K. Joost Batenburg

Three-dimensional atomic imaging of colloidal core-shell nanocrystals

Colloidal core-shell semiconductor nanocrystals form an important class of optoelectronic materials, in which the exciton wave functions can be tailored by the atomic configuration of the core, the interfacial layers, and the shell. Here, we provide a trustful 3D characterization at the atomic scale of a free-standing PbSe(core)-CdSe(shell) nanocrystal by combining electron microscopy and discrete tomography. Our results yield unique insights for understanding the process of cation exchange, which is widely employed in the synthesis of core-shell nanocrystals. The study that we present is generally applicable to the broad range of colloidal heteronanocrystals that currently emerge as a new class of materials with technological importance.

Quantitative three-dimensional modeling of zeotile through discrete electron tomography.

Discrete electron tomography is a new approach for three-dimensional reconstruction of nanoscale objects. The technique exploits prior knowledge of the object to be reconstructed, which results in an improvement of the quality of the reconstructions. Through the combination of conventional transmission electron microscopy and discrete electron tomography with a model-based approach, quantitative structure determination becomes possible. In the present work, this approach is used to unravel the building scheme of Zeotile-4, a silica material with two levels of structural order. The layer sequence of slab-shaped building units could be identified. Successive layers were found to be related by a rotation of 120 degrees, resulting in a hexagonal space group. The Zeotile-4 material is a demonstration of the concept of successive structuring of silica at two levels. At the first level, the colloid chemical properties of Silicalite-1 precursors are exploited to create building units with a slablike geometry. At the second level, the slablike units are tiled using a triblock copolymer to serve as a mesoscale structuring agent.

Measuring Lattice Strain in Three Dimensions through Electron Microscopy

The three-dimensional (3D) atomic structure of nanomaterials, including strain, is crucial to understand their properties. Here, we investigate lattice strain in Au nanodecahedra using electron tomography. Although different electron tomography techniques enabled 3D characterizations of nanostructures at the atomic level, a reliable determination of lattice strain is not straightforward. We therefore propose a novel model-based approach from which atomic coordinates are measured. Our findings demonstrate the importance of investigating lattice strain in 3D.

A bimodal tomographic reconstruction technique combining EDS-STEM and HAADF-STEM

A three-dimensional (3D) chemical characterization of nanomaterials can be obtained using tomography based on high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) or energy dispersive X-ray spectroscopy (EDS) STEM. These two complementary techniques have both advantages and disadvantages. The Z-contrast images have good image quality but lack robustness in the compositional analysis, while the elemental maps give more element-specific information, but at a low signal-to-noise ratio and a longer exposure time. Our aim is to combine these two types of complementary information in one single tomographic reconstruction process. Therefore, an imaging model is proposed combining both HAADF-STEM and EDS-STEM. Based on this model, the elemental distributions can be reconstructed using both types of information simultaneously during the reconstruction process. The performance of the new technique is evaluated using simulated data and real experimental data. The results demonstrate that combining two imaging modalities leads to tomographic reconstructions with suppressed noise and enhanced contrast.

Three dimensional mapping of Fe dopants in ceria nanocrystals using direct spectroscopic electron tomography.

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.

Real-Time Atomic Scale Imaging of Nanostructural Evolution in Aluminum Alloys

We present a new approach to study the three-dimensional compositional and structural evolution of metal alloys during heat treatments such as commonly used for improving overall material properties. It relies on in situ heating in a high-resolution scanning transmission electron microscope (STEM). The approach is demonstrated using a commercial Al alloy AA2024 at 100-240 °C, showing in unparalleled detail where and how precipitates nucleate, grow, or dissolve. The observed size evolution of individual precipitates enables a separation between nucleation and growth phenomena, necessary for the development of refined growth models. We conclude that the in situ heating STEM approach opens a route to a much faster determination of the interplay between local compositions, heat treatments, microstructure, and mechanical properties of new alloys.

