Structural analyses of an RNA stability element interacting with poly(A)
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Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.
Finite element solutions are only valid if the information inputted into the models is a valid assumption. This is why it is important to determine if the model behaves as expected especially in the material response. Material models that consider deformation in the elastic and plastic regions are desirable. As a material deforms plastically, hardening will occur until the ultimate strength is reached due to the reduction of the cross sectional area in a uniaxial tensile test environment. A phenomenological approximation of this behavior which is known as power law hardening (plh) can be expressed as σ = σ y + K $ϵ^{n}_{p}$ where K and n are empirically determined constants, and σ y is the proportional limit of the material. The first objective of this study is to verify the behavior for a calibrated plh material model for selective deviatoric hexahedral elements when applied to a different material stock. The Sierra SM input for this calibrated model can be found in the appendix. The second objective of this study is to determine the differences in failure characteristics for considerably large shell elements versus that of fine hexahedral elements.
Scanning electron microscopy (SEM), combined with energy-dispersive spectroscopy (EDS), is an extensively used technique for in-depth microstructural analysis. Here, we present the EDS-Phase Segmentation (EDS-PhaSe) tool that enables phase segmentation and phase analysis using the EDS elemental map images. Importantly, it converts the EDS map images into estimated composition maps for calculating markers of selective elemental redistribution in the scanned area and creates a phase-segmented micrograph while providing approximate fraction and composition of each identified phase. EDS-PhaSe offers two unique advantages. Firstly, it enables the direct processing of EDS elemental map images without requiring any raw or proprietary data/software, thereby allowing the analysis of EDS results available in the published literature as images. Secondly, it enables segmentation and analysis of phases even when the phase contrast is missing in backscattered micrographs, assisting in correlating the XRD and SEM-EDS data as shown in this work for a AlCoCrFeNi high-entropy alloy.
Iron redistribution in the intestine after total body irradiation is an established phenomenon. However, in the literature, there are no reports about the use of X-ray fluorescence microscopy or equivalent techniques to generate semi-quantitative 2D maps of iron in sectioned intestine samples from irradiated mice. In this work, we used X-ray fluorescence microscopy (XFM) to map the elemental content of iron as well as phosphorus, sulfur, calcium, copper and zinc in tissue sections of the small intestine from eight-week-old BALB/c male mice that developed gastrointestinal acute radiation syndrome (GI-ARS) in response to exposure to 8 Gray of gamma rays. Seven days after irradiation, we found that the majority of the iron is localized as hot spots in the intercellular regions of the area surrounding crypts and stretching between the outer perimeter of the intestine and the surface cell layer of villi. In addition, this study represents our current efforts to develop elemental cell classifiers that could be used for the automated generation of regions of interest for analyses of X-ray fluorescence maps. Once developed, such a tool will be instrumental for studies of effects of radiation and other toxicants on the elemental content in cells and tissues. While XFM studies cannot be conducted on living organisms, it is possible to envision future scenarios where XFM imaging of single cells sloughed from the human (or rodent) intestine could be used to follow up on the progression of GI-ARS.
The pivotal role of uranium and plutonium fluorides in the nuclear fuel cycle, particularly in the pyrochemical reduction process, is well recognized. Traditionally, the fluorination of uranium and plutonium materials relies on the use of highly toxic and corrosive gases (e.g., HF (g) , F 2(g) ). Herein, we present an alternative approach using the ionic liquid 1‑butyl‑3-methylimidazolium hexafluorophosphate ([Bmim][PF 6 ] (l) ) and/or hexafluorophosphoric acid (HPF 6(aq) ) as fluorinating agents for the ƒ-element oxalates M$^{III}_{2}$(C 2 O 4 ) 3 ∙ 9H 2 O (s) M$^{III}_{2}$(C 2 O 4 ) 3 ∙ 9H 2 O (s) (M III = Ce, Pu) and M IV (C 2 O 4 ) 2 ∙ 6H 2 O (s) (M IV = Th, U). Our findings demonstrate that [Bmim][PF 6 ] (l) and HPF 6(aq) enable the ionothermal fluorination of ƒ-element oxalates, resulting in the formation of anhydrous CeF 3(s) , ThF 4(s) , and UF 4(s) within 2 hours at 200 °C. This method also facilitates the partial fluorination of plutonium(III) oxalate, yielding a mixture of anhydrous PuF 3(s) and an unidentified phase. Overall, the ionothermal treatment approach offers a safer and more efficient means of producing anhydrous ƒ-element fluorides than conventional methods involving hazardous gases. In addition, we describe the morphology of UF 4(s) materials as a function of production route and demonstrate the presence of morphological signatures that could be used during a nuclear forensic investigation.
Light-element reactions at low energies in the resolved resonance region are important for a range of applications in basic and applied sciences including nuclear reactors, nonproliferation, cultural heritage, forensics and environmental control, rare event investigations and nuclear astrophysics. In this paper, we report on an effort to evaluate charged-particle cross sections in the resolved resonance region and produce evaluated nuclear data files for further processing and inclusion in evaluated data libraries. We discuss the open issues in R-matrix calculations as we extend to higher energies, such as dealing with the rapidly growing number of open channels and merging with the regime of smooth cross sections described by the statistical model, and present attempts to address these issues in neutron-induced reactions relevant to nuclear reactor applications.
