|
“Hidden Markov model for atom-counting from sequential ADF STEM images: Methodology, possibilities and limitations”. De wael A, De Backer A, Van Aert S, Ultramicroscopy 219, 113131 (2020). http://doi.org/10.1016/j.ultramic.2020.113131
Abstract: We present a quantitative method which allows us to reliably measure dynamic changes in the atomic structure of monatomic crystalline nanomaterials from a time series of atomic resolution annular dark field scanning transmission electron microscopy images. The approach is based on the so-called hidden Markov model and estimates the number of atoms in each atomic column of the nanomaterial in each frame of the time series. We discuss the origin of the improved performance for time series atom-counting as compared to the current state-of-the-art atom-counting procedures, and show that the so-called transition probabilities that describe the probability for an atomic column to lose or gain one or more atoms from frame to frame are particularly important. Using these transition probabilities, we show that the method can also be used to estimate the probability and cross section related to structural changes. Furthermore, we explore the possibilities for applying the method to time series recorded under variable environmental conditions. The method is shown to be promising for a reliable quantitative analysis of dynamic processes such as surface diffusion, adatom dynamics, beam effects, or in situ experiments.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2020.113131
|
|
|
“Atom column detection”. Fatermans J, de Backer A, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 177 (2021).
Abstract: By combining statistical parameter estimation and model-order selection using a Bayesian framework, the maximum a posteriori (MAP) probability rule is proposed in this chapter as an objective and quantitative method to detect atom columns from high-resolution scanning transmission electron microscopy (HRSTEM) images. The validity and usefulness of this approach is demonstrated to both simulated and experimental annular dark-field (ADF) STEM images, but also to simultaneously acquired annular bright-field (ABF) and ADF STEM image data.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1016/BS.AIEP.2021.01.006
|
|
|
“Atom counting”. de Backer A, Fatermans J, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 91 (2021).
Abstract: In this chapter, a statistical model-based method to count the number of atoms of monotype crystalline nanostructures from high-resolution annular dark-field (ADF) scanning transmission electron microscopy (STEM) images is discussed in detail together with a thorough study on the possibilities and inherent limitations. We show that this method can be applied to nanocrystals of arbitrary shape, size, and atom type. The validity of the atom-counting results is confirmed by means of detailed image simulations and it is shown that the high sensitivity of our method enables us to count atoms with single atom sensitivity.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1016/BS.AIEP.2021.01.004
|
|
|
“Efficient fitting algorithm”. de Backer A, Fatermans J, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 73 (2021).
Abstract: An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic-resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighboring columns, enabling the analysis of a large field of view. To provide end-users with this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. In this chapter, this efficient algorithm is applied to three different nanostructures for which the analysis of a large field of view is required.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT)
DOI: 10.1016/BS.AIEP.2021.01.003
|
|
|
“General conclusions and future perspectives”. de Backer A, Fatermans J, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 243 (2021).
Abstract: This chapter provides an overview of statistical and quantitative methodologies that have pushed (scanning) transmission electron microscopy ((S)TEM) toward accurate and precise measurements of unknown structure parameters for understanding the relation between the structure of a material and its properties. Hereby, statistical parameter estimation theory has extensively been used which enabled not only measuring atomic column positions, but also quantifying the number of atoms, and detecting atomic columns as accurately and precisely as possible from experimental images. As a general conclusion, it can be stated that advanced statistical techniques are ideal tools to perform quantitative electron microscopy at the atomic scale. In the future, statistical methods will continue to be developed and novel quantification procedures will open up new possibilities for studying material structures at the atomic scale.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1016/BS.AIEP.2021.01.008
|
|
|
“Image-quality evaluation and model selection with maximum a posteriori probability”. Fatermans J, de Backer A, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 215 (2021).
Abstract: The maximum a posteriori (MAP) probability rule for atom column detection can also be used as a tool to evaluate the relation between scanning transmission electron microscopy (STEM) image quality and atom detectability. In this chapter, a new image-quality measure is proposed that correlates well with atom detectability, namely the integrated contrast-to-noise ratio (ICNR). Furthermore, the working principle of the MAP probability rule is described in detail showing a close relation to the principles of model-selection methods.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1016/BS.AIEP.2021.01.007
|
|
|
“Introduction”. de Backer A, Fatermans J, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 1 (2021).
