Atomic force microscopy (AFM) measurements of dihedral angles are conducted for the first time to characterize the ratio of the twin-boundary energy (γΤ) to the surface free energy (γS). In plane, twin morphology is measured with AFM, verified by scanning electron microscopy, optical microscopy, and found to be consistent. The chemical composition and homogeneity of annealed Cu10 wt%Zn sample are confirmed by energy-dispersive spectroscopy. AFM data indicate that the average depth and height of the grooves and peaks are 118 ± 45 and 158 ± 45 nm, respectively. Surface roughness parameters, Sq and Sa, are measured by a factor of two to four less than the depth and height of the twin boundaries. Both surface roughness parameters are less with no planar defects present compared with selected areas containing twin boundaries. The average dihedral angle is found to be 167 ± 5° for the grooves and 193 ± 4° for the peaks. The twin to surface interfacial free energy ratio, γT/γS, is 0.0018. The comparison of AFM-based results to the other method-based results obtained on pure metals is discussed.

This work introduces NexusLIMS, an electron microscopy laboratory information management system designed and implemented by the Office of Data and Informatics and the Materials Science and Engineering Division at NIST for a multi-user electron microscopy co-op facility. NexusLIMS comprises network infrastructure, shared information technology resources, a custom software package to harvest and extract experimental information and construct experimental metadata records, and an intuitive web-based user-facing interface for searching, browsing, and examining research data. These metadata records conform to the Nexus Experiment schema, which is introduced in this work. The NexusLIMS suite of tools requires minimal input and adjustments to user behavior, instead relying on existing organizational procedures and the collection of information from a multitude of sources to construct a complete picture and record of a research experiment. The underlying infrastructure and design considerations for a multi-user data management system are discussed. The core codebase for NexusLIMS is made publicly available alongside this work, and its modular design encourages the adaptation of the presented methods at other research organizations.

The fine microstructure resulting from the martensitic transformation drives many of the desired mechanical properties for quench-and-temper steels. The scale of martensite features correlates with austenite grain size, such that finer austenite grains produce finer martensitic structures. Measuring prior austenite grain (PAG) size from observations of the hierarchical martensitic structure using etch-based techniques remains challenging, especially for fine-grained specimens with low C and P content. This has driven an interest in our field in reconstructing prior austenite microstructure from electron backscatter diffraction (EBSD) data collected on tempered martensite. However, reliably reconstructing the data is not straightforward because (1) up to 24 variants can theoretically be observed from a single PAG; (2) the shear-induced plasticity that arises from martensite formation produces significant scatter in the pole figure; (3) there is inherent noise in Hough-indexed EBSD data; (4) annealing twins exist within many PAGs, producing martensite variants of similar orientations when compared with the parent grains; and (5) variation in austenite orientation can occur across a single PAG. To the author's knowledge, the majority of reconstruction algorithms published to date utilize either point-to-point or flood-fill approaches, which can produce artifacts due to the relatively high probability of adjacent EBSD observations falling within any reasonable tolerance angle with 24 variants in a parent and four annealing twin orientations. Therefore, we propose a probabilistic austenite reconstruction technique based on a clustering algorithm known as graph cutting. We demonstrate its application to steels and a binary ferrous alloy, then validate the results against chemical etching and retained austenite orientations. The graph cutting technique utilizes the misorientation distribution of martensitic variants and the potential austenite orientations for each observation, as estimated via Bayesian inference of the experimental orientation relationship and its scatter. It is found that this technique produces accurate reconstructions with identification of annealing twins in the PAGs, even in the presence of a large fraction of poorly indexed data points.

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.

While application of clustering algorithms to atom probe tomography data have enabled quantification of solute clusters in terms of number density, size, and subcomposition there exist other properties (e.g., volume, surface area, and composition) that are better determined by defining an interface between the cluster and the surrounding matrix. The limitation in composition results from an ion selection step where the expected matrix ion types are omitted from the cluster search algorithm to enhance the contrast between the matrix and cluster and to reduce the complexity of the search. Previously, composition determination within solute clusters has utilized a secondary envelopment and erosion step on top of conventional methods such as maximum separation. In this work, we present a novel stochastic method that combines the particle identification fidelity of a conventional clustering algorithm with the analytical flexibility of mesh-based approaches through the generation of alpha shapes for each identified cluster. The corresponding mesh accounts for concave components of the clusters and determines the volume and surface area of the clusters; additionally, the mesh boundary is utilized to update the total composition according to the internal ions.

Measurements of highly radioactive actinides such as Np, Pu, Am, or Cm in as-fabricated or irradiated nuclear fuels are crucial to improve the safety and performance of the nuclear fuel cycle, but also necessary to find solutions to the environmental impact of long-lived actinides in nuclear waste. Currently, the most common technique of measuring the quantity and distribution of actinides in nuclear fuel is electron probe microanalysis (EPMA). However, the accurate analysis of actinides by EPMA is accompanied by numerous obstacles such as interferences in the M-line X-ray region, absorption edges, and the lack of reference materials. Therefore, the measurement protocol of radioactive and irradiated samples requires further investigation and most importantly facilitation. Here, we establish a guideline for precise quantification of actinide elements (Th, U, Np, Pu, Am, and Cm) in fabricated or spent nuclear fuels. For this purpose, we have characterized actinide-bearing reference samples to visualize spectral interferences, list peak, and background positions and employ the standardless calibration curve method for the measurements of Am and Cm. This newly derived analytical protocol was successfully tested on an irradiated fuel sample and can be now implemented as an improved guideline for the quantification of actinides.

