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: Principal component analysis (PCA) noise filtering is a popular method to remove noise from experimental electron energy loss (EELS) spectrum images. Here, we investigate the statistical behaviour of this method by applying it on a simulated data set with realistic noise levels. This phantom data set provides access to the true values contained in the data set as well as to many different realizations of the noise. Using least squares fitting and parameter estimation theory, we demonstrate that even though the precision on the estimated parameters can be better as the CramérRao lower bound, a significant bias is introduced which can alter the conclusions drawn from experimental data sets. The origin of this bias is in the incorrect retrieval of the principal loadings for noisy data. Using an expression for the bias and precision of the singular values from literature, we present an evaluation criterion for these singular values based on the noise level and the amount of information present in the data set. This criterion can help to judge when to avoid PCA noise filtering in practical situations. Further we show that constructing elemental maps of PCA noise filtered data using the background subtraction method, does not guarantee an increase in the signal to noise ratio due to correlation of the spectral data as a result of the filtering process.

Abstract: A current method for the protection of cretaceous limestone present in various monuments consists of performing a passivating treatment with ammonium oxalate (AmOx). A calcium oxalate protective layer is formed on the surface and enhances the acid resistance of the stone. The in-depth formation of the calcium oxalate layer was investigated on cross sections by using combined micro X-ray fluorescence and micro X-ray powder diffraction (mu XRF/mu XRPD). XRPD showed the presence of both whewellite and weddellite in the calcite stone matrix. A correction was made for sample misalignment, which was visible in both the fluorescence and the diffraction line measurements. A semi-quantitative analysis was performed on the basis of Klug's equation for a two-phase mixture (the presence of weddellite was neglected) without the need for a known reference sample. By assuming two extreme compositions for a reference weight fraction (1 and 99wt%), it was possible to obtain whewellite concentration profiles, which can be used for comparing the effectiveness of different methods for the application of AmOx to the stone surface and the effect of treatment time and AmOx concentration used. It is shown that for the relative amounts of whewellite formed, the differences due to the assumed weight fractions are smaller than the errors due to sample heterogeneity and preferred orientation. Copyright (c) 2013 John Wiley & Sons, Ltd.

Abstract: The crystal structure of SrCo0.7Fe0.2Nb0.1O2.72 was determined using a combination of precession electron diffraction (PED), high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) and spatially resolved electron energy loss spectroscopy (STEM-EELS). The structure has a tetragonal P4/mmm symmetry with cell parameters a = b = a(p), c = 2a(p) (a(p) being the cell parameter of the perovskite parent structure). Octahedral BO2 layers alternate with the anion-deficient BO1.4 layers, the different B cations are randomly distributed over both layers. The specific feature of the SrCo0.7Fe0.2NB0.1O2.72 microstructure is a presence of extensive nanoscale twinning resulting in domains with alignment of the tetragonal c-axis along all three cubic direction of the perovskite subcell.