|
“Phase retrieval from 4-dimensional electron diffraction datasets”. Friedrich T, Yu C-P, Verbeek J, Pennycook T, Van Aert S, Proceedings
T2 –, IEEE International Conference on Image Processing (ICIP), SEP 19-22, 2021, Electr. network , 3453 (2021). http://doi.org/10.1109/ICIP42928.2021.9506709
Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.
Keywords: P1 Proceeding; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
DOI: 10.1109/ICIP42928.2021.9506709
|
|
|
“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
|
|
|
“Investigating lattice strain in Au nanodecahedrons”. Goris B, De Beenhouwer J, de Backer A, Zanaga D, Batenburg J, Sanchez-Iglesias A, Liz-Marzan L, Van Aert S, Sijbers J, Van Tendeloo G, Bals S, , 11 (2016). http://doi.org/10.1002/9783527808465.EMC2016.5519
Keywords: P1 Proceeding; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1002/9783527808465.EMC2016.5519
|
|
|
“Nano- and microcrystal investigations of precipitates, interfaces and strain fields in Ni-Ti-Nb by various TEM techniques”. Schryvers D, Shi H, Martinez GT, Van Aert S, Frenzel J, Van Humbeeck J, Materials science forum
T2 –, 9th European Symposium on Martensitic Transformations (ESOMAT 2012), SEP 09-16, 2012, St Petersburg, RUSSIA 738/739, 65 (2013). http://doi.org/10.4028/www.scientific.net/MSF.738-739.65
Abstract: In the present contribution several advanced electron microscopy techniques are employed in order to describe chemical and structural features of the nano- and microstructure of a Ni45.5Ti45.5Nb9 alloy. A line-up of Nb-rich nano-precipitates is found in the Ni-Ti-rich austenite of as-cast material. Concentration changes of the matrix after annealing are correlated with changes in the transformation temperatures. The formation of rows and plates of larger Nb-rich precipitates and particles is described. The interaction of a twinned martensite plate with a Nb-rich nano-precipitate is discussed and the substitution of Nb atoms on the Ti-sublattice in the matrix is confirmed.
Keywords: P1 Proceeding; Electron microscopy for materials research (EMAT)
Times cited: 2
DOI: 10.4028/www.scientific.net/MSF.738-739.65
|
|
|
“Quantitative annular dark field scanning transmission electron microscopy for nanoparticle atom-counting: What are the limits?”.De Backer A, De Wael A, Gonnissen J, Martinez GT, Béché, A, MacArthur KE, Jones L, Nellist PD, Van Aert S, Journal of physics : conference series 644Electron Microscopy and Analysis Group Conference (EMAG), JUN 02-JUL 02, 2015, Manchester, ENGLAND, 012034 (2015). http://doi.org/10.1088/1742-6596/644/1/012034
Abstract: Quantitative atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) has become a powerful technique for nanoparticle atom-counting. However, a lot of nanoparticles provide a severe characterisation challenge because of their limited size and beam sensitivity. Therefore, quantitative ADF STEM may greatly benefit from statistical detection theory in order to optimise the instrumental microscope settings such that the incoming electron dose can be kept as low as possible whilst still retaining single-atom precision. The principles of detection theory are used to quantify the probability of error for atom-counting. This enables us to decide between different image performance measures and to optimise the experimental detector settings for atom-counting in ADF STEM in an objective manner. To demonstrate this, ADF STEM imaging of an industrial catalyst has been conducted using the near-optimal detector settings. For this experiment, we discussed the limits for atom-counting diagnosed by combining a thorough statistical method and detailed image simulations.
Keywords: P1 Proceeding; Electron microscopy for materials research (EMAT)
DOI: 10.1088/1742-6596/644/1/012034
|
|
|
Van Aert S (2011) Atomen in 3D : Antwerpenaren brengen atomaire structuur nanodeeltjes in beeld. 9
Keywords: Newspaper/Magazine/blog article; Electron microscopy for materials research (EMAT)
|
|
|
“65th birthdays of W. Owen Saxton, David J. Smith and Dirk Van Dyck / PICO 2013 From multislice to big bang”. Lichte H, Dunin-Borkowski R, Tillmann K, Van Aert S, Van Tendeloo G Amsterdam (2013).
Keywords: ME3 Book as editor; Electron microscopy for materials research (EMAT)
|
|
|
“The notion of resolution”. Van Aert S, den Dekker AJ, van Dyck D, van den Bos A Springer, Berlin, page 1228 (2008).
Keywords: H3 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
|
|
|
“The notion of resolution”. Van Aert S, den Dekker AJ, van Dyck D, van den Bos A Springer, Berlin, page 1228 (2007).
Keywords: H3 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
|
|
|
“High resolution electron microscopy from imaging towards measuring”. Van Aert S, den Dekker AJ, van den Bos A, Van Dyck D ... IEEE International Instrumentation and Measurement Technology Conference
T2 – Rediscovering measurement in the age of informatics : proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference (IMTC), 2001: vol 3. Ieee, page 2081 (2001).
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1109/IMTC.2001.929564
|
|
|
“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
|
|
|
“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
|
|
|
“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
|
|
|
“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
|
|
|
“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
|
|
|
“Statistical experimental design for quantitative atomic resolution transmission electron microscopy”. Van Aert S, den Dekker AJ, van den Bos A, van Dyck D Academic Press, San Diego, Calif., page 1 (2004).
