Journal
TALANTA
Volume 161, Issue -, Pages 520-527Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.talanta.2016.09.009
Keywords
Gold nanoparticles; Pectinase; Colorimetric sensor; Cysteine
Categories
Funding
- National Natural Science Foundation of China [21305082, 21403135, 21575084, 21475080]
- Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi
- Shanxi Scholarship Council of China [2014-017]
- Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province
- 131 Leading Talents Project of Higher Learning Institutions of Shanxi
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Herein, a novel, high sensitive, and specific colorimetric sensor for cysteine (Cys) based on pectinase protected gold nanoparticles (P@AuNPs) has been demonstrated for the first time. The P@AuNPs were synthesized by MW-assisted heat method and were characterized by UV-vis, TEM, FT-IR and zeta potential techniques. Cys could cause the aggregation of P@AuNPs due to formation of the strong covalent Au-S bond and electrostatic binding. As the Cys concentration increased, the color of the solutions gradually changed from wine-red to blue as well as the large absorption band shifted from 523 to 650 nm upon P@AuNPs aggregation. In addition, various experimental parameters such as pH, the amount of P@AuNPs and incubation time were investigated for the optimum sensing conditions. The concentration of Cys could be determined by monitoring with the naked eye or a UV-vis spectrometer. The proposed colorimetric sensor showed an extreme selectivity toward the determination of Cys in the presence of 20-fold all other different interferents. Under optimum conditions, this method exhibited two good linear ranges from 4.85 x 10(-9) to 3.02 x 10(-4) M (R-2=0.996) and 3.25 x 10(-3) to 1.03 x 10(-2) M (R-2=0.999), with a low detection limit of 4.6 x 10(-9) M. Moreover, this colorimetric sensor was successfully applied to the detection of Cys in human urine samples, demonstrating its great value for practical application in biological systems. (C) 2016 Elsevier B.V. All rights reserved.
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