Abstract: Objective: We aim to investigate if anatomical and functional properties of the upper airway using computerized 3D models derived from computed tomography (CT) scans better predict obstructive sleep apnea (OSA) severity than standard clinical markers. Methods: Consecutive children with suspected OSA underwent polysomnography, clinical assessment of upper airway patency, and a CT scan while awake. A three-dimensional (3D) reconstruction of the pharyngeal airway was built from these images, and computational fluid dynamics modeling of low inspiratory flow was performed using open-source software. Results: Thirty-three children were included (23 boys; mean age, was 6.0 +/- 3.2 y). OSA was diagnosed in 23 patients. Children with OSA had a significantly lower volume of the overlap region between tonsils and the adenoids (median volume, 1408 mm compared to 2173 mm; p = 0.04), a lower mean cross-sectional area at this location (median volume, 69.3 mm(2) compared to 114.3 mm(2); p = 0.04), and a lower minimal cross-sectional area (median volume, 17.9 mm(2) compared to 25.9 mm(2); p = 0.05). Various significant correlations were found between several imaging parameters and the severity of OSA, most pronounced for upper airway conductance (r = -0.46) (p < 0.01) for correlation between upper airway conductance and the apnea-hypopnea index. No differences or significant correlations were observed with clinical parameters of upper airway patency. Preliminary data after treatment showed that none of the patients with residual OSA had their smallest cross-sectional area located in segment 3, and this frequency was significantly lower than in their peers whose sleep study normalized (64%; p = 0.05). Conclusion: Functional imaging parameters are highly correlated with OSA severity and are a more powerful correlate than clinical scores of upper airway patency. Preliminary data also showed that we could identify differences in the upper airway of those subjects who did not benefit from a local upper airway treatment. (c) 2013 Elsevier B.V. All rights reserved.

Abstract: Background: Inhaled formulations using extrafine particles of long-acting beta(2)-agonists and corticosteroids were developed to optimize asthma treatment. Findings that these combinations reach and treat smaller airways more effectively are predominantly based on general non-specific outcomes with little information on regional characteristics. Objectives: This study aims to assess long-term effects of extrafine beclomethasone/formoterol on small airways of asthmatic patients using novel functional imaging methods. Methods: Twenty-four stable asthma patients were subdivided into three groups (steroid naive, n = 7; partially controlled, n = 6; well controlled, n = 11). Current treatment was switched to a fixed combination of extrafine beclomethasone/formoterol (Foster (R); Chiesi Pharmaceuticals, Parma, Italy). Patients underwent lung function evaluation and thorax high-resolution computerized tomography (HRCT) scan. Local airway resistance was obtained from computational fluid dynamics (CFD). Results: After 6 months, the entire population showed improvement in pre-bronchodilation imaging parameters, including small airway volume (p = 0.0007), resistance (p = 0.011), and asthma control score (p = 0.016). Changes in small airway volume correlated with changes in asthma control score (p = 0.004). Forced expiratory volume in 1 s (p = 0.044) and exhaled nitric oxide (p = 0.040) also improved. Functional imaging provided more detail and clinical relevance compared to lung function tests, especially in the well-controlled group where only functional imaging parameters showed significant improvement, while the correlation with asthma control score remained. Conclusions: Extrafine beclomethasone/formoterol results in a significant reduction of small airway obstruction, detectable by functional imaging (HRCT/CFD). Changes in imaging parameters correlated significantly with clinically relevant improvements. This indicates that functional imaging is a useful tool for sensitive assessment of changes in the respiratory system after asthma treatment. Copyright (C) 2013 S. Karger AG, Basel

Abstract: Zinc is routinely employed in the synthesis of InP quantum dots (QDs) to improve the photoluminescence efficiency and carrier mobility of the resulting In(Zn)P alloy nanostructures. The exact location of Zn in the final structures and the mechanism by which it enhances the optoelectronic properties of the QDs is debated. We use synchrotron X-ray absorbance spectroscopy to show that the majority of Zn in In(Zn)P QDs is located at their surface as Zn-carboxylates. However, a small amount of Zn is present inside the bulk of the QDs with the consequent contraction of their lattice, as confirmed by combining high resolution high-angle annular dark-field imaging scanning transmission electron microscopy (HAADF-STEM) with statistical parameter estimation theory. We further demonstrate that the Zn content and its incorporation into the QDs can be tuned by the ligation of commonly employed Zn carboxylate precursors: the use of highly reactive Zn-acetate leads to the formation of undesired Zn3P2 and the final nanostructures being characterized by broad optical features, whereas Zn-carboxylates with longer carbon chains lead to InP crystals with much lower zinc content and narrow optical features. These results can explain the differences between structural and optical properties of In(Zn)P samples reported across the literature, and provide a rational method to tune the amount of Zn in InP nanocrystals and to drive the incorporation of Zn either as surface Zn-carboxylate, as a substitutional dopant inside the InP crystal lattice, or even predominantly as Zn3P2.