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT)
DOI: 10.1016/BS.AIEP.2021.01.001
|
|
|
“Optimal experiment design for nanoparticle atom counting from ADF STEM images”. de Backer A, Fatermans J, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 145 (2021).
Abstract: In this chapter, the principles of detection theory are used to quantify the probability of error for atom counting from high-resolution scanning transmission electron microscopy (HRSTEM) images. Binary and multiple hypothesis testing have been investigated in order to determine the limits to the precision with which the number of atoms in a projected atomic column can be estimated. The probability of error has been calculated when using STEM images, scattering cross-sections or peak intensities as a criterion to count atoms. Based on this analysis, we conclude that scattering cross-sections perform almost equally well as images and perform better than peak intensities. Furthermore, the optimal STEM detector design can be derived for atom counting using the expression of the probability of error. We show that for very thin objects the low-angle annular dark-field (LAADF) regime is optimal and that for thicker objects the optimal inner detector angle increases.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1016/BS.AIEP.2021.01.005
|
|
|
“Statistical parameter estimation theory : principles and simulation studies”. de Backer A, Fatermans J, den Dekker AJ, Van Aert S Advances in imaging and electron physics
T2 – Advances in imaging and electron physics. page 29 (2021).
Abstract: In this chapter, the principles of statistical parameter estimation theory for a quantitative analysis of atomic-resolution electron microscopy images are introduced. Within this framework, electron microscopy images are described by a parametric statistical model. Here, parametric models are introduced for different types of electron microscopy images: reconstructed exit waves, annular dark-field (ADF) scanning transmission electron microscopy (STEM) images, and simultaneously acquired ADF and annular bright-field (ABF) STEM images. Furthermore, the Cramér-Rao lower bound (CRLB) is introduced, i.e. a theoretical lower bound on the variance of any unbiased estimator. This CRLB is used to quantify the precision of the structure parameters of interest, such as the atomic column positions and the integrated atomic column intensities.
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1016/BS.AIEP.2021.01.002
|
|
|
“Modelling ADF STEM images using elliptical Gaussian peaks and its effects on the quantification of structure parameters in the presence of sample tilt”. De wael A, De Backer A, Lobato I, Van Aert S, Ultramicroscopy , 113391 (2021). http://doi.org/10.1016/j.ultramic.2021.113391
Abstract: A small sample tilt away from a main zone axis orientation results in an elongation of the atomic columns in ADF STEM images. An often posed research question is therefore whether the ADF STEM image intensities of tilted nanomaterials should be quantified using a parametric imaging model consisting of elliptical rather than the currently used symmetrical peaks. To this purpose, simulated ADF STEM images corresponding to different amounts of sample tilt are studied using a parametric imaging model that consists of superimposed 2D elliptical Gaussian peaks on the one hand and symmetrical Gaussian peaks on the other hand. We investigate the quantification of structural parameters such as atomic column positions and scattering cross sections using both parametric imaging models. In this manner, we quantitatively study what can be gained from this elliptical model for quantitative ADF STEM, despite the increased parameter space and computational effort. Although a qualitative improvement can be achieved, no significant quantitative improvement in the estimated structure parameters is achieved by the elliptical model as compared to the symmetrical model. The decrease in scattering cross sections with increasing sample tilt is even identical for both types of parametric imaging models. This impedes direct comparison with zone axis image simulations. Nonetheless, we demonstrate how reliable atom-counting can still be achieved in the presence of small sample tilt.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.843
DOI: 10.1016/j.ultramic.2021.113391
|
|
|
“Dynamical diffraction of high-energy electrons investigated by focal series momentum-resolved scanning transmission electron microscopy at atomic resolution”. Robert Hl, Lobato I, Lyu Fj, Chen Q, Van Aert S, Van Dyck D, Müller-Caspary K, Ultramicroscopy 233, 113425 (2022). http://doi.org/10.1016/j.ultramic.2021.113425
Abstract: We report a study of scattering dynamics in crystals employing momentum-resolved scanning transmission
electron microscopy under varying illumination conditions. As we perform successive changes of the probe
focus, multiple real-space signals are obtained in dependence of the shape of the incident electron wave.
