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The selection of the correct convergence angle is essential for achieving the highest resolution imaging in scanning transmission electron microscopy (STEM). The use of poor heuristics, such as Rayleigh's quarter-phase rule, to assess probe quality and uncertainties in the measurement of the aberration function results in the incorrect selection of convergence angles and lower resolution. Here, we show that the Strehl ratio provides an accurate and efficient way to calculate criteria for evaluating the probe size for STEM. A convolutional neural network trained on the Strehl ratio is shown to outperform experienced microscopists at selecting a convergence angle from a single electron Ronchigram using simulated datasets. Generating tens of thousands of simulated Ronchigram examples, the network is trained to select convergence angles yielding probes on average 85% nearer to optimal size at millisecond speeds (0.02% of human assessment time). Qualitative assessment on experimental Ronchigrams with intentionally introduced aberrations suggests that trends in the optimal convergence angle size are well modeled but high accuracy requires a high number of training datasets. This near-immediate assessment of Ronchigrams using the Strehl ratio and machine learning highlights a viable path toward the rapid, automated alignment of aberration-corrected electron microscopes.
Hydrocarbon contamination plagues high-resolution and analytical electron microscopy by depositing carbonaceous layers onto surfaces during electron irradiation, which can render carefully prepared specimens useless. Increased specimen thickness degrades resolution with beam broadening alongside loss of contrast. The large inelastic cross-section of carbon hampers accurate atomic species detection. Oxygen and water molecules pose problems of lattice damage by chemically etching the specimen during imaging. These constraints on high-resolution and spectroscopic imaging demand clean, high-vacuum microscopes with dry pumps. Here, we present an open-hardware design of a high-vacuum manifold for transmission electron microscopy (TEM) holders to mitigate hydrocarbon and residual species exposure. We quantitatively show that TEM holders are inherently dirty and introduce a range of unwanted chemical species. Overnight storage in our manifold reduces contaminants by one to two orders of magnitude and promotes two to four times faster vacuum recovery. A built-in bakeout system further reduces contaminants partial pressure to below 10−10 hPa (Torr) (approximately four orders of magnitude down from ambient storage) and alleviates monolayer adsorption during a typical TEM experiment. We determine that bakeout of TEM holder with specimen held therein is the optimal cleaning method. Our high-vacuum manifold design is published with open-source blueprints, parts, and cost list.
Modern nanomaterials contain complexity that spans all three dimensions—from multigate semiconductors to clean energy nanocatalysts to complex block copolymers. For nanoscale characterization, it has been a long-standing goal to observe and quantify the three-dimensional (3D) structure—not just surfaces, but the entire internal volume and the chemical arrangement. Electron tomography estimates the complete 3D structure of nanomaterials from a series of two-dimensional projections taken across many viewing angles. Since its first introduction in 1968, electron tomography has progressed substantially in resolution, dose, and chemical sensitivity. In particular, scanning transmission electron microscope tomography has greatly enhanced the study of 3D nanomaterials by providing quantifiable internal morphology and spectroscopic detection of elements. Combined with recent innovations in computational reconstruction algorithms and 3D visualization tools, scientists can interactively dissect volumetric representations and extract meaningful statistics of specimens. This article highlights the maturing field of electron tomography and the widening scientific applications that utilize 3D structural, chemical, and functional imaging at the nanometer and subnanometer length scales.