RESUMO
Frequency-mixing technology has been widely used to precisely identify magnetic nanoparticles in applications of quantitative biomedical detection in recent years. Examples include immune adsorption, lateral flow assays (LFAs), and biomagnetic imaging. However, the signals of magnetic response generated by adjacent magnetic samples interfere with each other owing to the small spacing between them in applications involving multi-sample detection (such as the LFA and multiplexing detection). Such signal interference prevents the biosensor from obtaining characteristic peaks related to the concentration of adjacent biomarkers from the magnetic response signals. Mathematical and physical models of the structure of sensors based on frequency-mixing techniques were developed. The theoretical model was verified and its key parameters were optimized by using simulations. A new frequency-mixing magnetic sensor structure was then designed and developed based on the model, and the key technical problem of signal crosstalk between adjacent samples was structurally solved. Finally, standard cards with stable magnetic properties were used to evaluate the performance of the sensor, and strips of the gastrin-17 (G-17) LFA were used to evaluate its potential for use in clinical applications. The results show that the minimum spacing between samples required by the optimized sensor to accurately identify them was only about 4-5 mm, and the minimum detectable concentration of G-17 was 11 pg mL-1 . This is a significant reduction in the required spacing between samples for multiplexing detection. The optimized sensor also has the potential for use in multi-channel synchronous signal acquisition, and can be used to detect synchronous magnetic signals in vivo.
