37 Search Results

Abstract Amorphous titanium dioxide (TiO ₂ ) film coating by atomic layer deposition (ALD) is a promising strategy to extend the photoelectrode lifetime to meet the industrial standard for solar fuel generation. To realize this promise, the essential structure-property relationship that dictates the protection lifetime needs to be uncovered. In this work, we reveal that in addition to the imbedded crystalline phase, the presence of residual chlorine (Cl) ligands is detrimental to the silicon (Si) photoanode lifetime. We further demonstrate that post-ALD in-situ water treatment can effectively decouple the ALD reaction completeness from crystallization. The as-processed TiO ₂ film hasmore » a much lower residual Cl concentration and thus an improved film stoichiometry, while its uniform amorphous phase is well preserved. As a result, the protected Si photoanode exhibits a substantially improved lifetime to ~600 h at a photocurrent density of more than 30 mA/cm ² . This study demonstrates a significant advancement toward sustainable hydrogen generation.« less

The evolution of medium range ordering (MRO) and crystallization behavior of amorphous TiO ₂ films grown by atomic layer deposition (ALD) were studied using in situ four-dimensional scanning transmission electron microscopy. The films remain fully amorphous when grown at 120 °C or below, but they start showing crystallization of anatase phases when grown at 140 °C or above. The degree of MRO increases as a function of temperature and maximizes at 140 °C when crystallization starts to occur, which suggests that crystallization prerequires the development of nanoscale MRO that serves as the site of nucleation. In situ annealing of amorphous TiO ₂ filmsmore » grown at 80 °C shows enhancement of MRO but limited number of nucleation, which suggests that post-annealing develops only a small portion of MRO into crystal nuclei. The MRO regions that do not develop into crystals undergo structural relaxation instead, which provides insights into the critical size and degree of ordering and the stability of certain MRO types at different temperatures. In addition, crystallographic defects were observed within crystal phases, which likely negate corrosion resistance of the film. Our result highlights the importance of understanding and controlling MRO for optimizing ALD-grown amorphous films for next-generation functional devices and renewable energy applications.« less

Abstract Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiationsmore » are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.« less

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected usingmore » a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.« less

In this study, we use a random forest (RF) model to predict the critical cooling rate (R_C) for glass formation of various alloys from features of their constituent elements. The RF model was trained on a database that integrates multiple sources of direct and indirect R_C data for metallic glasses to expand the directly measured R_C database of less than 100 values to a training set of over 2000 values. The model error on 5-fold cross-validation (CV) is 0.66 orders of magnitude in K/s. The error on leave-out-one-group CV on alloy system groups is 0.59 log units in K/s whenmore » the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass systems for future study, but the model is most useful for the identification of alloy systems likely to contain good glass formers rather than detailed discovery of bulk glass composition regions within known glassy systems.« less

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Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopymore » data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.« less

During aqueous corrosion, atoms in the solid react chemically with oxygen, leading either to the formation of an oxide film or to the dissolution of the host material. Commonly, the first step in corrosion involves an oxygen atom from the dissociated water that reacts with the surface atoms and breaks near-surface bonds. In contrast, hydrogen on the surface often functions as a passivating species. Here, we discovered that the roles of O and H are reversed in the early corrosion stages on a Si-terminated SiC surface. O forms stable species on the surface, and chemical attack occurs by H thatmore » breaks the Si–C bonds. Here, this so-called hydrogen scission reaction is enabled by a newly discovered metastable bridging hydroxyl group that can form during water dissociation. The Si atom that is displaced from the surface during water attack subsequently forms H₂SiO₃, which is a known precursor to the formation of silica and silicic acid. This study suggests that the roles of H and O in oxidation need to be reconsidered.« less

Abstract Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correctmore » it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 −  R ² error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.« less