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

We introduce the reconstructed residual error, a new segmentation evaluation measure.The method provides a spatial map of the errors in a segmented image in tomography.The original projection images are exploited in an unsupervised approach.Validation is performed through simulations and experimental micro-CT data.The method can improve gray level estimates and discriminate between segmentations. In this paper, we present the reconstructed residual error, which evaluates the quality of a given segmentation of a reconstructed image in tomography. This novel evaluation method, which is independent of the methods that were used to reconstruct and segment the image, is applicable to segmentations that are based on the density of the scanned object. It provides a spatial map of the errors in the segmented image, based on the projection data. The reconstructed residual error is a reconstruction of the difference between the recorded data and the forward projection of that segmented image. The properties and applications of the algorithm are verified experimentally through simulations and experimental micro-CT data. The experiments show that the reconstructed residual error is close to the true error, that it can improve gray level estimates, and that it can help discriminating between different segmentations.

A distributed ASTRA toolbox

AbstractnWhile iterative reconstruction algorithms for tomography have several advantages compared to standard backprojection methods, the adoption of such algorithms in large-scale imaging facilities is still limited, one of the key obstacles being their high computational load. Although GPU-enabled computing clusters are, in principle, powerful enough to carry out iterative reconstructions on large datasets in reasonable time, creating efficient distributed algorithms has so far remained a complex task, requiring low-level programming to deal with memory management and network communication. The ASTRA toolbox is a software toolbox that enables rapid development of GPU accelerated tomography algorithms. It contains GPU implementations of forward and backprojection operations for many scanning geometries, as well as a set of algorithms for iterative reconstruction. These algorithms are currently limited to using GPUs in a single workstation. In this paper, we present an extension of the ASTRA toolbox and its Python interface with implementations of forward projection, backprojection and the SIRT algorithm that can be distributed over multiple GPUs and multiple workstations, as well as the tools to write distributed versions of custom reconstruction algorithms, to make processing larger datasets with ASTRA feasible. As a result, algorithms that are implemented in a high-level conceptual script can run seamlessly on GPU-enabled computing clusters, up to 32 GPUs or more. Our approach is not limited to slice-based reconstruction, facilitating a direct portability of algorithms coded for parallel-beam synchrotron tomography to cone-beam laboratory tomography setups without making changes to the reconstruction algorithm.

Automatic correction of nonlinear damping effects in HAADF–STEM tomography for nanomaterials of discrete compositions

HAADF-STEM tomography is a common technique for characterizing the three-dimensional morphology of nanomaterials. In conventional tomographic reconstruction algorithms, the image intensity is assumed to be a linear projection of a physical property of the specimen. However, this assumption of linearity is not completely valid due to the nonlinear damping of signal intensities. The nonlinear damping effects increase w.r.t the specimen thickness and lead to so-called cupping artifacts, due to a mismatch with the linear model used in the reconstruction algorithm. Moreover, nonlinear damping effects can strongly limit the applicability of advanced reconstruction approaches such as Total Variation Minimization and discrete tomography. In this paper, we propose an algorithm for automatically correcting the nonlinear effects and the subsequent cupping artifacts. It is applicable to samples in which chemical compositions can be segmented based on image gray levels. The correction is realized by iteratively estimating the nonlinear relationship between projection intensity and sample thickness, based on which the projections are linearized. The correction and reconstruction algorithms are tested on simulated and experimental data.

EDS tomographic reconstruction regularized by total nuclear variation joined with HAADF-STEM tomography

Energy-dispersive X-ray spectroscopy (EDS) tomography is an advanced technique to characterize compositional information for nanostructures in three dimensions (3D). However, the application is hindered by the poor image quality caused by the low signal-to-noise ratios and the limited number of tilts, which are fundamentally limited by the insufficient number of X-ray counts. In this paper, we explore how to make accurate EDS reconstructions from such data. We propose to augment EDS tomography by joining with it a more accurate high-angle annular dark-field STEM (HAADF-STEM) tomographic reconstruction, for which usually a larger number of tilt images are feasible. This augmentation is realized through total nuclear variation (TNV) regularization, which encourages the joint EDS and HAADF reconstructions to have not only sparse gradients but also common edges and parallel (or antiparallel) gradients. Our experiments show that reconstruction images are more accurate compared to the non-regularized and the total variation regularized reconstructions, even when the number of tilts is small or the X-ray counts are low.