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• A method to quantify atomic resolution first moment STEM images was developed. • The images are modelled as a superposition of derivatives of 2D Lorentzian peaks. • The unknown parameters are estimated using the least squares estimator. • The estimated parameters allow determination of atom counts in each atomic column. • First moment STEM imaging shows promise compared to HAADF STEM for atom counting. Through a simulation-based study we develop a statistical model-based quantification method for atomic resolution first moment scanning transmission electron microscopy (STEM) images. This method uses the uniformly weighted least squares estimator to determine the unknown structure parameters of the images and to isolate contributions from individual atomic columns. In this way, a quantification of the projected potential per atomic column is achieved. Since the integrated projected potential of an atomic column scales linearly with the number of atoms it contains, it can serve as a basis for atom counting. The performance of atom counting from first moment STEM imaging is compared to that from traditional HAADF STEM in the presence of noise. Through this comparison, we demonstrate the advantage of first moment STEM images to attain more precise atom counts. Finally, we compare the integrated potential extracted from first-moment images of a wedge-shaped sample to those values from the bulk crystal. The excellent agreement found between these values proves the robustness of using bulk crystal simulations as a reference library. This enables atom counting for samples with different shapes by comparison with these library values. [ABSTRACT FROM AUTHOR]

Nowadays, aberration corrected transmission electron microscopy (TEM) is a popular method to characterise nanomaterials at the atomic scale. Here, atomically resolved images of nanomaterials are acquired, where the contrast depends on the illumination, imaging and detector conditions of the microscope. Visualization of light elements is possible when using low angle annular dark field (LAADF) STEM, annular bright field (ABF) STEM, integrated differential phase contrast (iDPC) STEM, negative spherical aberration imaging (NCSI) and imaging STEM (ISTEM). In this work, images of a NdGaO 3 -La 0.67 Sr 0.33 MnO 3 (NGO-LSMO) interface are quantitatively evaluated by using statistical parameter estimation theory. For imaging light elements, all techniques are providing reliable results, while the techniques based on interference contrast, NCSI and ISTEM, are less robust in terms of accuracy for extracting heavy column locations. In term of precision, sample drift and scan distortions mainly limits the STEM based techniques as compared to NCSI. Post processing techniques can, however, partially compensate for this. In order to provide an outlook to the future, simulated images of NGO, in which the unavoidable presence of Poisson noise is taken into account, are used to determine the ultimate precision. In this future counting noise limited scenario, NCSI and ISTEM imaging will provide more precise values as compared to the other techniques, which can be related to the mechanisms behind the image recording. [ABSTRACT FROM AUTHOR]

Aberration correction in scanning transmission electron microscopy (STEM) has greatly improved the lateral and depth resolution. When using depth sectioning, a technique during which a series of images is recorded at different defocus values, single impurity atoms can be visualised in three dimensions. In this paper, we investigate new possibilities emerging when combining depth sectioning and precise atom-counting in order to reconstruct nanosized particles in three dimensions. Although the depth resolution does not allow one to precisely locate each atom within an atomic column, it will be shown that the depth location of an atomic column as a whole can be measured precisely. In this manner, the morphology of a nanoparticle can be reconstructed in three dimensions. This will be demonstrated using simulations and experimental data of a gold nanorod. [ABSTRACT FROM AUTHOR]

Recently, imaging scanning transmission electron microscopy (ISTEM) has been proposed as a promising new technique combining the advantages of conventional TEM (CTEM) and STEM (Rosenauer et al., 2014 [1] ). The ability to visualize light and heavy elements together makes it a particularly interesting new, spatially incoherent imaging mode. Here, we evaluate this technique in term of precision with which atomic column locations can be measured. By using statistical parameter estimation theory, we will show that these locations can be accurately measured with a precision in the picometer range. Furthermore, a quantitative comparison is made with HAADF STEM imaging to investigate the advantages of ISTEM. [ABSTRACT FROM AUTHOR]

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 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, has been investigated. The highest attainable precision is reached even for low dose images. Furthermore, the advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. This is done for the estimation of the distance between two neighbouring columns as a function of their distance and for the estimation of the scattering cross-section which is compared to the integrated intensity from a Voronoi cell. 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. [ABSTRACT FROM AUTHOR]

Abstract: Off-axis electron holography is a method for the transmission electron microscope (TEM) that measures the electric and magnetic properties of a specimen. The electrostatic and magnetic potentials modulate the electron wavefront phase. The error in measurement of the phase therefore determines the smallest observable changes in electric and magnetic properties. Here we explore the summation of a hologram series to reduce the phase error and thereby improve the sensitivity of electron holography. Summation of hologram series requires independent registration and correction of image drift and phase wavefront drift, the consequences of which are discussed. Optimization of the electro-optical configuration of the TEM for the double biprism configuration is examined. An analytical model of image and phase drift, composed of a combination of linear drift and Brownian random-walk, is derived and experimentally verified. The accuracy of image registration via cross-correlation and phase registration is characterized by simulated hologram series. The model of series summation errors allows the optimization of phase error as a function of exposure time and fringe carrier frequency for a target spatial resolution. An experimental example of hologram series summation is provided on WS2 fullerenes. A metric is provided to measure the object phase error from experimental results and compared to analytical predictions. The ultimate experimental object root-mean-square phase error is 0.006rad (2π/1050) at a spatial resolution less than 0.615nm and a total exposure time of 900s. The ultimate phase error in vacuum adjacent to the specimen is 0.0037rad (2π/1700). The analytical prediction of phase error differs with the experimental metrics by +7% inside the object and −5% in the vacuum, indicating that the model can provide reliable quantitative predictions. [Copyright &y& Elsevier]

Abstract: A new graphical software (STEM_CELL) for analysis of HRTEM and STEM-HAADF images is here introduced in detail. The advantage of the software, beyond its graphic interface, is to put together different analysis algorithms and simulation (described in an associated article) to produce novel analysis methodologies. Different implementations and improvements to state of the art approach are reported in the image analysis, filtering, normalization, background subtraction. In particular two important methodological results are here highlighted: (i) the definition of a procedure for atomic scale quantitative analysis of HAADF images, (ii) the extension of geometric phase analysis to large regions up to potentially 1μm through the use of under sampled images with aliasing effects. [Copyright &y& Elsevier]

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. • Elliptical Gaussian peaks are introduced to quantify ADF STEM image intensities. • Their performance is compared quantitatively to symmetrical Gaussian peaks. • There is a qualitative improvement in the presence of sample tilt. • Quantitatively, the performance is the same for symmetrical and elliptical peaks. • Reliable atom-counting remains possible in the presence of sample tilt. [ABSTRACT FROM AUTHOR]

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. • Hidden Markov models are introduced to quantify sequential ADF STEM images. • These models can be used for atom-counting from time series data. • The method outperforms existing atom-counting methods. • The probability and cross section for atomic structural changes are estimated. • Atomic scale dynamic processes are reliably analysed quantitatively. [ABSTRACT FROM AUTHOR]