With support from extensive simulations, each signal is shown to be characterised by an optimum focus for
which the contrast is maximum and which differs among different signals. For instance, a systematic focus
mismatch is found between images formed by high-angle scattering, being sensitive to thickness and chemical
composition, and the first moment in diffraction space, being sensitive to electric fields. It follows that a single
recording at one specific probe focus is usually insufficient to characterise materials comprehensively. Most
importantly, we demonstrate in experiment and simulation that the second moment (
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2021.113425
|
|
|
“Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm”. De Backer A, Van Aert S, Faes C, Arslan Irmak E, Nellist PD, Jones L, N P J Computational Materials 8, 216 (2022). http://doi.org/10.1038/s41524-022-00900-w
Abstract: 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.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
DOI: 10.1038/s41524-022-00900-w
|
|
|
“Three Approaches for Representing the Statistical Uncertainty on Atom-Counting Results in Quantitative ADF STEM”. De wael A, De Backer A, Yu C-P, Sentürk DG, Lobato I, Faes C, Van Aert S, Microscopy and microanalysis , 1 (2022). http://doi.org/10.1017/S1431927622012284
Abstract: A decade ago, a statistics-based method was introduced to count the number of atoms from annular dark-field scanning transmission electron microscopy (ADF STEM) images. In the past years, this method was successfully applied to nanocrystals of arbitrary shape, size, and composition (and its high accuracy and precision has been demonstrated). However, the counting results obtained from this statistical framework are so far presented without a visualization of the actual uncertainty about this estimate. In this paper, we present three approaches that can be used to represent counting results together with their statistical error, and discuss which approach is most suited for further use based on simulations and an experimental ADF STEM image.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.8
DOI: 10.1017/S1431927622012284
|
|
|
“Optimal experiment design for element specific atom counting using multiple annular dark field scanning transmission electron microscopy detectors”. Sentürk DG, De Backer A, Friedrich T, Van Aert S, Ultramicroscopy 242, 113626 (2022). http://doi.org/10.1016/j.ultramic.2022.113626
Abstract: This paper investigates the possible benefits for counting atoms of different chemical nature when analysing multiple 2D scanning transmission electron microscopy (STEM) images resulting from independent annular dark field (ADF) detector regimes. To reach this goal, the principles of statistical detection theory are used to quantify the probability of error when determining the number of atoms in atomic columns consisting of multiple types of elements. In order to apply this theory, atom-counting is formulated as a statistical hypothesis test, where each hypothesis corresponds to a specific number of atoms of each atom type in an atomic column. The probability of error, which is limited by the unavoidable presence of electron counting noise, can then be computed from scattering-cross sections extracted from multiple ADF STEM images. Minimisation of the probability of error as a function of the inner and outer angles of a specified number of independent ADF collection regimes results in optimal experimental designs. Based on simulations of spherical Au@Ag and Au@Pt core–shell nanoparticles, we investigate how the combination of two non-overlapping detector regimes helps to improve the probability of error when unscrambling two types of atoms. In particular, the combination of a narrow low angle ADF detector with a detector formed by the remaining annular collection regime is found to be optimal. The benefit is more significant if the atomic number Z difference becomes larger. In
addition, we show the benefit of subdividing the detector regime into three collection areas for heterogeneous nanostructures based on a structure consisting of three types of elements, e.g., a mixture of Au, Ag and Al atoms. Finally, these results are compared with the probability of error resulting when one would ultimately use a pixelated 4D STEM detector and how this could help to further reduce the incident electron dose.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2022.113626
|
|
|
“Fast generation of calculated ADF-EDX scattering cross-sections under channelling conditions”. Zhang Z, Lobato I, De Backer A, Van Aert S, Nellist P, Ultramicroscopy 246, 113671 (2023). http://doi.org/10.1016/j.ultramic.2022.113671
Abstract: Advanced materials often consist of multiple elements which are arranged in a complicated structure. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core–shell nanoparticle and a high entropy alloy. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2022.113671