The invention of silicon drift detectors has resulted in an unprecedented improvement in detection efficiency for energy-dispersive X-ray (EDX) spectroscopy in the scanning transmission electron microscope. The result is numerous beautiful atomic-scale maps, which provide insights into the internal structure of a variety of materials. However, the task still remains to understand exactly where the X-ray signal comes from and how accurately it can be quantified. Unfortunately, when crystals are aligned with a low-order zone axis parallel to the incident beam direction, as is necessary for atomic-resolution imaging, the electron beam channels. When the beam becomes localized in this way, the relationship between the concentration of a particular element and its spectroscopic X-ray signal is generally nonlinear. Here, we discuss the combined effect of both spatial integration and sample tilt for ameliorating the effects of channeling and improving the accuracy of EDX quantification. Both simulations and experimental results will be presented for a perovskite-based oxide interface. We examine how the scattering and spreading of the electron beam can lead to erroneous interpretation of interface compositions, and what approaches can be made to improve our understanding of the underlying atomic structure.

The characteristics of electron channeling contrast (ECC) of stacking faults in a [101] single-crystal 316L fcc (face-centered cubic) stainless steel have been evaluated. Channeling contrast was analyzed from a series of ECC images taken as a function of sample tilt at ~0.1° increments across the (111) Kikuchi band. The most relevant imaging parameters of the fault contrast, namely the number of fringes, fringe intensity, and fringe spacing, were analyzed under different channeling conditions. The present work shows that the channeling contrast of stacking faults exhibits strong dependence on the sign and magnitude of the deviation parameter, w (w = s ξg, where s is the excitation vector and ξg is the extinction distance). This effect has strong influence on channeling conditions for fault imaging and g.R analysis for determining the nature of a stacking fault. g.R analysis was evaluated by the method developed by Gevers et al. (1963) on g-reversion experiments of ECC images of a stacking fault configuration. The interplay between the stacking fault nature and ε-martensite is analyzed.

Atomic force microscope (AFM) based nanomanipulation has been proved to be a possible way to assemble various nanoparticles into complex patterns and devices. To achieve an efficient and full-automatic nanomanipulation, the nanoparticles as well as other features on the substrate must be quickly identified by the computer. This work focuses on an autodetection method for flexible nanowires based on deep learning. The You Only Look Once (YOLO) network is applied to find all movable nanowires in the AFM images. A series of morphology transformation algorithms, including an adaptive threshold edge detection, are applied to refine the skeletons of the nanowires. The bidirectional long short-term memory model with conditional random field layer (BI-LSTM-CRF) is proposed to precisely determine the posture and position of the detected nanowires. Benefiting from these algorithms, our detecting program is able to automatically detect the nanowires of different morphology with nanometer resolution and with over 80% reliability in the testing set. The detecting results are less affected by the image quality, which demonstrates good robustness of this algorithm.

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

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.

New interest in microscopic studies of ferroelectric materials with low piezoelectric coefficient, $d_{33}^\ast$, has emerged after the discovery of ferroelectric properties in HfO2 thin films, which are the main candidate for the next generation of nonvolatile ferroelectric memory. The study of the microscopic structure of ferroelectric HfO2 capacitors is crucial to get insights into the device behavior and performance. However, a small $d_{33}^\ast$ of ferroelectric HfO2 films leads to a low piezoresponse, especially in band excitation piezoresponse force microscopy (BE-PFM). In this work, we have implemented the BE-PFM technique with an increased scanning rate, thus improving this versatile tool for weak ferroelectrics. The acceleration of measurement was achieved by focusing excitation into a narrow frequency band and tuning the central frequency on-the-fly using an online real-time model estimation by fitting a complex BE response. The tracking of the contact resonance frequency was implemented using a pure mechanical cantilever response acquired in BE atomic force acoustic microscopy. To obtain optimal excitation parameters, we perform statistical analysis by minimizing estimator variance. The measurement precision of several PFM techniques was compared both by the simulation and experimentally using a Hf0.5Zr0.5O2-based ferroelectric capacitor.

Recent work has revived interest in the scattering matrix formulation of electron scattering in transmission electron microscopy as a stepping stone toward atomic-resolution structure determination in the presence of multiple scattering. We discuss ways of visualizing the scattering matrix that make its properties clear. Through a simulation-based case study incorporating shot noise, we shown how regularizing on this continuity enables the scattering matrix to be reconstructed from 4D scanning transmission electron microscopy (STEM) measurements from a single defocus value. Intriguingly, for crystalline samples, this process also yields the sample thickness to nanometer accuracy with no a priori knowledge about the sample structure. The reconstruction quality is gauged by using the reconstructed scattering matrix to simulate STEM images at defocus values different from that of the data from which it was reconstructed.

The ability to accurately and precisely measure the thickness of biomaterial constructs is critical for characterizing both specific dimensional features and related mechanical properties. However, in the absence of a standardized approach for thickness measurements, a variety of imaging modalities have been employed, which have been associated with varying limits of accuracy, particularly for ultrathin hydrated structures. Electron microscopy (EM), a commonly used modality, yields thickness values for extensively processed and nonhydrated constructs, potentially resulting in overestimated mechanical properties, including elastic modulus and ultimate tensile strength. Confocal laser scanning microscopy (CLSM) has often been used as a nondestructive imaging alternative. However, published CLSM-derived image analysis protocols use arbitrary signal intensity cutoffs and provide minimal information regarding thickness variability across imaged surfaces. To address the aforementioned limitations, we present a standardized, user-independent CLSM image acquisition and analysis approach developed as a custom ImageJ macro and validated with collagen-based scaffolds. In the process, we also quantify thickness discrepancies in collagen-based scaffolds between CLSM and EM techniques, further illustrating the need for improved strategies. Employing the same image acquisition protocol, we also demonstrate that this approach can be used to estimate the surface roughness of the same scaffolds without the use of specialized instrumentation.