Detection and intelligent optimization of blood glucose signals based on MXene-assisted sensors

Abstract

Two-dimensional materials exhibit unique properties that make them ideal for electronic sensing applications, particularly in the field of biosensors. In this study, a Ti3C2Tx MXene/graphene composite was investigated for its potential use in non-invasive blood glucose detection. The strong electrostatic interaction between MXene and graphene nanosheets effectively reduces the self-stacking of both components, resulting in the formation of a porous interlayer structure with enlarged interlayer spacing. This unique structure creates an efficient nanoscale transport channel, which increases the number of active sites and shortens ion diffusion paths. Consequently, the electrochemical properties of the composite membrane are enhanced, making it a promising candidate for sensing element applications. As the human blood glucose level is primarily determined by the glucose concentration in the blood, the concentration of glucose solution serves as the foundation for noninvasive blood glucose concentration detection. In this work, a prediction model was established using the partial least squares fitting method to correlate the electrochemical fingerprinting data with the glucose aqueous solution concentration, and the reliability of the model was verified. To address the limitations of the partial least squares method, which lacks nonlinear fitting capabilities, an RBF (radial basis function) network model was employed. Finally, a genetic algorithm was utilized to perform global optimization of the training parameters of the RBF network, and the quantitative relationship between the NIR spectral data and glucose concentration was validated. The findings of this study provide a theoretical basis for the development of equipment and the improvement of measurement accuracy in the field of human noninvasive glucose detection.