7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Multi-scale mapping between the control parameter space and a cold
atmospheric plasma chemical space

Li Lin1, Yi Liu2, Sophia Gershman3, Yevgeny Raitses3, Michael Keidar1
1
Department of Mechanical and Aerospace Engineering, School of Engineering and Applied
Science, George Washington University, 2 School of Mechanical Engineering, Shanghai Jiao Tong
University, 3 Princeton Plasma Physics Laboratory
e-mail (speaker): lilin@gwu.edu
Cold atmospheric plasma (CAP) is a low-temperatureplasma exposed to open air without thermal equilibrium,
and it is currently widely used for plasma-based
biomedical applications, relying on a unique chemicalsystem [1]. Based on the air species such as N2, O2, andH2O, CAP can synthesize the reactive oxygen species(ROS) and reactive nitrogen species (RNS) including O,OH, HO2, and NxOy [1]. ROS can lead to cell cycle
arrest and apoptosis for cancer therapy, while NO andother reactive species can assist tissue regeneration, andthe ozone and UV generated by CAP can deactivate
microorganisms for sterilization [2]. However, theplasma chemistry in the open air is thus too complicatedto be manually controlled [3]. There are more than 100
chemical species with 1000 chemical reactions occurringat the same time [4]. Each reaction has a unique andhighly dynamic rate coefficient. Meanwhile, there aremany different types of CAP generators such as plasma
jet and dielectric barrier discharge (DBD). Eachgenerator can also work at different hardware setups
such as discharge voltage, discharge frequency, gas flow
rate, etc. Any one of those hardware setup and controlparameters can change the entire chemical system,
including all the species concentrations.
Therefore, it is urgent to investigate and understand thecomplicated relationship between the control inputs andthe complete concentrations of CAP chemistry. Several
experimental diagnostics have been well-developed andwidely used, such as laser-induced fluorescence (LIF),
optical emission spectroscopy (OES), mass spectroscopy(MS), etc. However, these measurement methods havelimits. For example, LIF relies on the specific species’
energy levels and thus it can only give access toOH-related species. OES, however, merely provides theexcited species information and indirectly provides otherinformation such as the electron temperature byassuming a simplified chemical kinetic scheme. MS canmeasure a relatively full picture of the species spectrum,
but it is not sensitive to low-concentration species, suchas those at about 1 ppm and lower.
Numerical simulations, on the other hand, can overcome
these experimental difficulties. One needs a temporal
resolution at sub-nanosecond to ensure an accurate
simulation of both the chemistry and the dynamicdischarge process, such as the streamer propagation ofCAP jets. However, the actual biomedical applications
usually require CAP chemistry applied to targeting cells
and tissues for several minutes. On the other hand, thespatial resolution of the simulation is usually set at a
sub-micrometer scale while the actual hardware is at a
centimeter scale with a dynamic CAP gas flow field.
Therefore, this is a multi-scale problem for both time andspace, which is challenging to simulate.
In this work, a machine learning (ML) assisted method isdeveloped to solve such a problem. Physics-informed
data-driven modeling is a mapping between the twomathematical spaces: the space of control parameters of
the plasma generator and the space of chemical species
compositions in the plasma. Fourier-transform infrared
spectroscopy (FTIR) is used to measure theconcentrations of NO, NO2, and N2O. Like LIF, FTIR
can merely provide limited species concentrations. Alongwith other physical laws such as mass and charge
conservations, this physical information will be theconstraint to training a physics-informed neural network(PINN). The input of PINN is a randomly selectedcontrol parameter while the output of it will be the full
picture of all species concentrations. They will then be
sent to a 0D chemical simulation to verify if all thespecies are at a steady state. Any changes of them will bean error, while the violation of physics laws andexperimental observations of NO, NO2, and N2O will
also increase the error. The total error will be used in an
evolutionary algorithm to update the weights of PINN
and complete an iteration of the training loop. Finally,the total error will converge to an acceptable low value.
This means that no matter what control parameters areinput to the PINN, it can always provide the full pictureof all the species concentrations at steady state, agreewith the conservation laws, and agree with theexperimental observations for those species available tomeasure. Such a multi-scale problem is thus solved.
References[1]
M. Laroussi et al., “Low-Temperature Plasma for
Biology, Hygiene, and Medicine: Perspective andRoadmap,” IEEE Trans Radiat Plasma Med Sci,
6(2), 127–157, 2022
[2]
M. Laroussi, “Plasma Medicine: A BriefIntroduction,” Plasma, 1(1), 47–60, 2018[3]
L. Lin and M. Keidar, “A map of control for cold
atmospheric plasma jets: From physicalmechanisms to optimizations,” Appl Phys Rev,8(1), 011306, 2021
[4]
L. Lin et al., “Self‐Adaptive Plasma Chemistry
and Intelligent Plasma Medicine,” AdvancedIntelligent Systems, 4(3), 2100112, 2022