|
|
|
“Unusual structural rearrangement and superconductivity in infinite layer cuprate superlattices”. Samal D, Gauquelin N, Takamura Y, Lobato I, Arenholz E, Van Aert S, Huijben M, Zhong Z, Verbeeck J, Van Tendeloo G, Koster G, Physical review materials 7, 054803 (2023). http://doi.org/10.1103/PhysRevMaterials.7.054803
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 3.4
DOI: 10.1103/PhysRevMaterials.7.054803
|
|
|
“Real-time simulations of ADF STEM probe position-integrated scattering cross-sections for single element fcc crystals in zone axis orientation using a densely connected neural network”. Lobato I, De Backer A, Van Aert S, Ultramicroscopy 251, 113769 (2023). http://doi.org/10.1016/j.ultramic.2023.113769
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms
of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In
order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for
a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such
simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently
parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most
research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will
only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and
present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as
a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag,
Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope
parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS
values for a wide range of input parameters that are commonly used for aberration-corrected transmission
electron microscopes.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2023.113769
|
|
|
“Deep convolutional neural networks to restore single-shot electron microscopy images”. Lobato I, Friedrich T, Van Aert S, npj Computational Materials 10, 10 (2024). http://doi.org/10.1038/s41524-023-01188-0
Abstract: Advanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology.
Keywords: A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ;
DOI: 10.1038/s41524-023-01188-0
|
|
|
“Additivity of Atomic Strain Fields as a Tool to Strain-Engineering Phase-Stabilized CsPbI3Perovskites”. Teunissen JL, Braeckevelt T, Skvortsova I, Guo J, Pradhan B, Debroye E, Roeffaers MBJ, Hofkens J, Van Aert S, Bals S, Rogge SMJ, Van Speybroeck V, The Journal of Physical Chemistry C 127, 23400 (2023). http://doi.org/10.1021/acs.jpcc.3c05770
Abstract: CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability.
Keywords: A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ;
Impact Factor: 3.7
DOI: 10.1021/acs.jpcc.3c05770
|
|
|
“Atom counting from a combination of two ADF STEM images”. Şentürk DG, Yu CP, De Backer A, Van Aert S, Ultramicroscopy 255, 113859 (2024). http://doi.org/10.1016/j.ultramic.2023.113859
Abstract: To understand the structure–property relationship of nanostructures, reliably quantifying parameters, such as the number of atoms along the projection direction, is important. Advanced statistical methodologies have made it possible to count the number of atoms for monotype crystalline nanoparticles from a single ADF STEM image. Recent developments enable one to simultaneously acquire multiple ADF STEM images. Here, we present an extended statistics-based method for atom counting from a combination of multiple statistically independent ADF STEM images reconstructed from non-overlapping annular detector collection regions which improves the accuracy and allows one to retrieve precise atom-counts, especially for images acquired with low electron doses and multiple element structures.
Keywords: A1 Journal Article; Atomic resolution scanning transmission electron microscopy; Atom counting; Heterogeneous nanostructures; Multivariate Gaussian mixture model; 4D STEM; Electron Microscopy for Materials Science (EMAT) ;
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2023.113859
|
|
|
“Conformation-Dependent Monolayer and Bilayer Structures of an Alkylated TTF Derivative Revealed using STM and Molecular Modeling”. Delfino CL, Hao Y, Martin C, Minoia A, Gopi E, Mali KS, Van der Auweraer M, Geerts YH, Van Aert S, Lazzaroni R, De Feyter S, The Journal of Physical Chemistry C 127, 23023 (2023). http://doi.org/10.1021/acs.jpcc.3c04913
Abstract: In this study, the multi-layer self-assembled molecular network formation of an alkylated tetrathiafulvalene compound is studied at the liquid-solid interface between 1-phenyloctane and graphite. A combined theoretical/experimental approach associating force-field and quantum-chemical calculations with scanning tunnelling microscopy is used to determine the two-dimensional self-assembly beyond the monolayer, but also to further the understanding of the molecular adsorption conformation and its impact on the molecular packing within the assemblies at the monolayer and bilayer level.
