A two-step framework to analyze local microstructure variations of paper sheets based on 3D image data is presented. First, a multi-stage workflow efficiently acquires a large set of highly resolved tomographic image data, which enables—in combination with statistical image analysis—the quantification of local variations and pairwise correlations of morphological microstructure characteristics on length scales ranging from micrometers to centimeters. Secondly, the microstructure is analyzed in terms of the local behavior of porosity, thickness, and further descriptors related to transportation paths. The power of the presented framework is demonstrated, showing that it allows one (i) to quantitatively reveal the difference in terms of local structural variations between a model paper before and after unidirectional compression via hard-nip calendering and that (ii) the field of view which is required to reliably compute the probability distributions of the considered local microstructure characteristics is at least 20 mm$^{2}$. The results elucidate structural differences related to local densification. In particular, it is shown how calendering transforms local variations in sheet thickness into marked local mass density variations. The obtained results are in line with experimental measurements of macroscopic properties (basis weight, Bekk smoothness parameters, thickness, and Gurley retention times).

Core holes are an important contributing factor to the core-loss fine structure in electron energy-loss spectroscopy (EELS). While there has been much work on bulk materials, less is known about core hole screening in more complex dielectric environments, such as free surfaces or interfaces between two different materials, even though the latter is frequently encountered in high spatial resolution EELS analysis. In this work, experimental O K- and Mg L3,2-EELS edges from the free surfaces of a MgO cube are measured using scanning transmission electron microscopy (STEM). The free surface O K-spectrum shows extra intensity at the edge onset compared with the “bulk” spectrum. Core hole screening near a free surface is modeled using electrodynamic theory. It is shown that much of the extra intensity is due to reduced core hole screening, while genuine surface states make a smaller contribution to the fine structure. The low energy Mg L3,2-edge, however, does not show any significant change at a free surface. This is because the measurement is less surface sensitive due to strong delocalization.

Algae like diatoms are widely studied as a means to remediate anthropogenically contaminated sites. In the present study, CL (cathodoluminescence) and EDX (energy-dispersive X-ray) spectroscopy in an SEM (scanning electron microscope) were optimized for the detection of Eu(III) sorbed on diatom biosilica. The required stability of biosilica under a focused electron beam was extensively investigated. Using experimentally determined data of thermal properties, the temperature increase within biosilica exposed to an electron beam was simulated by finite element calculations based on results from Monte Carlo simulations of electron scattering. Complementary thermogravimetric studies substantiated a chemical stability of biosilica in a wide temperature range, confirming its suitability for long-lasting SEM investigations. In subsequent EDX measurements, characteristic Eu lines were detected. Eu was found to preferentially accumulate and aggregate on silica fragments. CL spectra were obtained for the Eu(III) reference material, EuCl3. For Eu-loaded biosilica, even parts without detectable Eu signal in the EDX spectra show significant Eu(III) signals in the CL spectra. This highlights the sensitivity of CL in studying f-element sorption. CL data showed that Eu(III) was distributed on the entire surface. In conclusion, this work demonstrates the merit of CL and EDX methods for sorption studies on biogenic materials.

In the field of high-temperature superconductors, atom probe tomography is a relatively new instrument, with the ability to provide a new perspective on the 3D nanoscale microstructure. However, field evaporation of nonmetallic materials is fraught with unique challenges that matter little in the world of metallic evaporation. In this study, we review the laser absorption, correlated evaporation, molecular dissociation, and the crystallographic effects on the field evaporation of 800-m ${\rm RB}{\rm a}_ 2{\rm C}{\rm u}_ 3{\rm O}_{ 7-{\rm \delta }}$ (R = Gd, Sm) coated conductor tapes deposited by Reactive Co-Evaporation Cyclic Deposition and Reaction (RCE-CDR). Ultraviolet 355 nm laser pulsing was found to have a substantial beneficial effect on minimizing the fracture probability compared with 532 nm illumination, especially when evaporating insulating oxide precipitates. This, in turn, allows for the 3D compositional analysis of defects such as flux pinning centers introduced by precipitation and doping. As a result, evidence for the precipitation of nanoscale ${\rm G}{\rm d}_ 2{\rm C}{\rm u}_ 2{\rm O}_ 5$ is discussed. The effect of crystallographic orientation is studied, where [001] aligned evaporation is found to develop compositional aberrations.

