Statistical Analysis of the Dynamic Behavior of Individual Discharges During the Ignition and Continuous Phases of Contact Glow Discharge Electrolysis

03 February 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Contact Glow Discharge Electrolysis (CGDE) denotes a plasma inside a vapor layer surrounding a gas-evolving electrode immersed in an aqueous electrolyte and operated at high voltages. We used a high-speed camera to image the formation of the vapor layer as well as its dynamic behavior during continuous CGDE on a Au wire cathode. The plasma ignites with a spark within a large bubble at the tip, which expands along the wire to the top, leaving a stable glow within the vapor layer behind. Using an in-house developed open-source Python-based software we deduced, from a thorough statistical analysis of images taken during continuous CGDE, a vapor layer thickness between 0.1 and 0.4 mm. Furthermore, we provide information on the dynamic behavior of individual discharges through the vapor layer from a series of images. The discharges are confined within the vapor layer and, thus, the extent of the discharges is similar to the vapor layer thickness. We find that the discharges have approximately the shape of oblate spheroids, which appear either as circles or ellipses in the camera images, depending on the orientation of the discharge with respect to the camera. We discuss the relevance of our results for the fundamental understanding of atomic scale surface structural changes and products formed in the solution in the presence of the plasma.

Keywords

plasma
electrolysis

Supplementary materials

Title
Description
Actions
Title
Supporting Information
Description
Additional information to the dataset shown in the manuscript.
Actions
Title
Supporting Information Two
Description
Figures for a second data set supporting the data in the manuscript. The Figure labels are identical to those used in the manuscript.
Actions
Title
Python data analysis
Description
Jupyter notebook containing the open-source Python code used for the evaluation of the datasets used in the manuscript and supporting informations.
Actions

Supplementary weblinks

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.