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1. Mending cracks atom-by-atom in rutile TiO2 with electron beam radiolysis

   https://doi.org/10.1038/s41467-023-41781-x  

   Guo, Silu; Yun, Hwanhui; Nair, Sreejith; Jalan, Bharat; Mkhoyan, K. Andre (December 2023, Nature Communications)

   Rich electron-matter interactions fundamentally enable electron probe studies of materials such as scanning transmission electron microscopy (STEM). Inelastic interactions often result in structural modifications of the material, ultimately limiting the quality of electron probe measurements. However, atomistic mechanisms of inelastic-scattering-driven transformations are difficult to characterize. Here, we report direct visualization of radiolysis-driven restructuring of rutile TiO₂under electron beam irradiation. Using annular dark field imaging and electron energy-loss spectroscopy signals, STEM probes revealed the progressive filling of atomically sharp nanometer-wide cracks with striking atomic resolution detail. STEM probes of varying beam energy and precisely controlled electron dose were found to constructively restructure rutile TiO₂according to a quantified radiolytic mechanism. Based on direct experimental observation, a “two-step rolling” model of mobile octahedral building blocks enabling radiolysis-driven atomic migration is introduced. Such controlled electron beam-induced radiolytic restructuring can be used to engineer novel nanostructures atom-by-atom. 

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2. Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise

   https://doi.org/10.1017/S1431927622000356  

   Manzorro, Ramon; Xu, Yuchen; Vincent, Joshua L.; Rivera, Roberto; Matteson, David S.; Crozier, Peter A. (January 2022, Microscopy and Microanalysis)

   Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision. 

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3. Direct calibration of laser intensity via Ramsey interferometry for cold atom imaging

   https://doi.org/10.1364/OE.488710  

   Altuntaş, Emine; Spielman, I. B. (January 2023, Optics Express)

   A majority of ultracold atom experiments utilize resonant absorption imaging techniques to obtain the atomic density. To make well-controlled quantitative measurements, the optical intensity of the probe beam must be precisely calibrated in units of the atomic saturation intensity I sat . In quantum gas experiments, the atomic sample is enclosed in an ultra-high vacuum system that introduces loss and limits optical access; this precludes a direct determination of the intensity. Here, we use quantum coherence to create a robust technique for measuring the probe beam intensity in units of I sat via Ramsey interferometry. Our technique characterizes the ac Stark shift of the atomic levels due to an off-resonant probe beam. Furthermore, this technique gives access to the spatial variation of the probe intensity at the location of the atomic cloud. By directly measuring the probe intensity just before the imaging sensor our method in addition yields a direct calibration of imaging system losses as well as the quantum efficiency of the sensor. 

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4. Electron beam characterization via quantum coherent optical magnetometry

   https://doi.org/10.1063/5.0234219  

   DeStefano, Nicolas; Pegahan, Saeed; Ramaswamy, Aneesh; Aubin, Seth; Averett, T; Camsonne, Alexandre; Malinovskaya, Svetlana; Mikhailov, Eugeniy E; Park, Gunn; Zhang, Shukui; et al (December 2024, Applied Physics Letters)

   We present a quantum optics-based detection method for determining the position and current of an electron beam. As electrons pass through a dilute vapor of rubidium atoms, their magnetic field perturbs the atomic spin's quantum state and causes polarization rotation of a laser resonant with an optical transition of the atoms. By measuring the polarization rotation angle across the laser beam, we recreate a 2D projection of the magnetic field and use it to determine the e-beam position, size, and total current. We tested this method for an e-beam with currents ranging from 30 to 110 μA. Our approach is insensitive to electron kinetic energy, and we confirmed that experimentally between 10 and 20 keV. This technique offers a unique platform for noninvasive characterization of charged particle beams used in accelerators for particle and nuclear physics research. 

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5. Benchmark Tests of Atom Segmentation Deep Learning Models with a Consistent Dataset

   https://doi.org/10.1093/micmic/ozac043  

   Wei, Jingrui; Blaiszik, Ben; Scourtas, Aristana; Morgan, Dane; Voyles, Paul M (December 2022, Microscopy and Microanalysis)

   Abstract The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models. 

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