Microscopy and Microanalysis最新文献

Tumor histomorphology is crucial for the prognostication of breast cancer outcomes because it contains histological, cellular, and molecular tumor heterogeneity related to metastatic potential. To enhance breast cancer prognosis, we aimed to apply radiomics analysis-traditionally used in 3D scans-to 2D histopathology slides. This study tested radiomics analysis in a cohort of 92 breast tumor specimens for outcome prognosis, addressing -omics dimensionality by comparing models with moderate and high feature counts, using least absolute shrinkage and selection operator for feature selection and machine learning for prognostic modeling. In the test folds, models with radiomics features [area under the curves (AUCs) range 0.799-0.823] significantly outperformed the benchmark model, which only included clinicopathological (CP) parameters (AUC = 0.584). The moderate-dimensionality model with 11 CP + 93 radiomics features matched the performance of the highly dimensional models with 1,208 radiomics or 11 CP + 1,208 radiomics features, showing average AUCs of 0.823, 0.799, and 0.807 and accuracies of 79.8, 79.3, and 76.6%, respectively. In conclusion, our application of deep texture radiomics analysis to 2D histopathology showed strong prognostic performance with a moderate-dimensionality model, surpassing a benchmark based on standard CP parameters, indicating that this deep texture histomics approach could potentially become a valuable prognostic tool.

Deformation bands are common constituents of porous clastic fluid reservoirs. Various techniques have been used to study deformation band structure and the associated changes in porosity and permeability. However, the use of electron backscatter diffraction technique is limited. Thus, more information is needed regarding the crystallographic relationships between detrital crystals, which can significantly impact reservoir rock quality. We employ microscopic and microstructural investigation techniques to analyze the influence of cataclastic deformation bands on pore space. Porosity measurements of the Cretaceous Ilhas Group sandstone in NE Brazil, obtained through computerized microtomography, indicate that the undeformed domains exhibit a total porosity of up to 13%. In contrast, this porosity is slightly over 1% in the deformation bands. Scanning electron microscopy analyses revealed the presence of grain fragmentation and dissolution microstructures, along with cement-filling pre-existing pores. The electron backscatter diffraction analyses indicated extensive grain fragmentation and minimal contribution from intracrystalline plasticity as a deformation mechanism. However, the c axes of quartz crystals roughly align parallel to the orientation of the deformation band. In summary, we have confirmed and quantified the internal changes in a deformation band cluster, with grain size reduction and associated compaction as the main mechanism supported by quartz cementation.

Materials characterization using electron backscatter diffraction (EBSD) requires indexing the orientation of the measured region from Kikuchi patterns. The quality of Kikuchi patterns can degrade due to pattern overlaps arising from two or more orientations, in the presence of defects or grain boundaries. In this work, we employ constrained nonnegative matrix factorization to segment a microstructure with small grain misorientations, (<1∘), and predict the amount of pattern overlap. First, we implement the method on mixed simulated patterns-that replicates a pattern overlap scenario, and demonstrate the resolution limit of pattern mixing or factorization resolution using a weight metric. Subsequently, we segment a single-crystal dendritic microstructure and compare the results with high-resolution EBSD. By utilizing weight metrics across a low-angle grain boundary, we demonstrate how very small misorientations/low-angle grain boundaries can be resolved at a pixel level. Our approach constitutes a versatile and robust tool, complementing other fast indexing methods for microstructure characterization.

The controlled creation and manipulation of defects in 2D materials has become increasingly popular as a means to design and tune new material functionalities. However, defect characterization by direct atomic-scale imaging is often severely limited by surface contamination due to a blanket of hydrocarbons. Thus, analysis techniques that can characterize atomic-scale defects despite the contamination layer are advantageous. In this work, we take inspiration from X-ray absorption spectroscopy and use broad-beam electron energy loss spectroscopy (EELS) to characterize defect structures in 2D hexagonal boron nitride (hBN) based on averaged fine structure in the boron K-edge. Since EELS is performed in a transmission electron microscope (TEM), imaging can be performed in-situ to assess contamination levels and other factors such as tears in the fragile 2D sheets, which can affect the spectroscopic analysis. We demonstrate the TEM-EELS technique for 2D hBN samples irradiated with different ion types and doses, finding spectral signatures indicative of boron-oxygen bonding that can be used as a measure of sample defectiveness depending on the ion beam treatment. We propose that even in cases where surface contamination has been mitigated, the averaging-based TEM-EELS technique can be useful for efficient sample surveys to support atomically resolved EELS experiments.

