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The unstable OBrO radical, which might play a role in atmospheric chemistry, has been observed in the gas phase over a solid product of the O + Br subscript 2 reaction. Under certain conditions BrO, OBrO, and Br subscript 2 O could be observed simultaneously.
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Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.
Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.
Assessment of bone structural property changes due to loading regimens or pharmacological treatment typically requires destructive mechanical testing and sectioning. Our group has accurately and non-destructively estimated three dimensional cross-sectional areal properties (principal moments of inertia, Imax and Imin, and principal angle, Theta) of human cadaver long bones from pixel-by-pixel analysis of three non-coplanar densitometry scans. Because the scanner beam width is on the order of typical small animal diapbyseal diameters, applying this technique to high-resolution scans of rat long bones necessitates additional processing to minimize errors induced by beam smearing, such as dependence on sample orientation and overestimation of Imax and Imin. We hypothesized that these errors are correctable by digital image processing of the raw scan data. In all cases, four scans, using only the low energy data (Hologic QDR-1000W, small animal mode), are averaged to increase image signal-to-noise ratio. Raw scans are additionally processed by interpolation, deconvolution by a filter derived from scanner beam characteristics, and masking using a variable threshold based on image dynamic range. To assess accuracy, we scanned an aluminum step phantom at 12 orientations over a range of 180 deg about the longitudinal axis, in 15 deg increments. The phantom dimensions (2.5, 3.1, 3.8 mm x 4.4 mm; Imin/Imax: 0.33-0.74) were comparable to the dimensions of a rat femur which was also scanned. Cross-sectional properties were determined at 0.25 mm increments along the length of the phantom and femur. The table shows average error (+/- SD) from theory of Imax, Imin, and Theta) over the 12 orientations, calculated from raw and fully processed phantom images, as well as standard deviations about the mean for the femur scans. Processing of phantom scans increased agreement with theory, indicating improved accuracy. Smaller standard deviations with processing indicate increased precision and repeatability. Standard deviations for the femur are consistent with those of the phantom. We conclude that in conjunction with digital image enhancement, densitometry scans are suitable for non-destructive determination of areal properties of small animal bones of comparable size to our phantom, allowing prediction of Imax and Imin within 2.5% and Theta within a fraction of a degree. This method represents a considerable extension of current methods of analyzing bone tissue distribution in small animal bones.
The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.
A series of donor-acceptor acetylene compounds was synthesized in which systematic changes in both the conjugation length and the donor-acceptor strength were made. The effect of these structural changes on the spectroscopic and electronic properties of the molecules and, ultimately, on the measured second-order molecular hyperpolarizabilities (beta) was investigated. It was found that increases in the donor-acceptor strength resulted in increases in the magnitude of beta. For this class of molecules, the increase is dominated by the energy of the intramolecular charge-transfer transition, while factors such as the ground to excited-state dipole moment change and the transition-moment integral are much less important. Increasing the conjugation length from one to two acetylene linkers did not result in an increase in the value of beta; however, beta increased sharply in going from two acetylenes to three. This increase is attributed to the superposition of several nearly isoenergetic excited states.
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Our research is focused on fundamental studies related to the molecular design, synthesis, characterization and modeling of molecular-reinforced hybrid glass films for superior mechanical and fracture properties. Molecular-reinforced hybrid glass films exhibit unique electro-optical properties while maintaining excellent thermal stability. They have important technological application for emerging nanoscience and energy technologies including anti-reflective and ultra-barrier layers in photovoltaics, display technologies, as well as quantum technologies. However, they are often inherently brittle in nature, do not adhere well to adjacent substrates, and exhibit poor mechanical properties. Furthermore, a robust understanding of the underlying mechanisms of deformation, fracture and fatigue has been lacking particularly given their mechanically fragile nature. The related lack of structure-property relationships between molecular structure, mechanical properties and processing represents a fundamental challenge to their integration into device technologies, compromises their thermomechanical reliability and has thwarted efforts to improve mechanical and, particularly, fracture properties.
