Mengping Huang,Shuai Ma,Jinrong He,Wei Xue,Xueyan Hou,Yuqi Zhang,Xiaofeng Liu,Heping Bai,Ran Li
Amino acids found in minor coarse cereals are essential for human growth and development and play a crucial role in efficient and rapid quantitative detection. Surface-enhanced Raman spectroscopy (SERS) enables nondestructive, efficient, and rapid sample detection. Traditional SERS detection efficiency is constrained by the use of a single target. In this study, three different amino acids (cysteine, valine, and tryptophan) were detected simultaneously using a ZIF-8@AuNPs composite substrate. The linear range of detection was 10−3to 10−1M, with limits of detection (LODs) of 2.40 × 10−4M, 2.24 × 10−4M, and 1.55 × 10−4M, respectively. Same linear ranges and LODs were achieved with a one-dimensional convolutional neural network method. Furthermore, this substrate enabled the effective detection of amino acids in millet and efficient detection of cysteine in health products. This study presents a novel method for simultaneous detection of multiple analytes.
Metal–organic frameworks (MOFs) with fit ligands and metals can be integrated into electrochemical biosensors for the detection of various biomolecules. In this study, we have synthesized novel magnetic MOF composites as electrocatalysts and constructed a novel biosensor for electrochemical detection of dopamine. The composites named Fe3O4@ZIF-8@AuNPs–COOH are synthesized through layer-by-layer assembly. They exhibit excellent stability and cooperative catalytic activity. In addition, green recycling is readily achieved through magnetizing/demagnetizing the electrode. The large specific surface area and ordered porous structures of the magnetic MOFs ensure good dispersion of gold nanoparticles, while the carboxyl group efficiently shields other redox-active interfering substances. The proposed electrochemical biosensor accomplishes the sensitive detection of dopamine in human serums and living cells. This study broadens the application of MOFs in electrochemical biosensing, validates the feasibility of biosensors for in vivo analysis, and provides new insights into green sensing.
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