Using a mixed-methods approach, we assessed the effect of the coronavirus disease 2019 (COVID-19) pandemic on antimicrobial stewardship programs (ASPs) in Colorado hospitals. ASP leaders reported decreased time and resources, reduced rigor of stewardship interventions, inability to complete new initiatives, and interpersonal challenges. Stewardship activities may be threatened during times of acute resource pressure.

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.

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.

In this paper, the effect of atomic hydrogen on carbon impurity incorporation during the metalorganic-molecular-beam-epitaxy (MOMBE) growth of GaAs is studied. Atomic hydrogen was introduced into the MOMBE chamber during the growth by cracking molecular hydrogen with a high temperature cracker cell. Atomic hydrogen appears to be effective in reducing the background doping level of MOMBE-grown GaAs, presumably by reacting with hydrocarbon radicals. Background doping levels as low as 4 × 1014 cm−3 and room temperature hole mobilities as high as 430 cm2/V-sec were achieved. This result demonstrates that it is feasible to grow high quality GaAs films in MOMBE without using AsH3 or a high flux of As4by introducing atomic hydrogen into the chamber during the growth.

Experimental observations of the surface oxide chemistry of GaAs are reported for various commonly used chemical surface preparations. Auger electron spectroscopy (AES), X-ray photoelectron spectroscopy (XPS), and ellipsometry were employed to obtain information regarding the stoichiometry, depth distribution, and oxide growth kinetics of thin surface oxides. Previous observations of the segregation in depth of Ga and As oxides are corroborated. Arsenic oxides tend to be found near the surface while Ga2O3 is found near the GaAs-oxide interface. The presence of elemental As was frequently detected at this interface, as well. Surfaces essentially free from oxide are shown to be produced by certain chemical treatments, and the state of the surface in the solution is inferred. It is shown that the GaAs surface oxide stoichiometry can undergo several changes in a short time when exposed to water and air. In addition, the characterization of the oxidation of GaAs by ozone in the presence of intense ultraviolet illumination is reported. The oxide primarily consists of Ga2O3 and exhibits an interesting growth kinetics: the thickness of the oxide proceeds at first linearly, then logarithmically, then parabolically. This behavior is explained in terms of various mechanisms which are dominant at different thicknesses of oxide.

Experimental investigations of the redistribution of a thin (1nm) Sn marker layer lying between two thicker dissimilar layers (Ge and Si) during 360 keV As ion irradiation are reported. Several permutations of layer arrangements were tested, i.e. Si/Sn/Ge, Ge/Sn/Si, Si/Sn/Si and Ge/Sn/Ge. It was also found that in the dissimilar “diffusion” couples, the Sn drifts in the Ge rich direction, regardless of whether the Ge is on the surface side or the substrate side of the marker. This phenomemon of anisotropic transport is interpreted as a drift induced by a gradient in the “diffusion” coefficient. The radiation resistance of concentrated alloys is discussed in the light of this phenomenon.

Several mechanisms can cause atomic relocation as a result of ion irradiation. Experiments are reviewed that provide information on the atomic redistribution within a solid. Predictions of different models, when compared with these results, shed light on the dominant processes. Sucesses and problem areas are discussed and possible practical applications of the results are explored.