Multilayers that comprise thin films of heavy metals and ferromagnets have been shown to host Néel-type magnetic skyrmions at room temperature. Fresnel defocus imaging in Lorentz transmission electron microscopy is a widely used technique for recording magnetic information about skyrmions. However, the visibility of Néel-type skyrmions in Fresnel defocus images is typically low, both because only a small component of their magnetic field contributes to the signal and because of the presence of diffraction contrast from the polycrystalline multilayer structure. Here, we take advantage of the out-of-plane hysteresis in such samples to record background-subtracted Fresnel defocus images. We demonstrate an improvement in magnetic signal-to-noise ratio and spatial resolution by a factor of 3 for a (Pt/Co/NiFe)×5 multilayer. We also use simulated Fresnel defocus images of Néel-type magnetic skyrmions to understand the influence of defocus on apparent skyrmion size.

Thanks to the ability to perform imaging and manipulation at the nanoscale, atomic force microscopy (AFM) has been widely used in biology, materials, chemistry, and other fields. However, as common error sources, vertical drift and illusory slope severely impair AFM imaging quality. To address this issue, this paper proposes a robust algorithm to synchronously correct the image distortion caused by vertical drift and slope, thus achieving accurate morphology characterization. Specifically, to eliminate the damage of abnormal points and feature areas on the correction accuracy, the laser spot voltage error acquired in the AFM scanning process is first utilized to preprocess the morphology height data of the sample, so as to obtain the refined alternative data suitable for line fitting. Subsequently, this paper proposes a novel line fitting algorithm based on sparse sample consensus, which accurately simulates vertical drift and slope in the cross-sectional profile of the topographic image, thereby achieving effective correction of the image distortion. In the experiments and applications, a nanoscale optical grating sample and a biological cell sample are adopted to perform topography imaging and distortion correction, so as to verify the ability of the proposed algorithm to promote AFM imaging quality.

Quantification of electron-exited X-ray spectra following the standards-based “k-ratio” (unknown/standard intensity) protocol with corrections for “matrix effects” (electron energy loss and backscattering, X-ray absorption, and secondary X-ray fluorescence) is a well-established method with a record of rigorous testing and extensive experience. Two recent studies by Gopon et al. working in the Fe–Si system and Llovet et al. working in the Ni–Si system have renewed interest in studying the accuracy of measurements made using L-shell X-ray peaks. Both have reported unexpectedly large deviations in analytical accuracy when analyzing intermetallic compounds when using the low photon energy Fe or Ni L-shell X-ray peaks with pure element standards and wavelength-dispersive X-ray spectrometry. This study confirms those observations on the Ni-based intermetallic compounds using energy-dispersive X-ray spectrometry and extends the study of analysis with low photon energy L-shell peaks to a wide range of elements, Ti to Se. Within this range of elements, anomalies in analytical accuracy have been found for Fe, Co, and Ge in addition to Ni. For these elements, the use of compound standards instead of pure elements usually resulted in significantly improved analytical accuracy. However, compound standards do not always provide satisfactory accuracy as is demonstrated for L-shell peak analysis in the Fe–S system: FeS and FeS2 unexpectedly do not provide good accuracy when used as mutual standards.

Currently, the automated electron backscatter diffraction (EBSD) technique only allows the differentiation of the Laue groups based on an electron backscatter pattern (EBSP). This article shows that information concerning the lattice plane polarity is not only stored in the EBSP, but also in the Hough transformed EBSP where it can be easily accessed for automated evaluation. Polar Kikuchi bands lead to asymmetric peaks during the Hough transformation that are dependent on the atomic number difference of the involved atoms. The effect can be strong enough to be detected when evaluating the intensities of the regular excess and deficiency lines. Polarity detection from the Hough transformation of an EBSP cannot only enhance the utility of the EBSD technique and expand the information gained from it, but also illustrates a path toward automated polarity determination during EBSD scans.

Technological opportunities are explored to enhance detection schemes in transmission electron microscopy (TEM) that build on the detection of single-electron scattering events across the typical spectrum of interdisciplinary applications. They range from imaging with high spatiotemporal resolution to diffraction experiments at the window to quantum mechanics, where the wave-particle dualism of single electrons is evident. At the ultimate detection limit, where isolated electrons are delivered to interact with solids, we find that the beam current dominates damage processes instead of the deposited electron charge, which can be exploited to modify electron beam-induced sample alterations. The results are explained by assuming that all electron scattering are inelastic and include phonon excitation that can hardly be distinguished from elastic electron scattering. Consequently, a coherence length and a related coherence time exist that reflect the interaction of the electron with the sample and change linearly with energy loss. Phonon excitations are of small energy (<100 meV), but they occur frequently and scale with beam current in the irradiated area, which is why we can detect their contribution to beam-induced sample alterations and damage.

