Self-accelerated corrosion of nuclear waste forms at material interfaces.

The US plan for high-level nuclear waste includes the immobilization of long-lived radionuclides in glass or ceramic waste forms in stainless-steel canisters for disposal in deep geological repositories. Here we report that, under simulated repository conditions, corrosion could be significantly accelerated at the interfaces of different barrier materials, which has not been considered in the current safety and performance assessment models. Severe localized corrosion was found at the interfaces between stainless steel and a model nuclear waste glass and between stainless steel and a ceramic waste form. The accelerated corrosion can be attributed to changes of solution chemistry and local acidity/alkalinity within a confined space, which significantly alter the corrosion of both the waste-form materials and the metallic canisters. The corrosion that is accelerated by the interface interaction between dissimilar materials could profoundly impact the service life of the nuclear waste packages, which, therefore, should be carefully considered when evaluating the performance of waste forms and their packages. Moreover, compatible barriers should be selected to further optimize the performance of the geological repository system.

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks.

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

Does the real ReN2 have the MoS2 structure?

Rhenium nitride (ReN(2)) with the hexagonal MoS(2) structure was recently synthesized by metathesis reaction under high pressure. Here the calculated elastic and thermodynamic stabilities and chemical bonding show that the MoS(2) phase is unstable based on first-principles calculations. Meanwhile, the MoS(2)-type ReN(2) compound may be stabilized by nitrogen-vacancies from X-ray diffraction and supercell calculations. Structure searches identify a monoclinic C2/m phase for ReN(2), which is energetically more stable than previous predictions and MoS(2) structure over a wide range of pressures. Above 130 GPa, a tetragonal P4/mbm phase becomes favorable from enthalpy calculations. Both phases have superior mechanical properties, and their syntheses would have important applications fundamentally and technologically.

Advanced characterization-informed machine learning framework and quantitative insight to irradiated annular U-10Zr metallic fuels.

U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Since the Experimental Breeder Reactor-II in the late 1960s, researchers accumulated a considerable amount of experience and knowledge on fuel performance at the engineering scale. However, a mechanistic understanding of fuel microstructure evolution and property degradation during in-reactor irradiation is still missing due to a lack of appropriate tools for rapid fuel microstructure assessment and property prediction based on post irradiation examination. This paper proposed a machine learning enabled workflow, coupled with domain knowledge and large dataset collected from advanced post-irradiation examination microscopies, to provide rapid and quantified assessments of the microstructure in two reactor irradiated prototypical annular metal fuels. Specifically, this paper revealed the distribution of Zr-bearing secondary phases and constitutional redistribution across different radial locations. Additionally, the ratios of seven different microstructures at various locations along the temperature gradient were quantified. Moreover, the distributions of fission gas pores on two types of U-10Zr annular fuels were quantitatively compared.

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel.

Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel.

U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model's performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

Thermally-Conductive and Mechanically-Robust Graphene Nanoplatelet Reinforced UO2 Composite Nuclear Fuels.

Low thermal transport behavior along the radial direction of nuclear fuel pellets and pellet-cladding mechanical interaction significantly impact fuel performance and the safety of current nuclear energy systems. Here we report a new strategy of advanced fuel design in which highly thermally-conductive and mechanically-robust graphene nanoplatelets are incorporated into UO2 fuel matrix to improve fuel thermal-mechanical properties. The 2D geometry of the graphene nanoplatelets enables a unique lamellar structure upon fuel consolidation by spark plasma sintering. The thermal conductivity along the radial direction of the sintered fuel pellets at room temperature reaches 12.7 and 19.1 wm-1K-1 at 1 wt.% and 5 wt.% loadings of the graphene nanoplatelets, respectively, representing at least 74% and 162% enhancements as compared to pure UO2 fuel pellets. Indentation testing suggests great capability of the 2D graphene nanoplatelets to deflect and pin crack propagation, drastically improving the crack propagation resistance of fuel matrix. The estimated indentation fracture toughness reaches 3.5 MPa·m1/2 by 1 wt.% loading of graphene nano-platelets, representing a 150% improvement over 1.4 MPa·m1/2 for pure UO2 fuel pellets. Isothermal annealing of the composite fuel indicates that the graphene nano-platelet is able to retain its structure and properties against reaction with UO2 matrix up to 1150 °C.

Highly thermally conductive and mechanically strong graphene fibers.

