“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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