Multi-element electrostatic aperture lens systems are widely used to control electron or charged particle beams in many scientific instruments. By means of applied voltages, these lens systems can be operated for different purposes. In this context, numerous methods have been performed to calculate focal properties of these lenses. In this study, an artificial neural network (ANN) classification method is utilized to determine the focused/unfocused charged particle beam in the image point as a function of lens voltages for multi-element electrostatic aperture lenses. A data set for training and testing of ANN is taken from the SIMION 8.1 simulation program, which is a well known and proven accuracy program in charged particle optics. Mean squared error results of this study indicate that the ANN classification method provides notable performance characteristics for electrostatic aperture zoom lenses.

The microstructural evolution of a 2101 lean duplex stainless steel (DSS) during isothermal aging from room temperature to 470 °C was investigated using thermoelectric power (TEP) measurements to follow the kinetics, atom probe tomography, and transmission electron microscopy. Despite the low Ni, Cr, and Mo contents, the lean DSS was sensitive to α–α′ phase separation and Ni–Mn–Si–Al–Cu clustering at intermediate temperatures. The time–temperature pairs characteristic of the early stages of ferrite decomposition were determined from the TEP kinetics. Considering their composition and locations, the clusters are most likely G phase precursors.