[Micromachines 2022] Accepted Paper: Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency

Our paper “Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency” has been accepted to the journal Micromachines. Congratulations Louis. This is the collaborative work with Prof. Lawrence Kulinsky’s group from the MAE department.