Foundations of plasma standards

Abstract

The field of low-temperature plasmas (LTPs) excels by virtue of its broad intellectual diversity, interdisciplinarity and range of applications. This great diversity also challenges researchers in communicating the outcomes of their investigations, as common practices and expectations for reporting vary widely in the many disciplines that either fall under the LTP umbrella or interact closely with LTP topics. These challenges encompass comparing measurements made in different laboratories, exchanging and sharing computer models, enabling reproducibility in experiments and computations using traceable and transparent methods and data, establishing metrics for reliability, and in translating fundamental findings to practice. In this paper, we address these challenges from the perspective of LTP standards for measurements, diagnostics, computations, reporting and plasma sources. This discussion on standards, or recommended best practices, and in some cases suggestions for standards or best practices, has the goal of improving communication, reproducibility and transparency within the LTP field and fields allied with LTPs. This discussion also acknowledges that standards and best practices, either recommended or at some point enforced, are ultimately a matter of judgment. These standards and recommended practices should not limit innovation nor prevent research breakthroughs from having real-time impact. Ultimately, the goal of our research community is to advance the entire LTP field and the many applications it touches through a shared set of expectations.

Keywords: data and reaction mechanisms; open source codes; plasma diagnostics; plasma dose; standard plasma sources; standards and best practice; verification and validation.

Figure 1.

The GECRC continues to be a benchmark for validating codes and investigating fundamental plasma properties. (a) Simulation results using the SOMAFOAM platform for plasma potential and Ar⁺ density in a CCP sustained in 100 mTorr Ar powered with 400 V peak-to-peak at 13.56 MHz. (b) Comparison to experimental results. Reprinted from [6], Copyright (2021), with permission from Elsevier.

Figure 2.

Reproducibility of the COST-jet: plasma and device properties in four sources were measured independently. The black dots indicate measured data from one jet, the shaded area indicates the deviation between the four sources. (Left) Plasma power as a function of voltage and (right) measured effluent gas temperature at 3 mm distance from the nozzle as a function of plasma power. Reproduced from [8]. © The Author(s). Published by IOP Publishing Ltd. CC BY 4.0.

Figure 3.

Comparison of measured absolute atomic oxygen densities in similar plasma sources using different diagnostic techniques: ns-TALIF [36], synchrotron VUV absorption [10] and ps-TALIF [37]. The plasma sources operate in a helium carrier gas flow through a 1 mm discharge gap with varying humidity admixtures. The power delivery is a RF-CCP at 13.56 MHz. Reproduced from [37]. © The Author(s). Published by IOP Publishing Ltd. CC BY 4.0.

Figure 4.

Comparison of experimental measurements of absolute atomic oxygen densities and computational simulations as a function of humidity admixture. Experimental measurements were carried out using ps-TALIF. Computational simulations were based on GlobalKin [43] using different levels of O₂ impurities. Reproduced from [37]. © The Author(s). Published by IOP Publishing Ltd. CC BY 4.0.

Figure 5.

Verification by convergence toward an exact solution as a numerical parameter is changed. In this example, the physical parameter is the conduction current density flowing in a thermionic diode, and the numerical parameter is the time step in a particle-in-cell simulation. The points with error bars are simulation data, and the curve indicates the expected rate of convergence, which is O(Δt²) in this case. Reproduced from [94]. © IOP Publishing Ltd. All rights reserved.

Figure 6.

(Left) Time evolution of the electron density and (right) the ionization rate calculated by the participants of the 2017–2018 round-robin exercise for the modelling of a pure argon plasma with four species (Ar, Ar* and Ar⁺ and e) undergoing the following electron-impact collisions: elastic scattering with Ar, direct excitation, direct ionization and dielectronic recombination. The plasma is excited by applying an electric field pulse to the neutral gas at 0.1 bar pressure and 300 K temperature, for initial electron and ion densities of 1 cm⁻³. The insert caption identifies the different model approximations that were used, where LFA refers to the ‘local field approximation’ in the Boltzmann-chemistry coupling.

Figure 7.

Schematic of recommendations for code development. Designed using resources from Freepik.com.

Figure 8.

Different types of data and their sources and the different forms in which they are reported.

Figure 9.

Example of filtered charge-voltage data, also known as a Lissajous plot, from a dielectric barrier discharge. Reproduced from [149]. CC BY 3.0.

Figure 10.

Example of data being presented as the raw data (points) with the statistical information overlaid (red bars for the respective averages and 95% confidence interval error bars). Data are from DBD experiments in packed beds with different materials and reflect the average number of filaments per half-cycle for different material configurations. Reproduced from [154]. © IOP Publishing Ltd. All rights reserved.

Figure 11.

Surface concentrations of oxygen [O] and nitrogen [N] as a function of J cm⁻² of plasma energy incident onto the surface after treating biaxially-oriented polypropylene (BOPP) using an air corona and an atmospheric pressure glow discharge (APGD) sustained in nitrogen. The repetition rate was 1 kHz. Reproduced from [202], with permission from Springer Nature.

Figure 12.

Common approaches, typical challenges and identified routes to success for technology transfer in the field of plasma science and technology.