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.

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.

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.

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.

Abstract: Recording the electron energy loss spectroscopy data cube with a series of energy filtered images is a dose inefficient process because the energy slit blocks most of the electrons. When recording the data cube by scanning an electron probe over the sample, perfect dose efficiency is attained; but due to the low current in nanoprobes, this often is slower, with a smaller field of view. In W. Van den Broek et al. [Ultramicroscopy, 106 (2006) 269], we proposed a new method to record the data cube, which is more dose efficient than an energy filtered series. It produces a set of projections of the data cube and then tomographically reconstructs it. In this article, we demonstrate these projections in practice, we present a simple geometrical model that allows for quantification of the projection angles and we present the first successful experimental reconstruction, all on a standard post-column instrument.

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.

Abstract: Nanoparticle self-assembly and epitaxy are utilized extensively to make 1D and 2D structures with complex shapes. High-resolution transmission electron microscopy (HRTEM) has shown that single-crystalline interfaces can form, but little is known about the strain and dislocations at these interfaces. Such information is critically important for applications: drastically reducing
dislocation density was the key breakthrough enabling widespread implementation of light-emitting diodes, while strain engineering has been fundamental to modern high-performance transistors, solar cells, and thermoelectrics. In this work, the interfacial defect and strain formation after selfassembly and room temperature epitaxy of 7 nm Pd nanocubes capped with polyvinylpyrrolidone (PVP) is examined. It is observed that, during ligand removal, the cubes move over large distances on the substrate, leading to both spontaneous self-assembly and epitaxy to form single crystals. Subsequently, atomically resolved images are used to quantify the strain and dislocation density at the epitaxial interfaces between cubes with different lateral and angular misorientations. It is shown that dislocation- and strain-free interfaces form when the nanocubes align parallel to each other. Angular misalignment between adjacent cubes does not necessarily lead to grain boundaries but does cause dislocations, with higher densities associated with larger rotations.

Abstract: We report an innovative method to explore the optimal experimental settings to detect light atoms from scanning transmission electron microscopy (STEM) images. Since light elements play a key role in many technologically important materials, such as lithium-battery devices or hydrogen storage applications, much effort has been made to optimize the STEM technique in order to detect light elements. Therefore, classical performance criteria, such as contrast or signal-to-noise ratio, are often discussed hereby aiming at improvements of the direct visual interpretability. However, when images are interpreted quantitatively, one needs an alternative criterion, which we derive based on statistical detection theory. Using realistic simulations of technologically important materials, we demonstrate the benefits of the proposed method and compare the results with existing approaches.

Abstract: A thorough understanding of the three-dimensional (3D) atomic structure and composition of coreshell nanostructures is indispensable to obtain a deeper insight on their physical behavior. Such 3D information can be reconstructed from two-dimensional (2D) projection images using electron tomography. Recently, different electron tomography techniques have enabled the 3D characterization of a variety of nanostructures down to the atomic level. However, these methods have all focused on the investigation of nanomaterials containing only one type of chemical element. Here, we combine statistical parameter estimation theory with compressive sensing based tomography to determine the positions and atom type of each atom in heteronanostructures. The approach is applied here to investigate the interface in coreshell Au@Ag nanorods but it is of great interest in the investigation of a broad range of nanostructures.

Abstract: 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.

Abstract: In the present paper, a statistical model-based method to count the number of atoms of monotype crystalline nanostructures from high resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images is discussed in detail together with a thorough study on the possibilities and inherent limitations. In order to count the number of atoms, it is assumed that the total scattered intensity scales with the number of atoms per atom column. These intensities are quantitatively determined using model-based statistical parameter estimation theory. The distribution describing the probability that intensity values are generated by atomic columns containing a specific number of atoms is inferred on the basis of the experimental scattered intensities. Finally, the number of atoms per atom column is quantified using this estimated probability distribution. The number of atom columns available in the observed STEM image, the number of components in the estimated probability distribution, the width of the components of the probability distribution, and the typical shape of a criterion to assess the number of components in the probability distribution directly affect the accuracy and precision with which the number of atoms in a particular atom column can be estimated. It is shown that single atom sensitivity is feasible taking the latter aspects into consideration.

Abstract: High angle annular dark field scanning transmission electron microscopy (HAADF STEM) images provide sample information which is sensitive to the chemical composition. The image intensities indeed scale with the mean atomic number Z. To some extent, chemically different atomic column types can therefore be visually distinguished. However, in order to quantify the atomic column composition with high accuracy and precision, model-based methods are necessary. Therefore, an empirical incoherent parametric imaging model can be used of which the unknown parameters are determined using statistical parameter estimation theory (Van Aert et al., 2009, [1]). In this paper, it will be shown how this method can be combined with frozen lattice multislice simulations in order to evolve from a relative toward an absolute quantification of the composition of single atomic columns with mixed atom types. Furthermore, the validity of the model assumptions are explored and discussed.