Non-enzymatic glucose sensor based on molecularly imprinted polymer: a theoretical, strategy fabrication and application
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A comprehensive theoretical screening of functional monomers, structural optimization, interaction energies (ΔE), and Gibbs free energy changes (ΔG) calculations of the preparation of molecularly imprinted polymer (MIP) were performed using density functional theory (DFT) method. Based on the thermodynamic and interaction energy calculations, it is found that acrylamide (AAm) as a functional monomer candidate has the potential to interact with glucose more efficiently for the preparation of MIP. In this work, on the basis of the theoretical calculations for the functional monomer selection in the MIP preparation, an electrochemical impedance sensor based on porous Ni foam modified with MIP (MIP@Ni) was developed for the glucose detection. The morphology and the electrochemical characteristics of the fabricated sensor were characterized by scanning electron microscopy, X-ray powder diffraction, cyclic voltammetry, and electrochemical impedance spectroscopy. The linear range and limit of detection were in the range of 0.8–4.0 mM and 0.45 mM with a signal to noise ratio of three in alkaline medium. Common interfering species such as ascorbic acid (AA), D-fructose, and 4-acetaminophenol (AP) were demonstrated have less effects on the glucose determination. The MIP@Ni foam exhibited better selectivity, which has a potential application in the advanced non-enzymatic glucose monitoring device.
KeywordsDensity functional theory calculations Molecularly imprinted polymer Glucose
This work was financially supported by the National Natural Science Foundation of China (31601549 and U1507115), the Natural Science of Jiangsu Education (16KJB150045), the China Postdoctoral Science Foundation funded project (2016 M601747), the Qing Lan Project of the Higher Education Institutions of Jiangsu Province, the Start-Up Research Fund from Jiangsu University of Technology, and the Training Program of Jiangsu Excellent Talents in Higher Vocational College (2017GRFX066). All calculations were supported by High-Performance Computing Platform of Jiangsu University.
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