Graphene, a single layer of carbon atoms bonded in a hexagonal lattice, is the thinnest, strongest, and stiffest known material and an excellent conductor of heat and electricity. However, these superior properties have yet to be realized for graphene-derived macroscopic structures such as graphene fibers. We report the fabrication of graphene fibers with high thermal and electrical conductivity and enhanced mechanical strength. The inner fiber structure consists of large-sized graphene sheets forming a highly ordered arrangement intercalated with small-sized graphene sheets filling the space and microvoids. The graphene fibers exhibit a submicrometer crystallite domain size through high-temperature treatment, achieving an enhanced thermal conductivity up to 1290 watts per meter per kelvin. The tensile strength of the graphene fiber reaches 1080 megapascals.

Flexible, thorn-like ZnO-multiwalled carbon nanotube hybrid paper for efficient ultraviolet sensing and photocatalyst applications.

We report fabrication of a flexible, thorn-like ZnO-multiwalled carbon nanotube (MWCNT) hybrid paper with high aspect ratio for efficient ultraviolet (UV) sensing and photocatalyst applications. The thorn-like ZnO-MWCNT hybrid paper was synthesized via atomic layer deposition (ALD) of a uniform ZnO thin film on the outside surface of the MWCNT followed by hydrothermal growth of ZnO branches. The hybrid paper achieved very high surface to volume ratio, which is favorable for photodetector and photocatalyst applications. A photodetector fabricated from the hybrid paper demonstrates a high sensitivity to UV light with a maximum photoresponsivity of 45.1 A W(-1) at 375 nm, corresponding to an external quantum efficiency as high as 14927%. The rise time and fall time of the UV photodetector are 29 ms and 33 ms, respectively, indicating fast transient response characteristics for the device. The high photoresponsivity and fast transient response are attributed to efficient carrier transport and collection efficiency of the hybrid paper. Besides, the thorn-like ZnO-MWCNT hybrid paper demonstrates excellent photocatalytic performance under UV irradiation, enabling photo-degradation of organic dyes such as Rhodamine B (RhB) within 90 minutes, with good recyclability.

Advanced phase change composite by thermally annealed defect-free graphene for thermal energy storage.

Organic phase change materials (PCMs) have been utilized as latent heat energy storage and release media for effective thermal management. A major challenge exists for organic PCMs in which their low thermal conductivity leads to a slow transient temperature response and reduced heat transfer efficiency. In this work, 2D thermally annealed defect-free graphene sheets (GSs) can be obtained upon high temperature annealing in removing defects and oxygen functional groups. As a result of greatly reduced phonon scattering centers for thermal transport, the incorporation of ultralight weight and defect free graphene applied as nanoscale additives into a phase change composite (PCC) drastically improve thermal conductivity and meanwhile minimize the reduction of heat of fusion. A high thermal conductivity of the defect-free graphene-PCC can be achieved up to 3.55 W/(m K) at a 10 wt % graphene loading. This represents an enhancement of over 600% as compared to pristine graphene-PCC without annealing at a comparable loading, and a 16-fold enhancement than the pure PCM (1-octadecanol). The defect-free graphene-PCC displays rapid temperature response and superior heat transfer capability as compared to the pristine graphene-PCC or pure PCM, enabling transformational thermal energy storage and management.

Structural and relative stabilities, electronic properties and possible reactive routing of osmium and ruthenium borides from first-principles calculations.

First-principles calculations are employed to provide a fundamental understanding of the structural features and relative stability, mechanical and electronic properties and possible reactive route for osmium and ruthenium borides. The structural searches and calculations of the formation enthalpy identify a low-energy monoclinic phase for OsB3 with P2(1)/m symmetry, an orthorhombic phase for OsB4 with Pmmn symmetry, an orthorhombic phase for RuB3 with Pnma symmetry and a hexagonal phase for RuB4 with P63/mmc symmetry. Also, the structure transition at high pressure is also predicted for MB3 and MB4 (M = Os and Ru). Moreover, among the borides, orthorhombic RuB3 and OsB4 phases are predicted to be potential hard materials with estimated Vickers hardness values of 26.3 and 31.3 GPa, respectively. The analysis on the electronic properties and crystal orbital Hamilton population shows that the directional boron-boron networks, together with the strong metal-boron bonds, are responsible for their excellent mechanical properties. Relative enthalpy calculations with respect to possible constituents are also investigated to assess the prospects for phase formation and an attempt at high-pressure synthesis is suggested to obtain osmium and ruthenium tri- and tetra-borides.