Corrosion is considered a uniform thickness reduction design guideline of the maritime industry. However, additionally, the corroded and irregular morphology of the surface affects the steel's load-bearing capacity and its impact on the strength and elongation behaviour of the steel is not yet fully understood. These effects on the local behaviour of steel structures under tensile loading were investigated with tensile tests on naturally corroded steel specimens and nonlinear finite element simulations including the corroded surface morphology with a uniform surface idealation. The models also include the deformed specimen shape. The developed approach led to highly accurate parametric finite element models predicting the ultimate tensile strength and longitudinal position of fracture. The results show that all included aspects are essential for accurate simulations, while solely the maximum available surface resolution was not as decisive.
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Here, we present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.
The synthesis, structure, and solid-state UV–vis–NIR spectroscopy of four new f-element squarates, M 2 (C 4 O 4 ) 3 (H 2 O) 4 (M = Eu, Am, Cf) and Sm(C 4 O 4 )(C 4 O 3 OH)(H 2 O) 2 ·0.5H 2 O, four new cationic lanthanide squarate chlorides, [M 4 (C 4 O 4 ) 5 (H 2 O) 12 ]Cl 2 ·5H 2 O (M = Eu, Dy, Ho Er), and two new actinide squarate oxalates, M 2 (C 4 O 4 ) 2 (C 2 O 4 )(H 2 O) 4 (M = Am, Cf), are presented. All of the metal centers are trivalent. Single-crystal X-ray diffraction analysis reveals that M 2 (C 4 O 4 ) 3 (H 2 O) 4 and Sm(C 4 O 4 )(C 4 O 3 OH)(H 2 O) 2 ·0.5H 2 O have a two-dimensional sheet structure constructed from MO 7 (H 2 O) 2 monocapped square-antiprismatic (coordination number (CN) = 9) metal centers and SmO 6 (H 2 O) 2 square-antiprismatic (CN = 8) metal centers, respectively, whereas M 2 (C 4 O 4 ) 2 (C 2 O 4 )(H 2 O) 4 have a three-dimensional (3D) structure constructed from MO 7 (H 2 O) 2 monocapped square-antiprismatic (CN = 9) metal centers. Additionally, the cationic framework materials [M 4 (C 4 O 4 ) 5 (H 2 O) 12 ]Cl 2 ·5H 2 O have a 3D structure constructed from two crystallographically unique MO 5 (H 2 O) 3 square-antiprismatic (CN = 8) metal centers. In these structures, the squarate ligands bind to the metal centers with varying coordination modes and denticities. The results of this study provide another example of the nonparallel chemistry between the lanthanides and transplutonium elements. From the crystallographic data for the isotypic series M 2 (C 4 O 4 ) 3 (H 2 O) 4 (M = La–Nd, Sm, Eu) and the linear regression fit to a plot of the unit cell volume as a function of the cube of the ionic radius, the nine-coordinate ionic radius of Cf 3+ was determined to be 1.127 ± 0.003 Å. Lastly, computational analysis of the americium and californium complexes M 2 (C 4 O 4 ) 3 (H 2 O) 4 and M 2 (C 4 O 4 ) 2 (C 2 O 4 )(H 2 O) 4 reveals three important attributes: (i) the 5f orbitals are nonbonding in all cases, with the bonding differences occurring with the empty 6d orbitals; (ii) the Cf complexes exhibit more covalent character than their Am counterparts; and (iii) there is more covalent character in the squarate-oxalate complexes than in the squarate complexes.
The potential-pH diagram, a graphical representation of the thermodynamically predominant reaction products in aqueous corrosion, is originally proposed for the corrosion of pure metals. The original approach only leads to stoichiometric oxides and hydroxides as the oxidation products. However, numerous experiments show that non-stoichiometric oxide scales are prevalent in the aqueous corrosion of alloys. In the present study, a room temperature potential-pH diagram considering oxide solid solutions, as a generalization of the traditional potential-pH diagram with stoichiometric oxides, is constructed for an FCC single-phase multi-principal element alloy (MPEA) based on the CALculation of PHAse Diagram method. The predominant reaction products, the ions in aqueous solution, and the cation distribution in oxides are predicted. The oxide solid solution is stabilized by the mixing free energy (or mixing entropy) and the stabilizing effect becomes more significant as the temperature increases. Consequently, solid solution oxides are stable in large regions of the potential-pH diagram and the mixing free energy mostly affects the equilibrium composition of the stable oxides, while the shape of stable regions for oxides is mostly determined by the structure of the stable oxides. Agreements are found for Ni 2+ , Fe 2+ , and Mn 2+ between the atomic emission spectroelectrochemistry measurements and thermodynamic calculations, while deviations exist for Cr 3+ and Co 2+ possibly due to surface complexation with species such as Cl - and the oxide dissolution. By incorporating the solution models of oxides, the current work presents a general and more accurate way to analyze the reaction products during aqueous corrosion of MPEAs.
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Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. As such, in this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to ‘level’ the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental’ DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errorswere computed, even though therewas no model form error, because the filtering effect of theDIC enginewas neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental’ DIC datawere purposefully misaligned slightly fromthe FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In conclusion, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled’ to the DIC data can meaningful, quantitative error maps be computed.