Monte Carlo simulation of full energy spectrum of electrons emitted from silicon in Auger electron spectroscopy

A theoretical characterization method for non-spherical core-shell nanoparticles by XPS

Core-shell nanoparticles (NPs) are active research areas for their unique properties and wide applications. By changing the elemental composition in the core and shell, a series of core-shell NPs with specific functions can be obtained, where the sizes of the core and shell also influence the properties. X-ray photoelectron spectroscopy (XPS) is useful in this context as a means of quantitatively analyzing such NPs. The empirical formula proposed by Shard [J. Phys. Chem. C, 2012, 116(31), 16806-16813] for calculating the shell thickness of the spherical core-shell NPs has been verified by Powell et al. [J. Phys. Chem. C, 2016, 120(39), 22730-22738] through a simulation of XPS with Simulation of Electron Spectra for Surface Analysis (SESSA) software. However, real core-shell NPs are not necessarily ideal spheres; such NPs can have rich shapes and uneven thicknesses. This work aims to extend the Shard formula to non-ideal core-shell NPs. We have used a Monte Carlo simulation method to study the XPS signal variation with the shell thickness for several modeled non-spherical shapes of core-shell NPs including some complex geometric structures which are numerically constructed with finite-element triangular meshes. Five types of non-spherical shapes, i.e. egg, ellipsoid, rod, rough-surface, and star shapes, are considered, while the size parameters are varied over a wide range. The equivalent radius and equivalent thickness are defined to characterize the average size of the nanoparticles for the use of the Shard formula. We have thus derived an extended Shard formula for the specific core-shell NPs, with which the relative error between the predicted shell thickness and the real thickness can be reduced to less than 10%.

Establishment and validation of an electron inelastic mean free path database for narrow bandgap inorganic compounds with a machine learning approach

Narrow bandgap inorganic compounds are extremely important in many areas of physics. However, their basic parameter database for surface analysis is incomplete. Electron inelastic mean free paths (IMFPs) are important parameters in surface analysis methods, such as electron spectroscopy and electron microscopy. Our previous research has presented a machine learning (ML) method to describe and predict IMFPs from calculated IMFPs for 41 elemental solids. This paper extends the use of the same machine learning method to 42 inorganic compounds based on the experience in predicting elemental electron IMFPs. The in-depth discussion extends to including material dependence discussion and parameter value selections. After robust validation of the ML method, we have produced an extensive IMFP database for 12 039 narrow bandgap inorganic compounds. Our findings suggest that ML is very efficient and powerful for IMFP description and database completion for various materials and has many advantages, including stability and convenience, over traditional methods.

Charging effect induced by electron beam irradiation: a review

Charging effect frequently occurs when characterizing nonconductive materials using electrons as probes and/or signals and can impede the acquisition of useful information about the material under investigation. It is not adequate to investigate it merely by experiments, but theoretical investigations, for which the Monte Carlo method is a suitable tool, are also necessary. In this paper we review Monte Carlo simulations and selected experiments, intending to provide general insight into the charging effects induced by electron beam irradiation. We will introduce categories of the charging effect, the theoretical framework that is adopted in Monte Carlo modeling of the charging effect and present some typical simulation results. At last, with the knowledge on charging effect imparted by the above contents, we will discuss the measures that can be used for minimizing it.

Ensemble machine learning methods: predicting electron stopping powers from a small experimental database

Electron stopping power (SP) is of great importance in theoretical and applied research areas specifically for Monte Carlo simulation studies in many microanalysis and surface analysis techniques, radiation dosimetry, and the design of particle detectors. However, experimental data are available for a dozen elemental materials only. On the other hand, the Bethe analytical expression of the SP is applicable at high energies only whereas no generally accepted formula exists at lower energies. We employed ensemble machine learning (ML) methods with the available experimental database for the prediction of SPs of electrons with energies from 100 keV down to 1 eV, in elements over the entire periodic table. With a small training database for electron SPs, we applied various algorithms individually as well as their ensembles, which have the credibility to enhance the prediction accuracy in the case of a small training database. Based on the model's performance evaluation tests, we concluded that the stacked generalization is more accurate than the individual algorithms. Using this method, we were able to predict the electron SPs for 54 elements (in total) including 12 elements that were present in the training database as well as for 42 elements beyond the training database over a wide energy range (1 eV to 100 keV). Compared to other theoretical approaches, the ML predicted SPs show very good agreement with the available experimental data at all energies. Moreover, unlike other theoretical approaches, the ML model does not need dielectric function data and other physical parameters which involve complex calculations. Using our ML model, we have predicted SPs for a further 14 elements for which no theoretical SPs are available because of the lack of good dielectric function data.

Quantum-trajectory Monte Carlo method for study of electron–crystal interaction in STEM

A quantum trajectory Monte Carlo method is developed to simulate electron scattering and secondary electron cascade process in crystalline specimen.