Search

We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses. [ABSTRACT FROM AUTHOR]

A thoroughunderstanding of the three-dimensional (3D) atomic structureand composition of core–shell nanostructures is indispensableto obtain a deeper insight on their physical behavior. Such 3D informationcan be reconstructed from two-dimensional (2D) projection images usingelectron tomography. Recently, different electron tomography techniqueshave enabled the 3D characterization of a variety of nanostructuresdown to the atomic level. However, these methods have all focusedon the investigation of nanomaterials containing only one type ofchemical element. Here, we combine statistical parameter estimationtheory with compressive sensing based tomography to determine thepositions and atom type of each atom in heteronanostructures. Theapproach is applied here to investigate the interface in core–shellAu@Ag nanorods but it is of great interest in the investigation ofa broad range of nanostructures. [ABSTRACT FROM AUTHOR]

We propose a new method to measure atomic scale dynamics of nanoparticles from experimental high-resolution annular dark field scanning transmission electron microscopy images. By using the so-called hidden Markov model, which explicitly models the possibility of structural changes, the number of atoms in each atomic column can be quantified over time. This newly proposed method outperforms the current atom-counting procedure and enables the determination of the probabilities and cross sections for surface diffusion. This method is therefore of great importance for revealing and quantifying the atomic structure when it evolves over time via adatom dynamics, surface diffusion, beam effects, or during in situ experiments. [ABSTRACT FROM AUTHOR]

Understanding nanostructures down to the atomic level is the key to optimizing the design of advanced materials with revolutionary novel properties. This requires characterization methods capable of quantifying the three-dimensional (3D) atomic structure with the highest possible precision. A successful approach to reach this goal is to count the number of atoms in each atomic column from 2D annular dark field scanning transmission electron microscopy images. To count atoms with single atom sensitivity, a minimum electron dose has been shown to be necessary, while on the other hand beam damage, induced by the high energy electrons, puts a limit on the tolerable dose. An important challenge is therefore to develop experimental strategies to optimize the electron dose by balancing atom-counting fidelity vs the risk of knock-on damage. To achieve this goal, a statistical framework combined with physics-based modeling of the dose-dependent processes is here proposed and experimentally verified. This model enables an investigator to theoretically predict, in advance of an experimental measurement, the optimal electron dose resulting in an unambiguous quantification of nanostructures in their native state with the highest attainable precision. [ABSTRACT FROM AUTHOR]