PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 17, 2025Plasma Sources Science and Technology1 citations

Advanced microwave method for electron density profile reconstruction of an atmospheric plasma torch

View Full Paper
CVChristos VagkidisASAndreas SchulzSMStefan Merli

Key Points

  • The study presents a method to extract both line-integrated density and electron-neutral collision frequency.
  • Utilizing a novel approach, a 2D spatial plasma density profile is obtained through microwave power measurement.
  • The scattering profile of the microwave wave provides detailed insights into the electron density distribution.
  • Direct comparisons with 3D simulations enhance the reliability of the reconstructed electron number density profile.

Abstract

Abstract Microwave interferometry is a reliable, well established, and non-perturbing method to measure the line-integrated electron density of a non-uniform plasma through the phase shift of a wave that propagates the plasma medium. In this paper we combine the phase shift and the attenuation of the wave to experimentally extract both, the line-integrated density and the electron-neutral collision frequency of an atmospheric plasma torch. In addition, a novel method to obtain the 2D spatial plasma density profile of the torch is demonstrated by measuring the microwave power, without any information of the phase. The receiving antenna of the interferometer is moved perpendicularly to the axis of the torch and measures the spatial distribution of the microwave power. The wave is scattered by the plasma and the scattering profile depends on the plasma density profile. Direct comparison of this scattering profile with 3D full-wave simulations provides information on the electron number density profile of the plasma torch.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Vagkidis et al. (2025) studied this question.

synapsesocial.com/papers/68d4567431b076d99fa5bf48https://doi.org/10.1088/1361-6595/ae0762
Ask AI
Helpful
Bookmark
Share
View Full Paper