Abstract not provided
Abstract Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.
van der Waals antiferromagnets with chemical formula MPX 3 (M = V, Mn, Fe, Co, Ni, Cd; X = S, Se) are superb platforms for exploring the fundamental properties of complex chalcogenides, revealing their structure-property relations and unraveling the physics of confinement. Pressure is extremely effective as an external stimulus, able to tune properties and drive new states of matter. In this review, we summarize experimental and theoretical progress to date with special emphasis on the structural, magnetic, and optical properties of the MPX 3 family of materials. Under compression, these compounds host inter-layer sliding and insulator-to-metal transitions accompanied by dramatic volume reduction and spin state collapse, piezochromism, possible polar metal and orbital Mott phases, as well as superconductivity. Some responses are already providing the basis for spintronic, magneto-optic, and thermoelectric devices. We propose that strain may drive similar functionality in these materials.
Reported here are the synthesis, structural characterization, and luminescence properties of 11 novel UO 2 2+ /Ag + heterometallic complexes. Halogenated benzoic acids (2,6-dihalobenzoic acid (halo = F, Br), 3,5-dichlorobenzoic acid, and 3-halobenzoic acid (halo = Br, I)) and N-donor polycyclic ligands (2,2′-bipyridine, 2,2’;6′,2″-terpyridine, 1,10-phenanthroline, 2,2′-bipyrimidine) were employed to synthesize a set of compounds and induce structural diversity. The primary mode of coordination with the uranyl cation is hexagonal bipyramidal monomeric units with three halobenzoate ligands in the equatorial plane, though 1-D chains with pentagonal bipyramidal uranyl centers also form. The Ag + cations coordinate preferentially to the N-donor ligands and serve as counter-cations for the anionic uranyl motifs. The soft ligand character of the N-donor molecules is found to be a requirement for the inclusion of the Ag + cation into the structures. Anionic uranyl units and cationic silver units assemble via noncovalent interactions between π systems on adjacent rings and between halogens (when Br and I are present). Solid-state emission spectra display the usual uranyl band with superimposed vibronic fine structure, except for that of compound 1 , which shows emission from the 2,2′-bipyridine center. This family of compounds represents a substantial contribution to the already rich library of UO 2 2+ /Ag + compounds, and the synthetic parameters discussed within reveal a platform for the design of new heterometallic uranyl-containing materials.
It has been ten years since research on lead halide perovskites (LHPs) took off spectacularly and expanded into one of the most active fields today in chemistry, physics, materials science, and engineering. There is no doubt that the rapid growth of this field has been fueled by the exceptional performance of this class of materials in solar cells, light emitting devices, and other electronic, optoelectronic, and photonic devices. Such application oriented advance has been accompanied by major development in mechanistic understanding. Growing mechanistic studies on LHPs have revealed structure–property relationships including, among others, structural instability, unique defect chemistry, dynamic disorder and anharmonicity of phonons, local symmetry breaking, efficient screening of charge carriers in large polaron formation, and the Rashba effect due to spin–orbital coupling and the presence of local electric fields. This special issue collects the cutting edge of our understanding of why LHPs work and what lessons LHPs have taught us about structure and dynamics, in general.
Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+
Amorphous TiO 2 (a-TiO 2 ) is widely used in many fields, ranging from photo-electrochemistry to bio-engineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered TiO 2 with molecular dynamics. Our DP reproduces the structural properties of all (11) TiO 2 crystalline phases, predicts the densities and structure factors of molten and amorphous TiO 2 with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. Furthermore, it can be extended to model additional structures and compositions and can be thus of great value in the study of TiO 2 -based (nano-)materials.
Effective micro-structure properties of two-dimensional large repetitive frame-like structures are derived using straightforward combinations of energy methods of linear transformations. Once the actual structure is identified symmetry considerations are used in order to identify its independent property constants. The actual values of these constants are constructed according to a building block format which is carried out in the three consecutive steps: (1) all basic planar lattices are identified, (2) effective continuum properties are derived for each of these planar basic grids using energy methods, and (3) orthogonal transformations are finally used to determine the contribution of each basic set to the overall effective continuum properties of the structure.