Abstract
The gradual research in integrating artificial intelligence in the Dielectrophoresis system is rapid since the evolution of AI in every aspect of technology since the early 2020s. The benefits of AI integration into DEP systems include improving position and accuracy, faster processing and decision-making, enhancing particle classification, reducing human error, and many others. On the other hand, DEP research often focuses on CMF values of the particles. CMF values explain the behavior of the particle under the influence of the electric force in terms of trajectory and force magnitude. CMF values are calculated from the equation that requires the conductivity and permittivity of the medium and particles. One important aspect of CMF values is that they are non-numerical. Although possible, it is difficult to develop an algorithm using non-numerical values for detection applications. Hence, the study will focus on translating the non-numerical CMF values of E. coli and S. aureus into velocity (meters per second) and force (Newtons) parameters. In this study, we develop a simple method of calculating velocity and force units of bacterial movement using pixel coordinates and the time frame of the recorded video. From there, we managed to plot a force and velocity curve experimentally, with a crossover frequency of 1.0 to 1.5 MHz for S. aureus bacteria and 500 to 600 kHz for E. coli bacteria. Then, we validated our results with the velocity and force curves extracted from COMSOL simulation and the CMF curve extracted from MYDEP simulation. Our results show that the experimental curve plotted agrees with the simulation curve plotted from the COMSOL simulation, and the crossover frequency plotted in the experiment agrees with the CMF curve from MYDEP. The conclusion of the study is that the method developed in the study is important as the first step for the development of an artificial intelligence system to be integrated into the DEP system. The additional parameter of velocity alongside crossover frequency will improve the detection accuracy of bacterial cells using DEP technology. Furthermore, the collective data from future studies using this method will push DEP technology for future benefits.