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. As a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

Probe formation in scanning electron microscope (SEM) is often reduced to objective lens action modeling based on a point-spread function or Fourier transforms. In this study, we present the first complete wave optical modeling of the whole SEM column based on plane-by-plane propagation of the electron beam wavefunction without simplifying the optical system. We identify the challenges in plane-by-plane beam propagation and show how sampling limitations produce aliased results. Through a careful selection and combination of propagators, we have developed a general wave optical propagation method that is able to overcome the aliasing problem to achieve the appropriate probe widths. Using a two-step propagator, we show that it is possible to model the electron beam distribution throughout the column from the virtual source plane to the specimen plane. We also show that our results from the wave optical simulations are consistent with the geometrical theory of probe formation. Finally, as a direct application of this method, we demonstrated that the combined effect of aberrations in the condenser lens and the probe forming objective lens cannot be accurately represented using only the objective lens. Designing beam shaping experiments and studying the effect of partial coherence can be some novel applications.

This study examines the egg-laying behavior and egg morphology of Hydrometra stagnorum (Linnaeus, 1753) (Gerromorpha: Heteroptera) to provide ecofaunistic information about the species. Newly recorded H. stagnorum samples were collected from the Karabük province of Western Black Sea region of Türkiye. Physicochemical parameters of the water were also recorded. The morphology and egg-laying behavior of H. stagnorum eggs were identified using a stereo, light and electron microscopy. Mature eggs were observed to be blackish dark brown in color. The study reveals distinctive characteristics of the egg structure and micropyle areas, which may contribute to the classification of the species at the subfamily level. Additionally, it was found that H. stagnorum inhabits high-quality waters.

Identifying clusters of solute atoms in a matrix of solvent atoms helps to understand precipitation phenomena in alloys, for example, during the age hardening of certain aluminum alloys. Atom probe tomography datasets can deliver such information, provided that appropriate cluster identification routines are available. We investigate algorithms based on the local composition of the neighborhood of solute atoms and compare them with traditional approaches based on the local solute number density, such as the maximum separation distance method. For an ideal solid solution, the pair correlation functions of the kth nearest solute atom in the coordination number representation are derived, and the percolation threshold and the size distribution of clusters are studied. A criterion for selecting optimal control parameters based on maximizing the phase separation by the degree of clustering is proposed for a two-phase system. A map of phase compositions accessible for cluster analysis is constructed. The coordination number approach reduces the influence of density variations commonly observed in atom probe tomography data. Finally, a practical cluster analysis technique applied to the early stages of aluminum alloy aging is described.

The accuracy of carbon composition measurement of carbide precipitates in steel or other alloys is limited by the evaporation characteristics of carbon and the performance of current detector systems. Carbon evaporates in a higher fraction as clustered ions leading to detector pile-up during so-called multiple hits. To achieve higher accuracy, a grid was positioned behind the local electrode, reducing the detection efficiency from 52 to 7% and thereby reducing the fraction of multi-hit events. This work confirms the preferential loss of carbon due to detector pile-up. Furthermore, we demonstrate that the newer generation of commercial atom probe instruments displays somewhat higher discrepancy of carbon composition than previous generations. The reason for this might be different laser-matter interaction leading to less metal ions in multi-hit events.

The application of atom probe tomography (APT) to frozen liquids is limited by difficulties in specimen preparation. Here, we report on the use of nanoporous Cu needles as a physical framework to hold water ice for investigation using APT. Nanoporous Cu needles are prepared by electropolishing and dealloying Cu-Mn matchstick precursors. Cryogenic scanning electron microscopy and focused ion beam milling reveal a hierarchical, dendritic, highly wettable microstructure. The atom probe mass spectrum is dominated by peaks of Cu+ and H(H2O)n+ up to n ≤ 3, and the reconstructed volume shows the protrusion of a Cu ligament into an ice-filled pore. The continuous Cu ligament network electrically connects the apex to the cryostage, leading to an enhanced electric field at the apex and increased cooling, both of which simplify the mass spectrum compared to previous reports.