560 Search Results

Supercritical fluids are characterized by unique thermodynamic properties. One of these properties is the existence of two-component dynamics that is associated with distinct low-frequency and high-frequency vibrational responses of the fluid. However, the origin of this behavior remains unknown. By combining inelastic X-ray scattering and molecular dynamics simulations, we show that this behavior can be connected to density heterogeneities arising from molecular clusters. Analyses of measurements and molecular trajectories suggest that the two-component dynamics emerges due to distinct momentum fluctuations of clustered and unbound molecules. This connection between clusters and two-component dynamics highlights the importance of molecular-structural heterogeneities in supercriticalmore » fluids, colloids, and condensed-matter systems.« less

The growing demand for rare earth elements (REEs) has prompted interest in their recovery from alternative sources such as coal fly ash (CFA). This study explores the ionic liquid (IL) betainium bis(trifluoromethylsulfonyl)imide, [Hbet][Tf₂N], for selective extraction of REEs from leachates of a Class C CFA. While previous studies have demonstrated the effectiveness of using [Hbet][Tf₂N] to extract REEs from different types of CFA in direct ash-IL systems, this study investigates four CFA leachates prepared using HCl, HNO₃, H₂SO₄, and citrate. Extraction experiments were conducted across varying pH levels and with additives such as ascorbic acid and betaine. Among the systemsmore » tested, [Hbet][Tf₂N] achieved REE recoveries of 51% and 47% from the HCl and citrate leachates, respectively, comparable to 49% REE recovery in ash-IL extraction. Co-extraction of bulk elements was significantly reduced in the leachate-IL systems. Optimal REE extraction occurred near pH 11, and addition of ascorbic acid effectively suppressed iron co-extraction without compromising REE recovery. Recycling experiments demonstrated that [Hbet][Tf₂N] retains its performance over five cycles with manageable losses. These results reveal the promise of [Hbet][Tf₂N] for effectively recovering REEs from leachates of solid wastes, highlighting its applicability as a sustainable strategy for other aqueous REE feedstocks.« less

Electronic ground states are of central importance in chemical simulations, but have remained beyond the reach of efficient classical algorithms except in cases of weak electron correlation or one-dimensional spatial geometry. We introduce a hybrid quantum-classical eigenvalue solver that constructs a wavefunction ansatz from a linear combination of matrix product states in rotated orbital bases, enabling the characterization of strongly correlated ground states with arbitrary spatial geometry. The energy is converged via a gradient-free generalized sweep algorithm based on quantum subspace diagonalization, with a potentially exponential speedup in the off-diagonal matrix element contractions upon translation into compact quantum circuits ofmore » linear depth in the number of qubits. Chemical accuracy is attained in numerical experiments for both a stretched water molecule and an octahedral arrangement of hydrogen atoms, achieving substantially better correlation energies compared to a unitary coupled-cluster benchmark, with orders of magnitude reductions in quantum resource estimates and a surprisingly high tolerance to shot noise. This proof-of-concept study suggests a promising new avenue for scaling up simulations of strongly correlated chemical systems on near-term quantum hardware.« less

This work presents an analytical method to solve the heat conduction equation in three dimensions for problems consisting of multilayer concentric spheres. The method can be used to treat time-varying heat conduction problems where the heat source that drives the transient is time-invariant. Equally applicable to all Poisson-type problems with concentric spherical geometry, the method consists of representing the solution as a summation of weighted eigenfunctions. The weights for each eigenfunction are computed algebraically. Previous work has already established the core constituents of the methodology. The current work augments the existing methods by including consideration of nonzero interface resistance betweenmore » layers and explicit discussion on the boundary condition homogenization required to treat inhomogeneous problems. Also, two demonstration problems are presented. One demonstration problem is based on the method of manufactured solutions and therefore allows for comparison with exact expressions for the solution temperature distribution. The second, more complex, demonstration problem relies on the finite element method for comparisons. The expected convergence behavior is observed for both demonstration problems.« less

