Abstract

Non-equilibrium plasmas derive their low temperature reactivity from producing and driving energetic electrons and active species under large electric fields. Therefore, the impact of reactants on the plasma properties including electron number density, electric field, and electron temperature is critical for applications such as plasma methane (CH reforming. Due to experimental complexity, electron properties and the electric field are rarely measured together in the same discharge. In this work, we combine time-resolved Thomson scattering and electric field induced second harmonic generation to probe electron temperature, electron density, and electric field strength in a 60 Torr CH4/Ar nanosecond-pulsed dielectric barrier discharge while varying the CH4 mole fraction from 0% to 8%. These measurements are compared to a 1D numerical model to benchmark its predictions and identify areas of uncertainty. Nonlinear coupling between CH4 addition, electron temperature, electron density, and the electric field was directly observed. Contrary to previous measurements in He, the electron temperature increased with CH4 mole fraction. This rise in electron temperature is identified as electron heating by residual electric fields that increased with larger CH4 mole fraction. Moreover, the electron number density has been found to decrease rapidly with the increase of methane mole fraction. Comparison of these measurements with the model yielded better agreement at higher CH4 mole fractions and with the usage of ab initio calculated Ar electron-impact cross-sections from the B-spline R-matrix database. Furthermore, the calculated plasma properties are shown to be sensitive to the residual surface charge implanted on the quartz dielectric surfaces. Without considering surface charge in the simulations, the calculated electric field profiles agreed well with the measurements, but the electron properties were underpredicted by more than a factor of three. Therefore, measurements of either the electric field or electron properties measurements alone are insufficient to fully validate modeling predictions.

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