4.6 Article

Development of a Novel Biosensor Based on Tyrosinase/Platinum Nanoparticles/Chitosan/Graphene Nanostructured Layer with Applicability in Bioanalysis

期刊

MATERIALS
卷 12, 期 7, 页码 -

出版社

MDPI
DOI: 10.3390/ma12071009

关键词

biosensor; screen-printed carbon electrode; nanomaterial; nanoparticle; bionanocomposite; square wave voltammetry; L-tyrosine; bioanalysis

资金

  1. Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI [PN-II-RU-TE-2014-4-1093]

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The present paper describes the preparation and characterization of a graphene, chitosan, platinum nanoparticles and tyrosinase-based bionanocomposite film deposited on the surface of a screen-printed carbon electrode for the detection of L-tyrosine by voltammetry. The redox process on the biosensor surface is associated with the enzymatic oxidation of L-tyrosine, which is favoured by graphene and platinum nanoparticles that increase electrical conductivity and the electron transfer rate. Chitosan ensures the biocompatibility between the tyrosinase enzyme and the solid matrix, as well as a series of complex interactions for an efficient immobilization of the biocatalyst. Experimental conditions were optimized so that the analytical performances of the biosensor were maximal for L-tyrosine detection. By using square wave voltammetry as the detection method, a very low detection limit (4.75 x 10(-8) M), a vast linearity domain (0.1-100 M) and a high affinity of the enzyme for the substrate (K-M(app) is 53.4 M) were obtained. The repeatability of the voltammetric response, the stability, and the reduced interference of the chemical species present in the sample prove that this biosensor is an excellent tool to be used in bioanalysis. L-tyrosine detection in medical and pharmaceutical samples was performed with very good results, the analytical recovery values obtained being between 99.5% and 101%. The analytical method based on biosensor was validated by the standard method of analysis, the differences observed being statistically insignificant at the 99% confidence level.

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