Yichi_Wang

Electron energy-loss spectroscopy (EELS) has become a standard tool for identification and sometimes also quantification of elements in materials science. This is important for understanding the chemical and/or structural composition of processed materials. In EELS, the background is often modelled using an inverse power-law function. Core-loss ionization edges are superimposed on top of the dominating background, making it difficult to quantify their intensities. The inverse power-law has to be modelled for each pre-edge region of the ionization edges in the spectrum individually rather than for the entire spectrum. To achieve this, the prerequisite is that one knows all core losses possibly present. The aim of this study is to automatically detect core-loss edges, model the background and extract quantitative elemental maps and profiles of EELS, based on several EELS spectrum images (EELS SI) without any prior knowledge of the material. The algorithm provides elemental maps and concentration profiles by making smart decisions in selecting pre-edge regions and integration ranges. The results of the quantification for a semiconductor thin film heterostructure show high chemical sensitivity, reasonable group III/V intensity ratios but also quantification issues when narrow integration windows are used without deconvolution.

Electron energy-loss spectroscopy (EELS) has become a standard tool for identification and sometimes also quantification of elements in materials science. This is important for understanding the chemical and/or structural composition of processed materials. In EELS, the background is often intense and can be modelled over small energy ranges using an inverse power-law function. On top of this background, core-loss edges are superimposed that are due to the ionization energies characteristic of each element. This study describes a Matlab algorithm to automatically detect and quantify core-loss edges based on a single inelastic scattering approach, without any prior knowledge of the material. The algorithm provides elemental maps and concentration profiles by making smart decisions in selecting preedge regions and integration ranges. Deconvolution to take into account plural scattering is not considered yet but will be integrated in a future version.

Characterization of core–shell type nanoparticles in 3D by transmission electron microscopy (TEM) can be very challenging. Especially when both heavy and light elements coexist within the same nanostructure, artifacts in the 3D reconstruction are often present. A representative example would be a particle comprising an anisotropic metallic (Au) nanoparticle coated with a (mesoporous) silica shell. To obtain a reliable 3D characterization of such an object, a dose-efficient strategy is proposed to simultaneously acquire high-angle annular dark-field scanning TEM and annular dark-field tilt series for tomography. The 3D reconstruction is further improved by applying an advanced masking and interpolation approach to the acquired data. This new methodology enables us to obtain high-quality reconstructions from which also quantitative information can be extracted. This approach is broadly applicable to investigate hybrid core–shell materials.

Au nanoparticles covered by mesoporous silica shells are quantitatively characterized by advanced electron tomography using a dose-efficient strategy. The 3D reconstructions are further improved by applying a special masking and interpolation approach to the acquired data. This approach is broadly applicable to investigate hybrid core–shell materials.

In the present paper, we describe new robust methods of estimating cell shape and orientation in 3D from sections. The descriptors of 3D cell shape and orientation are based on volume tensors which are used to construct an ellipsoid, the Miles ellipsoid, approximating the average cell shape and orientation in 3D. The estimators of volume tensors are based on observations in several optical planes through sampled cells. This type of geometric sampling design is known as the optical rotator. The statistical behaviour of the estimator of the Miles ellipsoid is studied under a flexible model for 3D cell shape and orientation. In a simulation study, the lengths of the axes of the Miles ellipsoid can be estimated with coefficients of variation of about 2% if 100 cells are sampled. Finally, we illustrate the use of the developed methods in an example, involving neurons in the medial prefrontal cortex of rat.

A concise description of shape and orientation of certain types of cells is a challenging problem. Shape descriptors should be sophisticated enough to discriminate between cell populations that are visibly different. However, they should be simple enough to be interpretable and one has to be able to infer them from data. We propose a set of such shape descriptors and show that they have good statistical properties. The data are collected on optical planes through the cells, that is, we require only partial knowledge of the boundary of the cell in order to estimate the shape characteristics.