Keywords: A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ;
Impact Factor: 3.7
DOI: 10.1021/acs.jpcc.3c04913
|
|
|
“In Situ Plasma Studies Using a Direct Current Microplasma in a Scanning Electron Microscope”. Grünewald L, Chezganov D, De Meyer R, Orekhov A, Van Aert S, Bogaerts A, Bals S, Verbeeck J, Advanced Materials Technologies (2024). http://doi.org/10.1002/admt.202301632
Abstract: Microplasmas can be used for a wide range of technological applications and to improve the understanding of fundamental physics. Scanning electron microscopy, on the other hand, provides insights into the sample morphology and chemistry of materials from the mm‐ down to the nm‐scale. Combining both would provide direct insight into plasma‐sample interactions in real‐time and at high spatial resolution. Up till now, very few attempts in this direction have been made, and significant challenges remain. This work presents a stable direct current glow discharge microplasma setup built inside a scanning electron microscope. The experimental setup is capable of real‐time in situ imaging of the sample evolution during plasma operation and it demonstrates localized sputtering and sample oxidation. Further, the experimental parameters such as varying gas mixtures, electrode polarity, and field strength are explored and experimental<italic>V</italic>–<italic>I</italic>curves under various conditions are provided. These results demonstrate the capabilities of this setup in potential investigations of plasma physics, plasma‐surface interactions, and materials science and its practical applications. The presented setup shows the potential to have several technological applications, for example, to locally modify the sample surface (e.g., local oxidation and ion implantation for nanotechnology applications) on the µm‐scale.
Keywords: A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ;
Impact Factor: 6.8
DOI: 10.1002/admt.202301632
|
|
|
“Element specific atom counting for heterogeneous nanostructures: Combining multiple ADF STEM images for simultaneous thickness and composition determination”. Şentürk DG, De Backer A, Van Aert S, Ultramicroscopy 259, 113941 (2024). http://doi.org/10.1016/j.ultramic.2024.113941
Abstract: In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core-shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.
Keywords: A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ;
Impact Factor: 2.2
DOI: 10.1016/j.ultramic.2024.113941
|
|
|
“Low-dose 4D-STEM tomography for beam-sensitive nanocomposites”. Hugenschmidt M, Jannis D, Kadu AA, Grünewald L, De Marchi S, Perez-Juste J, Verbeeck J, Van Aert S, Bals S, ACS materials letters 6, 165 (2023). http://doi.org/10.1021/ACSMATERIALSLETT.3C01042
Abstract: Electron tomography is essential for investigating the three-dimensional (3D) structure of nanomaterials. However, many of these materials, such as metal-organic frameworks (MOFs), are extremely sensitive to electron radiation, making it difficult to acquire a series of projection images for electron tomography without inducing electron-beam damage. Another significant challenge is the high contrast in high-angle annular dark field scanning transmission electron microscopy that can be expected for nanocomposites composed of a metal nanoparticle and an MOF. This strong contrast leads to so-called metal artifacts in the 3D reconstruction. To overcome these limitations, we here present low-dose electron tomography based on four-dimensional scanning transmission electron microscopy (4D-STEM) data sets, collected using an ultrafast and highly sensitive direct electron detector. As a proof of concept, we demonstrate the applicability of the method for an Au nanostar embedded in a ZIF-8 MOF, which is of great interest for applications in various fields, including drug delivery.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
DOI: 10.1021/ACSMATERIALSLETT.3C01042
|
|
|
“Atomic resolution mapping using quantitative high-angle annular dark field scanning transmission electron microscopy”. Van Aert S, Verbeeck J, Bals S, Erni R, van Dyck D, Van Tendeloo G, Microscopy and microanalysis 15, 464 (2009). http://doi.org/10.1017/S1431927609093957
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 1.891
Times cited: 1
DOI: 10.1017/S1431927609093957
|
|
|
“Introduction to a special issue in honour of W. Owen Saxton, David J. Smith and Dirk Van Dyck on the occasion of their 65th birthdays”. Dunin-Borkowski RE, Lichte H, Tillmann K, Van Aert S, Van Tendeloo G, Ultramicroscopy 134, 1 (2013). http://doi.org/10.1016/j.ultramic.2013.07.013
Keywords: Editorial; Electron microscopy for materials research (EMAT)
Impact Factor: 2.843
Times cited: 1
DOI: 10.1016/j.ultramic.2013.07.013
|
|
|
“StatSTEM: An efficient program for accurate and precise model-based quantification of atomic resolution electron microscopy images”. De Backer A, van den Bos KHW, Van den Broek W, Sijbers J, Van Aert S, Journal of physics : conference series
T2 –, Electron Microscopy and Analysis Group Conference 2017 (EMAG2017), 3-6 July 2017, Manchester, UK 902, 012013 (2017). http://doi.org/10.1088/1742-6596/902/1/012013
Abstract: An efficient model-based estimation algorithm is introduced in order to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for the overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, is investigated. The highest attainable precision is reached even for low dose images. Furthermore, advantages of the model- based approach taking into account overlap between neighbouring columns are highlighted. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license.
