Introducing adaptive cold atmospheric plasma: The perspective of adaptive cold plasma cancer treatments based on real-time electrochemical impedance spectroscopy

Abstract

Following the understanding of the cold atmospheric plasma jet control, the optimization of plasma parameters for biomedical applications has become an important area of research in the field of plasma-based cancer treatment. A real-time feedback signal is usually required by a control algorithm, such as a self-adaptive plasma jet, which is designed to automatically self-optimize its parameters to adapt to a variety of biomedical applications and situations. In this paper, we introduce the potential of replacing the cell viability or cell stress assay with electrochemical impedance spectroscopy (EIS) to provide a real-time feedback signal for a model predictive control (MPC) method aided by machine learning. The EIS frequency is in the kHz to GHz regime. Therefore, the MPC method is not only designed for minimizing the cancer cell viability, but also considered to optimize cell membrane behaviors and other chemical species dialing. Since these signals are in the range of GHz, we introduce alternatives for the impedance analyzer to measure the impedance spectrum, including a Fabry–Pérot resonator and one of its scanning-array variations.