Abstract: Electron tomography is a powerful technique for the 3D characterization of the morphology of nanostructures. Nevertheless, resolving the chemical composition of complex nanostructures in 3D remains challenging and the number of studies in which electron energy loss spectroscopy (EELS) is combined with tomography is limited. During the last decade, dedicated reconstruction algorithms have been developed for HAADF-STEM tomography using prior knowledge about the investigated sample. Here, we will use the prior knowledge that the experimental spectrum of each reconstructed voxel is a linear combination of a well-known set of references spectra in a so-called direct spectroscopic tomography technique. Based on a simulation experiment, it is shown that this technique provides superior results in comparison to conventional reconstruction methods for spectroscopic data, especially for spectrum images containing a relatively low signal to noise ratio. Next, this technique is used to investigate the spatial distribution of Fe dopants in Fe:Ceria nanoparticles in 3D. It is shown that the presence of the Fe2+ dopants is correlated with a reduction of the Ce atoms from Ce4+ towards Ce3+. In addition, it is demonstrated that most of the Fe dopants are located near the voids inside the nanoparticle.

Abstract: Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new
iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the
proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials.