Unusual nanoscale amorphization of metallic chromium interfacing with SiC under high energy irradiation
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Engineering topics
Publications and source records attributed to Szlufarska, Izabela.
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Abstract Radiation and corrosion can be coupled to each other in non-trivial ways and such coupling is of critical importance for the performance of materials in extreme environments. However, it has been rarely studied in ceramics and therefore it is not well understood to what extent these two phenomena are coupled and by what mechanisms. Here, we discover that radiation-induced chemical changes at grain boundaries of ceramics can have a significant (and positive) impact on the corrosion resistance of these materials. Specifically, we demonstrate using a combination of experimental and simulation studies that segregation of C to grain boundaries of silicon carbide leads to improved corrosion resistance. Our results imply that tunning of stoichiometry at grain boundaries either through the sample preparation process or via radiation-induced segregation can provide an effective method for suppressing surface corrosion.
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Formation of He bubbles can have a significant effect on the microstructural evolution and properties of irradiated materials. In this report, we use atomistic simulations based on machine learning potentials to investigate the fundamental behavior of He bubbles in Be, with a specific focus on the shape, stability, and diffusivity of bubbles. Stability of He bubbles is quantified in terms of formation energies, which are determined as a function of the ratio of He/V. We find that He bubbles become unstable with respect to plastic deformation through punch-out dislocations around the bubble when the He/V ratio is larger than ~1.25, and the punch-out process induces the change of the regular bubble shape. In general, the bubble shape of He in Be is found to be ellipsoid-like. It is also found that for a fixed He/V ratio, the bubble attracts vacancies to become larger in size. If the bubble size is constant, the bubble attracts additional He atoms until the punch-out reaction occurs. The dominant diffusion mechanism of He bubbles changes from surface diffusion to volume diffusion as the temperature is increased, with a crossover occurring at about 900 K.
Fluoride salts are prospective materials for applications in some next-generation nuclear reactors and their thermophysical properties at various conditions are of interest. Experimental measurement of the properties of these salts is often difficult and, in some cases, unfeasible due to challenges from high temperatures, impurity control, and corrosivity. Therefore, accurate theoretical methods are needed for fluoride salt property prediction. In this work, we used moment tensor potentials (MTP) to approximate the potential energy surface of eutectic FLiBe (66.6% LiF – 33.3% BeF2) predicted by the ab initio (DFT-D3) method. Here, we then used the developed potential and molecular dynamics to obtain several thermophysical properties of FLiBe, including radial distribution functions, density, self-diffusion coefficients, thermal expansion, specific heat capacity, bulk modulus, viscosity, and thermal conductivity. Our results show that the MTP potential approximates the potential energy surface accurately and the overall approach yields very good agreement with experimental values. The converged fitting can be obtained with less than 600 configurations generated from DFT calculations, which data can be generated in just 1200 core hours on today's typical processors. The MTP potential is faster than many machine learning potentials and about one order of magnitude slower than widely used empirical molten salt potentials such as Tosi/Fumi.
Abstract Transition metal dichalcogenides (TMDs), especially in two-dimensional (2D) form, exhibit many properties desirable for device applications. However, device performance can be hindered by the presence of defects. Here, we combine state of the art experimental and computational approaches to determine formation energies and charge transition levels of defects in bulk and 2D MX 2 (M = Mo or W; X = S, Se, or Te). We perform deep level transient spectroscopy (DLTS) measurements of bulk TMDs. Simultaneously, we calculate formation energies and defect levels of all native point defects, which enable identification of levels observed in DLTS and extend our calculations to vacancies in 2D TMDs, for which DLTS is challenging. We find that reduction of dimensionality of TMDs to 2D has a significant impact on defect properties. This finding may explain differences in optical properties of 2D TMDs synthesized with different methods and lays foundation for future developments of more efficient TMD-based devices.
Control of surface reactions is commonly achieved by modification of surface electronic structures. Here, we discover an alternative pathway for controlling surface reactions by tuning the mechanical stiffness of the underlying material. We find that in addition to the typically assumed surface electronic contribution right at the reactive site, the contribution from the deformation of the bulk region plays a vital role in controlling surface reactions. The underlying mechanism is an elastic relaxation of the solid, which depends on the material’s stiffness and can be modified by tuning bulk stoichiometry. The effect of bulk stiffness on surface reactions has been demonstrated by considering hydrogen scission reaction and oxygen incorporation reaction during corrosion of amorphous SiC in water and air, respectively. Finally, our results imply that tuning of bulk stiffness by modifying stoichiometry can provide an effective method for controlling surface reactions.
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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 when 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.
The next generation of high power targets will use more complex geometries, novel materials, and new concepts (like flowing granular materials); however, the current numerical approaches will not be sufficient to converge towards a reliable target design that satisfies the physical requirements. We will discuss what can be improved in the next 10 years in target modeling to support high power (MW class) targets.
This work demonstrate that reconfiguration of amorphous oxide layers drives the assembly of oxide-based and freestanding nanomembranes into 3D structures with a radius of a few hundred nanometers. Reconfiguration-driven assembly is a versatile approach to impart large strains and strain gradients in a broad palette of complex oxides. This capability allows for manipulating and enhancing ferroelectricity, flexoelectricity, piezoelectricity, superconductivity, and ferromagnetism in complex oxides. Moreover, by the approach presented in this work, strain-engineered oxides are obtained in the form of 3D structures that can be fabricated in parallel on any substrate, including large-area and single crystalline semiconductor substrates. This approach will create a vast expanse of possibilities to investigate and leverage strain-tunable effects in amorphous, poly-crystalline, and single-crystalline complex oxides. Reconfiguration-driven assembly of NMs also allows combining different materials in a radial geometry and through scalable processes. For example, radial superlattices of Si (or GaAs) and various complex oxides or alternating layers of different complex oxides (e.g., SrTiO 3 and LaAlO 3 ) could be fabricated by release and heating of bilayer NMs. Here, a broad palette of electronic band structures and spin–orbit interactions could then be obtained by tailoring the curvature of the self-assembled NMs and the materials that the heterostructure comprises.
Transition metal borides, which are three-dimensional (3D) layered materials containing covalently bonded B networks, have shown a number of excellent properties, such as radiation resistance and the ability to act as a diffusion barrier in integrated circuits. However, defect behavior, which controls many of the materials’ properties, has remained unknown in these materials. In this work, we investigate the effects of the B networks on the defect chemistry in both binary borides (CrB, Cr 3 B 4 , Cr 2 B 3 ) and ternary MAB phases (Cr 2 AlB 2 , Cr 3 AlB 4 , Cr 4 AlB 6 ) using first-principles calculations. We find that increasing the number of B rings in the structure leads to lower formation energies and higher concentrations of Frenkel pairs. The results can be explained by the fact that the strongest Cr-B bond is weakened when borides have more B rings, leading to a reduction in the formation energy of Cr and B vacancies. Also, the bonds associated with Cr atoms bonded within B rings are softer in structures containing more B rings, which allows Cr interstitials to form with a lower energy cost and contributes to an increase in the concentration of Cr interstitials.