Traditional biosensors are costly, cumbersome, and take a long time to report results, which limits their use in resource-constrained areas. Due to its high integration, customized architecture, and ease of mass processing, biosensors have been widely used in biomedical applications in recent years. In this paper, a biosensor is implemented using flexible printed circuit board technology for monitoring and identifying the effect of Baculovirus infection on one of its host cells (Sf9 cells) through many electrical features such as capacitance, impedance, permittivity, and conductivity. Furthermore, the effect of viral infection with different particle concentrations at different times was characterized. The results proved that the virus has a rapid effect on the cell by losing its electrical properties, which were clear from the decrease in electrical properties (e.g., capacitance, impedance, permittivity, and conductivity). Moreover, we introduced an equivalent model to represent the normal cells and infected cells based on the experimental results obtained for capacitance values. The proposed model simulation results are matched with the results obtained experimentally, which leads to the use of the model for predicting baculovirus infection. © 2022 The Authors
Traditional biosensors are costly, cumbersome, and take a long time to report results, which limits their use in resource-constrained areas. Due to its high integration, customized architecture, and ease of mass processing, biosensors have been widely used in biomedical applications in recent years. In this paper, a biosensor is implemented using flexible printed circuit board technology for monitoring and identifying the effect of Baculovirus infection on one of its host cells (Sf9 cells) through many electrical features such as capacitance, impedance, permittivity, and conductivity. Furthermore, the effect of viral infection with different particle concentrations at different times was characterized. The results proved that the virus has a rapid effect on the cell by losing its electrical properties, which were clear from the decrease in electrical properties (e.g., capacitance, impedance, permittivity, and conductivity). Moreover, we introduced an equivalent model to represent the normal cells and infected cells based on the experimental results obtained for capacitance values. The proposed model simulation results are matched with the results obtained experimentally, which leads to the use of the model for predicting baculovirus infection. © 2022 The Authors