A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

Scanning transmission X-ray microscopy is a powerful method for mapping chemical phases in nano-materials. The point spread function (PSF) of a conventional zone-plate-based microscope limits the achievable spatial resolution and also results in spatially resolved spectra that do not accurately reflect the spatial heterogeneity of the samples when the scale of the detail approaches the probe size. X-ray ptychography, a coherent-scattering-based imaging scheme that effectively removes the probe from the image data, returns accurate spectra from regions smaller than the probe size. We show through simulation how the long tails on the PSF of an x-ray optic can cause spectral distortion near a boundary between two spectrally distinct regions. The resulting apparent point spectra can appear mixed, with the species on one side of the boundary seeming to be present on the other even at a distance from the boundary equal to several times the spatial resolution. We further demonstrate the effect experimentally and show that ptychographic microscopy can return the expected spectra from a model system, whereas conventional microscopy does not.

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.

Determining the composition of a mixed material is an open problem that has attracted the interest of researchers in many fields. In our recent work, we proposed a novel approach to determine the composition of a mixed material using convolutional neural networks (CNNs). In machine learning, a model “learns” a specific task for which it is designed through data. Hence, obtaining a dataset of mixed materials is required to develop CNNs for the task of estimating the composition. However, the proposed method instead creates the synthetic data of mixed materials generated from using only images of pure materials present in those mixtures. Thus, it eliminates the prohibitive cost and tedious process of collecting images of mixed materials. The motivation for this study is to provide mathematical details of the proposed approach in addition to extensive experiments and analyses. We examine the approach on two datasets to demonstrate the ease of extending the proposed approach to any mixtures. We perform experiments to demonstrate that the proposed approach can accurately determine the presence of the materials, and sufficiently estimate the precise composition of a mixed material. Moreover, we provide analyses to strengthen the validation and benefits of the proposed approach.

Recent advances in scanning transmission electron microscopy (STEM) have rekindled interest in multi-channel detectors and prompted the exploration of unconventional scan patterns. These emerging needs are not yet addressed by standard commercial hardware. The system described here incorporates a flexible scan generator that enables exploration of low-acceleration scan patterns, while data are recorded by a scalable eight-channel array of nonmultiplexed analog-to-digital converters. System integration with SerialEM provides a flexible route for automated acquisition protocols including tomography. Using a solid-state quadrant detector with additional annular rings, we explore the generation and detection of various STEM contrast modes. Through-focus bright-field scans relate to phase contrast, similarly to wide-field TEM. More strikingly, comparing images acquired from different off-axis detector elements reveals lateral shifts dependent on defocus. Compensation of this parallax effect leads to decomposition of integrated differential phase contrast (iDPC) to separable contributions relating to projected electric potential and to defocus. Thus, a single scan provides both a computationally refocused phase contrast image and a second image in which the signed intensity, bright or dark, represents the degree of defocus.

Atomic force microscopy (AFM) can determine mechanical properties, associated with surface topography and structure, of a material at the nanoscale. Force–indentation curves that depict the deformation of a target specimen as a function of an applied force are widely used to determine the elastic modulus of a material based on a contact model. However, a hysteresis may arise due to friction between the AFM tip and a specimen. Consequently, the normal force detected using a photodetector during extension and retraction could be underestimated and overestimated, respectively, and the extension/retraction data could result in a significant difference in the elastic modulus measurement result. In this study, elastic modulus and friction coefficient values were determined based on an in situ theoretical model that compensated for the effect of friction on force–indentation data. It validated the proposed model using three different polymer specimens and colloidal-tipped probes for the force–indentation curve and friction loop measurements. This research could contribute to the accurate measurement of mechanical properties using AFM by enhancing the interpretation of force–indentation curves with friction-induced hysteresis. Furthermore, the proposed approach may be useful for analyzing in situ relationships between mechanical and frictional properties from a fundamental tribological perspective.

Accurate predetermination of the quantum yield ratio (QA/QD) and the extinction coefficient ratio (KA/KD) between acceptor and donor is a prerequisite for quantitative fluorescence resonance energy transfer (FRET) imaging. We here propose a method to measure KA/KD and QA/QD by measuring the excitation–emission spectra (ExEm-spectra) of one dish of cells expressing m (≥3) kinds of FRET constructs. The ExEm-spectra images are unmixed to obtain the weight maps of donor (WD), acceptor (WA), and acceptor sensitization (WS). For each cell, the frequency distribution plots of the WS/WD and WS/WA images are fitted by using a single-Gaussian function to obtain the peak values of WS/WD (SD) and WS/WA (SA). The statistical frequency-SD/SA plots from all cells are fitted by using a multi-Gaussian function to obtain the peak values of both SD and SA, and then the ranges of WS/WD (RSD) and WS/WA (RSA) for each FRET construct are predetermined. Based on the predetermined RSD and RSA values of FRET constructs, our method is capable of automatically classifying cells expressing different FRET constructs. Finally, the WS/WD–WA/WD plot from different kinds of cells is linearly fitted to obtain KA/KD and QA/QD values.

