The 2022 Roadmap is the next update in the series of Plasma Roadmaps published by Journal of Physics D with the intent to identify important outstanding challenges in the field of low-temperature plasma (LTP) physics and technology. The format of the Roadmap is the same as the previous Roadmaps representing the visions of 41 leading experts representing 21 countries and five continents in the various sub-fields of LTP science and technology. In recognition of the evolution in the field, several new topics have been introduced or given more prominence. These new topics and emphasis highlight increased interests in plasma-enabled additive manufacturing, soft materials, electrification of chemical conversions, plasma propulsion, extreme plasma regimes, plasmas in hypersonics, data-driven plasma science and technology and the contribution of LTP to combat COVID-19. In the last few decades, LTP science and technology has made a tremendously positive impact on our society. It is our hope that this roadmap will help continue this excellent track record over the next 5–10 years.

Gas discharge plasmas formed at atmospheric pressure and near room temperature have recently been shown to be potentially useful for surface and wound sterilization, antisepsis, bleeding cessation, wound healing, and cancer treatment, among other biomedical applications. This tutorial review summarizes the field, stressing the likely role of reactive oxygen and nitrogen species created in these plasmas as the biologically and therapeutically active agents. Reactive species, including radicals and non-radical compounds, are generated naturally within the body and are now understood to be essential for normal biological functions. These species are known to be active agents in existing therapies for wound healing, infection control, and cancer treatment. But they are also observed at elevated levels in persons with many diseases and are associated with aging. The physical and chemical complexity of plasma medical devices and their associated biochemical effects makes the development of safe, effective plasma medical devices and procedures a challenge, but encouragingly rapid progress has been reported around the world in the last several years.

Machine learning (ML) is a set of computational tools that can analyze and utilize large amounts of data for many different purposes. Recent breakthroughs in ML and artificial intelligence largely enabled by advances in computing power and parallel computing present cross-disciplinary research opportunities to exploit some of these techniques in the field of non-equilibrium plasma (NEP) studies. This paper presents our perspectives on how ML can potentially transform modeling and simulation, real-time monitoring, and control of NEP.

A self-consistent two-dimensional fluid-plasma model coupled to Maxwell’s equations is presented for argon discharges sustained at atmospheric pressure by the propagation of an electromagnetic surface wave. The numerical simulation provides the full axial and radial structure of the surface-wave plasma column and the distribution of the electromagnetic fields for given discharge operating conditions. To describe the contraction phenomenon, a characteristic feature of high-pressure discharges, we consider the kinetics of argon molecular ions in the charged-particle balance. An original feature of the model is to take into account the gas flow by solving self-consistently the mass, momentum, and energy balance equations for neutral particles. Accounting for the gas flow explains reported discrepancies between measured and calculated plasma parameters when assuming the local axial uniformity approximation. In contrast to the low-pressure case, the latter approximation is shown to be of limited validity at atmospheric pressure. The gas temperature is found to be a key parameter in modeling surface-wave discharges sustained at atmospheric pressure. It determines the radial and the axial structure of the plasma column. The calculated plasma parameters and wave propagation characteristics using the present two-dimensional fluid model are in good agreement with our set of experimental data.