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.

To respond to global challenges of environmental contaminations, pursue more advanced material technologies, and achieve novel biomedical therapies, a variety of plasmas have been applied to wastewater and food processing, biomaterial treatments, and plasma−liquid ignitions. As these applications highly depend on the plasma−liquid interactions, researchers are now focusing on the physical and chemical reactions on the plasma−liquid interface. With massive publications reporting the molecular transfers, chemical pathways, and their effects on plasma treatments, this work provides a new point of view that the plasma−liquid interface can be manipulated by the chamber structure. In the experiment, plasma jet expansion in water is recorded in a cylinder chamber and a stepped-wall one. Data collected from the images show that the stepped-wall structure results in a shorter axial interface propagation, a small volume, more symmetry for the plasma jet, and more stability for the interface. To discover the physical mechanism behind these phenomena, we derived the momentum and energy equations for the plasma−liquid interface during its propagation. Those equations reveal how the stepped-wall structure can be used to manipulate the interface behaviors. Along with our experimental and theoretical investigation of the plasma−liquid interface, such information also sheds light on how the chamber wall structure can be used to manipulate the interface chemical reaction rates, stability, and expansion rate. This work is thus a basis of the future optimization for plasma−liquid treatments and ignitions which will be equipped with a flexible wall controlled by artificial intelligence to automatically achieve a variety of plasma treatment requirements.

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