Keywords: H1 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
Times cited: 13
DOI: 10.1016/S1076-5670(04)30001-7
|
|
|
“The benefits of statistical parameter estimation theory for quantitative interpretation of electron microscopy data”. Van Aert S, Bals S, Chang LY, den Dekker AJ, Kirkland AI, Van Dyck D, Van Tendeloo G Springer, Berlin, page 97 (2008).
Keywords: H1 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1007/978-3-540-85156-1_49
|
|
|
“High-resolution visualization techniques : structural aspects”. Schryvers D, Van Aert S Springer, Berlin, page 135 (2012).
Keywords: H1 Book chapter; Electron microscopy for materials research (EMAT)
|
|
|
“Statistical parameter estimation theory : a tool for quantitative electron microscopy”. Van Aert S Wiley-VCH, Weinheim, page 281 (2012).
Keywords: H1 Book chapter; Electron microscopy for materials research (EMAT)
|
|
|
“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
|
|
|
Grü,newald L, Chezganov D, De Meyer R, Orekhov A, Van Aert S, Bogaerts A, Bals S, Verbeeck J (2023) Supplementary Information for “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope”
Abstract: Supplementary information for the article “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” containing the videos of in-situ SEM imaging (mp4 files), raw data/images, and Jupyter notebooks (ipynb files) for data treatment and plots. Link to the preprint: https://doi.org/10.48550/arXiv.2308.15123 Explanation of the data files can be found in the Information.pdf file. The Videos folder contains the in-situ SEM image series mentioned in the paper. If there are any questions/bugs, feel free to contact me at lukas.grunewaldatuantwerpen.be
Keywords: Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT)
DOI: 10.5281/ZENODO.8042030
|
|
|
Cioni M, Delle Piane M, Polino D, Rapetti D, Crippa M, Arslan Irmak E, Pavan GM, Van Aert S, Bals S (2024) Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning
Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.
Keywords: Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
DOI: 10.5281/ZENODO.10997963
|
|
|
Zhang Z, Lobato I, Brown H, Jannis D, Verbeeck J, Van Aert S, Nellist P (2023) Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions
Abstract: Inelastic excitation as exploited in Electron Energy Loss Spectroscopy (EELS) contains a rich source of information that is revealed in the scattering process. To accurately quantify core-loss EELS, it is common practice to fit the observed spectrum with scattering cross-sections calculated using experimental parameters and a Generalized Oscillator Strength (GOS) database [1]. The GOS is computed using Fermi’s Golden Rule and orbitals of bound and excited states. Previously, the GOS was based on Hartree-Fock solutions [2], but more recently Density Functional Theory (DFT) has been used [3]. In this work, we have chosen to use the Dirac equation to incorporate relativistic effects and have performed calculations using Flexible Atomic Code (FAC) [4]. This repository contains a tabulated GOS database based on Dirac solutions for computing double differential cross-sections under experimental conditions. We hope the Dirac-based GOS database can benefit the EELS community for both academic use and industry integration. Database Details: – Covers all elements (Z: 1-108) and all edges – Large energy range: 0.01 – 4000 eV – Large momentum range: 0.05 -50 Å-1 – Fine log sampling: 128 points for energy and 256 points for momentum – Data format: GOSH [3] Calculation Details: – Single atoms only; solid-state effects are not considered – Unoccupied states before continuum states of ionization are not considered; no fine structure – Plane Wave Born Approximation – Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when – core-shell electron is excited – Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; Local Density Approximation is assumed for electron exchange interactions; continuum states are normalized against asymptotic form at large distances – Both large and small component contributions of Dirac solutions are included in GOS – Final state contributions are included until the contribution of the previous three states falls below 0.1%. A convergence log is provided for reference. Version 1.1 release note: – Update to be consistent with GOSH data format [3], all the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script. Version 1.2 release note: – Add “File Type / File version” information [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689.
Keywords: Dataset; Electron microscopy for materials research (EMAT)
DOI: 10.5281/ZENODO.8360240
|
|
|
“Atomen tellen”. Van Aert S, Batenburg J, Van Tendeloo S, Nederlands tijdschrift voor natuurkunde (1991) 77, 292 (2011)
Keywords: A3 Journal article; Electron microscopy for materials research (EMAT); Vision lab
|
|
|
“Statistical estimation of oxygen atomic positions eith sub Ångstrom precision from exit wave reconstruction”. Bals S, Van Aert S, Van Tendeloo G, van Dyck D, Avila-Brande D, Microscopy and microanalysis 11, 556 (2005)
Keywords: A3 Journal article; Electron microscopy for materials research (EMAT); Vision lab
|
|
|
“Obstacles on the road towards atomic resolution tomography”. van Dyck D, Van Aert S, Croitoru MD, Microscoy and microanalysis 11, 238 (2005)
Keywords: A3 Journal article; Electron microscopy for materials research (EMAT); Condensed Matter Theory (CMT); Vision lab
|
|
|
“Three-dimensional reconstruction of a nanoparticle at atomic resolution”. Batenburg J, Van Aert S, ERCIM news 86, 52 (2011)
Keywords: A2 Journal article; Electron microscopy for materials research (EMAT); Vision lab
|
|