Characterization of AI-enhanced magnetophoretic transistors operating in a tri-axial magnetic field for on-chip...

We demonstrate two general classes of magnetophoretic transistors, called the "trap" and the "repel-and-collect" transistors, capable of switching single magnetically labeled cells and magnetic particles between different paths in a microfluidic chamber. Compared with prior work on magnetophoretic transistors operating in a two-dimensional in-plane rotating field, the use of a tri-axial magnetic field has the fundamental advantages of preventing particle cluster formation and better syncing of single particles with the general operating clock. We use finite element methods to investigate the energy distribution on the chip surface and to predict the particle behavior at various device geometries. We then fabricate the proposed transistors and compare the experimental results with the simulation predictions. We found that with gate electrical currents of ~ 40 mA for a transistor with proper geometry, complete switching of magnetic particles with diameters in the range of 8-15 μm is achieved. We show that the device is reliable and works well at different magnetic field strengths (50-100 Oe) and frequencies (0.05-0.5 Hz). We also employed an image processing code with a trained convolutional neural network to automate the proposed transistors for identifying and sorting particles with various sizes and magnetic susceptibilities with accuracies higher than 98%. The proposed transistors can be used in designing novel magnetophoretic circuits for important applications in biomedical microdevices and single-cell biology.