Retarding potential analyzer: Principles, designs, and space applications

Abstract

The main emphasis of this paper is on the use of Retarding Potential Analyzers (RPAs) for measuring the electron and ion distribution functions, density, and temperature of space plasmas and the charging level of spacecraft surfaces interacting with the plasmas. Multiple grids of progressively negative potential are used to suppress the secondary electrons produced at the entrance of an RPA. We point out that it is impossible to achieve complete suppression. The secondary electrons collected by the receiver may produce a spurious lump in the distribution measured. Improved designs and space applications for spacecraft charging and ionic liquid ion beam diagnostics are discussed. Spacecraft charging will be very important for planetary explorations, whereas ionic liquid ion beams will be important for spacecraft propulsion in the future.

A Review on CubeSat Missions for Ionospheric Science

“…A retarding potential analyzer (RPA) is a type of electrostatic analyzer [93] that works based on the Debye length, i.e., based on the distance that an electrostatic charge can significantly influence a particle. This length depends on the electrons' temperature and density and defines the spacing between grids.…”

Magnetic Nozzle and RPA Simulations vs. Experiments for a Helicon Plasma Thruster Plume

“…). The usual diagnostic tools employed in the experimental characterization of plasma thrusters plumes include, among others, retarding potential analyzers (RPA) [7], Faraday probes (FP) [8], Langmuir probes (LP) [9,10], emissive plasma probes [11], laser induced fluorescence techniques (LIF) [12]. Of particular relevance is the RPA, which has been used for decades to measure relevant plasmas properties in both space plasmas [13] and plasma thruster plumes [14,15], such as the ion velocity distribution function (IVDF).…”

“…Given its simplicity and usefulness, improvements of the RPA design that reduce measurement errors have been constantly proposed for the last decades [16,17]. In this context, the RPA design optimization can greatly benefit from numerical synthetic simulations: in fact, the effects of varying the geometry of the grids and even advanced RPA concepts [7] can be quickly evaluated without having to build a costly experimental prototype.…”