-enhanced nuclear magnetic resonance (NMR) spectroscopy. Recent applications of dDNP to calcium carbonate (CaC) and calcium phosphate (CaP) mineralization have demonstrated the feasibility of identifying and tracing very early-stage prenucleation clusters (PNCs).
View Article and Find Full Text PDFNano Lett
September 2025
Institute of Energy Materials Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Ampere-level electrocatalytic nitrate reduction to ammonia (eNRA) offers a carbon-neutral alternative to the Haber-Bosch process. However, its energy efficiency is critically hampered by the inherent conflict between the reaction and diffusion. Herein, we propose a reaction-diffusion-coupled strategy implemented on a well-tailored CuCoNiRuPt high-entropy alloy aerogel (HEAA) to simultaneously realize energy barrier homogenization and accelerate mass transport, endowing ampere-level eNRA with a high energy efficiency.
View Article and Find Full Text PDFMikrochim Acta
September 2025
Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan Province, 650500, China.
Iron-cerium co-doped carbon dots (Fe,Ce-CDs) were synthesized by one-step hydrothermal method using tartaric acid and L-tryptophan as ligands. Fe,Ce-CDs shows excellent peroxidase-like (POD) activity and nitrite (NO) can promote the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to its blue oxidation product (oxTMB) due to the formation of ∙NO free radical. NO further react with oxTMB to form a yellow color via diazotization resulting in the absorbance Change at 450 nm.
View Article and Find Full Text PDFInt J Biol Macromol
September 2025
School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430023, China. Electronic address:
Quantum dots, with their superior intrinsic fluorescence and photostability, are emerging as a promising option for cancer gene therapy, diagnosis, and imaging. However, low gene delivery efficiency, insufficient targeting, and responsiveness remain challenges. To address these issues, PEI-based carbon quantum dots (CPNCs) were constructed by crosslinking polyethylenimine quantum dots (PQDs) with carbon quantum dots (CQDs) via disulfide bonds.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
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