Immunostaining in clinical routine and research highly depends on standardized staining methods and quantitative image analyses. We qualitatively and quantitatively compared antigen retrieval methods (no pretreatment, pretreatment with pepsin, and heat-induced pretreatment with pH 6 or pH 9) for 17 antibodies relevant for placenta and implantation diagnostics and research. Using our newly established, comprehensive automated quantitative image analysis approach, fluorescent signal intensities were evaluated. Automated quantitative image analysis found that 9 out of 17 antibodies needed antigen retrieval to show positive staining. Heat induction proved to be the most efficient form of antigen retrieval. Eight markers stained positive after pepsin digestion, with β-hCG and vWF showing enhanced staining intensities. To avoid the misinterpretation of quantitative image data, the qualitative aspect should always be considered. Results from native placental tissue were compared with sections of a placental invasion model based on thermo-sensitive scaffolds. Immunostaining on placentas in vitro leads to new insights into fetal development and maternal pathophysiological pathways, as pregnant women are justifiably excluded from clinical studies. Thus, there is a clear need for the assessment of reliable immunofluorescent staining and pretreatment methods. Our evaluation offers a powerful tool for antibody and pretreatment selection in placental research providing objective and precise results.

Interpretation errors may still represent a limiting factor for diagnosing Cryptosporidium spp. oocysts with the conventional staining techniques. Humans and machines can interact to solve this problem. We developed a new temporary staining protocol associated with a computer program for the diagnosis of Cryptosporidium spp. oocysts in fecal samples. We established 62 different temporary staining conditions by studying 20 experimental protocols. Cryptosporidium spp. oocysts were concentrated using the Three Fecal Test (TF-Test®) technique and confirmed by the Kinyoun method. Next, we built a bank with 299 images containing oocysts. We used segmentation in superpixels to cluster the patches in the images; then, we filtered the objects based on their typical size. Finally, we applied a convolutional neural network as a classifier. The trichrome modified by Melvin and Brooke, at a concentration use of 25%, was the most efficient dye for use in the computerized diagnosis. The algorithms of this new program showed a positive predictive value of 81.3 and 94.1% sensitivity for the detection of Cryptosporidium spp. oocysts. With the combination of the chosen staining protocol and the precision of the computational algorithm, we improved the Ova and Parasite exam (O&P) by contributing in advance toward the automated diagnosis.

Lectins are glycoproteins of a non-immune origin often used as histochemical reagents to study the distribution of glycoconjugates in different types of tissues. In this study, we performed a comparative cellular localization of sugar residues in bull and donkey testes using immunofluorescent lectin histochemistry. We inspected the cellular localization of the glycoconjugates within the testes using 11 biotin-labeled lectins (LCA, ConA, PNA, WGA, DBA, SBA, ECA, BPL, PTL-II, UEA-1, and PHA-E4) classified under six groups. Although the basic testicular structure in both species was similar, the cellular components showed different lectin localization patterns. The statistical analysis revealed no significant association between the intensity of labeling and different variables, including group and type of lectin and type of cell examined, at p < 0.05. However, a stronger response tended to occur in the donkey than in the bull testes (odds ratio: 1.3). These findings may be associated with the different cellular compositions of the glycoproteins and modification changes during spermatogenesis. Moreover, glycoconjugate profiling through lectin histochemistry can characterize some cell-type selective markers that will be helpful in studying bull and donkey spermatogenesis.

Ectopic calcification or pathological biomineralization correlates with morbidity and mortality from cardiovascular diseases. Aortas with atherosclerotic lesions and biomineralization were selected for the study. Thirty samples of mineralized abdominal aortas (group M) were examined by histology. Depending on the calcifications size, samples were separated into group M1 (macroscopic calcifications) and M2 (microscopic calcifications). Each group consists of 15 samples. Calcification 2 mm or less were considered as microscopic, >2 mm—macroscopic. Thirty samples of aortic tissue without biomineralization (group C) were used as a control group. Aortic tissue was examined by macroscopic description, histology, histochemistry, immunohistochemistry (IHC), scanning electron microscopy (SEM) with microanalysis, and transmission electron microscopy (TEM). The results of IHC showed the involvement of OPN in the formation and development of pathological biomineralization, but the obvious role of OPN in the differentiation of macro- and microcalcifications of atherosclerotic aorta was not revealed. SEM with X-ray microanalysis confirmed that the biomineral part of the aortic samples of the M1 group consisted mainly of apatites, which correspond to previous studies. The Ca/P ratio was less in the M2 group than in the M1 group. It means that microcalcifications can be formed by more defective (immature) hydroxyapatite.