A numerical simulation and high-speed photographic study of the audio-frequency noise in an analytical ICP source

Abstract

Signal noise is a crucial factor connecting instrument design and analytical performance in ICP-MS. The audio-frequency (a.f.) noise observed in the signal noise power spectra is caused by the turbulent mixing of entrained air with the plasma flow. In this paper, we proposed a COMSOL®-based numerical method for predicting the fundamental parameters of an analytical ICP source. The proposed method was verified by comparing the fundamental parameters simulated in this work with the experimental/simulation values from the literature. In a custom-built ICP source, we observed ion clouds originating from individual rare-earth-element powder and also investigated the characteristics of gas flow pulsation using a high-speed camera. The simulated velocity downstream of the torch exit agrees with the experimentally determined velocity, demonstrating the reliability of the proposed numerical method. By retrieving the maximum value and the half-width at half maximum in the simulated profile of gas velocity at the torch outlet in the absence of an interface, the pulsation frequency could be predicted accurately using an empirical formula. The effects of torch geometry, sample type and operating parameters on the pulsation frequency were evaluated. The dependence of the pulsation frequency on the sampling depth was investigated in the presence of an interface. The experimental data reported in the literature should be attributed to a coupling effect of vortex dissipations in the longitudinal and lateral directions. Finally, the suppression mechanism of using a bonnet on the a.f. noise was explored by comparing the flow fields without and with a bonnet. Introducing a bonnet or an extension makes the entrainment position move away from the analytical zone and reduces the fluctuation intensity, eliminating the a.f. noise. The work presented in this paper is beneficial for ICP-MS instrument optimization, analytical performance improvement and data interpretation.