Keywords: P1 Proceeding; Electron microscopy for materials research (EMAT); Vision lab
Times cited: 1
DOI: 10.1088/1742-6596/902/1/012013
|
|
|
“Quantification by aberration corrected (S)TEM of boundaries formed by symmetry breaking phase transformations”. Schryvers D, Salje EKH, Nishida M, De Backer A, Idrissi H, Van Aert S, Ultramicroscopy 176, 194 (2017). http://doi.org/10.1016/j.ultramic.2016.12.022
Abstract: The present contribution gives a review of recent quantification work of atom displacements, atom site occupations and level of crystallinity in various systems and based on aberration corrected HR(S)TEM images. Depending on the case studied, picometer range precisions for individual distances can be obtained, boundary widths at the unit cell level determined or statistical evolutions of fractions of the ordered areas calculated. In all of these cases, these quantitative measures imply new routes for the applications of the respective materials.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.843
Times cited: 1
DOI: 10.1016/j.ultramic.2016.12.022
|
|
|
“The maximum a posteriori probability rule for atom column detection from HAADF STEM images”. Fatermans J, Van Aert S, den Dekker AJ, Ultramicroscopy 201, 81 (2019). http://doi.org/10.1016/j.ultramic.2019.02.003
Abstract: Recently, the maximum a posteriori (MAP) probability rule has been proposed as an objective and quantitative method to detect atom columns and even single atoms from high-resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images. The method combines statistical parameter estimation and model-order selection using a Bayesian framework and has been shown to be especially useful for the analysis of the structure of beam-sensitive nanomaterials. In order to avoid beam damage, images of such materials are usually acquired using a limited incoming electron dose resulting in a low contrast-to-noise ratio (CNR) which makes visual inspection unreliable. This creates a need for an objective and quantitative approach. The present paper describes the methodology of the MAP probability rule, gives its step-by-step derivation and discusses its algorithmic implementation for atom column detection. In addition, simulation results are presented showing that the performance of the MAP probability rule to detect the correct number of atomic columns from HAADF STEM images is superior to that of other model-order selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Moreover, the MAP probability rule is used as a tool to evaluate the relation between STEM image quality measures and atom detectability resulting in the introduction of the so-called integrated CNR (ICNR) as a new image quality measure that better correlates with atom detectability than conventional measures such as signal-to-noise ratio (SNR) and CNR.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 2.843
Times cited: 1
DOI: 10.1016/j.ultramic.2019.02.003
|
|
|
“Self‐Assembly of Atomically Thin Chiral Copper Heterostructures Templated by Black Phosphorus”. Nerl HC, Pokle A, Jones L, Müller‐Caspary K, Bos KHW, Downing C, McCarthy EK, Gauquelin N, Ramasse QM, Lobato I, Daly D, Idrobo JC, Van Aert S, Van Tendeloo G, Sanvito S, Coleman JN, Cucinotta CS, Nicolosi V, Advanced functional materials 29, 1903120 (2019). http://doi.org/10.1002/adfm.201903120
Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
Impact Factor: 12.124
Times cited: 1
DOI: 10.1002/adfm.201903120
|
|