We employed cryogenic X-ray Raman spectroscopy to investigate the early-stage decomposition of the high explosive molecule hexanitrohexaazaisowurtzitane (CL-20). By systematically varying the radiation dose under cryogenic conditions, we induced the decomposition of the molecule using ionizing radiation and observed the evolution of spectral features at the carbon, nitrogen, and oxygen K edges. Through extensive first-principles calculations, we identified key intermediates in the early stages of the decomposition process, resulting from C–C and C–N bond cleavage which leads to the opening of the internal cage structure. A detailed analysis of spectral trends and fingerprints provided evidence supporting N–NO₂ homolytic cleavage asmore » the primary initial decomposition pathway. The combination of advanced core-level spectroscopy methods and state-of-the-art theoretical calculations enabled a comprehensive characterization of the molecular changes induced by controlled radiation dose exposures. In conclusion, our findings establish a benchmark for understanding the decomposition chemistry of high-explosive materials, offering important insights into their stability and reactivity under extreme conditions.« less

Solid-solid phase change materials (SS-PCMs) hold promise for energy storage/dissipation in batteries and energetic materials. Yet, phase change kinetics for SS-PCMs undergoing metastable to semi-stable/stable phase transformations remain relatively ill-studied because trapping metastable phases remain challenging. Recently, we demonstrated the kinetic entrapment and stabilization of a highly disordered and amorphous Al-oxide phase m-AlO_x@C (x~2.5-3.0) via laser ablation synthesis in solution (LASiS). We report here, to our knowledge, the first chemical kinetics analysis for S-S phase transition of the m-AlO₃@C nanocomposites (< 5–8 nm sizes) into semi-stable equilibrium alumina phases (θ/γ-Al₂O₃) via disproportionation reaction, while releasing excess trapped gases. Our results indicatemore » the atomic density of the AlO₃ structures to be ~5–10 times less than that of the final Al₂O₃ phases, which led to the hypothesis of a volume shrinkage process during their phase transition. Temperature-dependent X-ray diffraction studies reveal the high-temperature phase transition for m-AlO₃ → θ/γ-Al₂O₃ to follow contracting volume kinetics model, thereby validating our earlier hypothesis. Using the geometric volume contraction model, reaction kinetics analyses from Arrhenius plots reveal the activation energy barrier for the phase transition to be ~270±11 kJ/mol. This makes the activation energy barrier nearly identical to the oxidation of micron-sized Al particles.« less

Shock-induced detonation of insensitive high explosives based on 1,3,5-triamino-2,4,6-trinitrobenzene starts with formation of hot spots at microstructural defects but has eluded atomistic modeling treatment at micron length scales. To this end, we performed multimicron scale all-atom molecular dynamics (MD) simulations of hot spots that form during the collapse of cylindrical pores with diameters between 10 and 300 nm. Our MD simulations show that hot spots formed at pores larger than 20 nm exhibit temperature fields with scale-invariant features for sizes up to at least 300 nm. Through a continuum-based grain-scale modeling framework, we span and extend beyond the size scalesmore » currently accessible to MD and find that hot spot scale invariance is a general feature that arises when the mechanical strength is insensitive to strain rate. Finally, our results demonstrate the applicability of all-atom MD to simulate the complicated dynamical evolution of micron-sized systems and bolster confidence in insights from MD simulations of materials that exhibit strength with negligible rate dependence over the relevant intervals.« less

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet),more » MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.« less

Fine roots are key to ecosystem-scale nutrient, carbon (C), and water cycling, yet our understanding of fine root trait variation within and among tropical forests, one of Earth’s most C-rich ecosystems, is limited. We characterized root biomass, morphology, nutrient content, and arbuscular mycorrhizal fungal (AMF) colonization to 1.2 m depths across four distinct lowland Panamanian forests, and related root characteristics to soil C stocks. We hypothesized that: (H1) Fine root characteristics vary consistently with depth across seasonal tropical forests, with deeper roots exhibiting more exploratory traits, such as for deep water acquisition; (H2) fine root characteristics vary among tropical forestsmore » mainly in surface soils, where resource availability also varies. Here we found consistent variation with depth across the four forests, including decreased root biomass, root tissue density, and AMF, and increased specific root length. Among the forests, there was variation in some fine root characteristics, including greater surface root biomass and lower SRL in the wettest forest, and smaller fine root diameter in the driest forest. We also found that root characteristics were related to total soil C stocks, which were positively related to root biomass and negatively related to specific root length. These results indicate emergent properties of root variation with depth across tropical forests, and show site-scale variation in surface root characteristics. Future work could explore the flexibility in root characteristics under changing conditions such as drought.« less

In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing the hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High-density carbon capsules made for ICF experiments at the National Ignition Facility are precision polished to achieve surface smoothness on the order of a few nanometers as well as to minimize isolated defects in the form of pits. Given the critical role of this process,more » we are developing smart manufacturing techniques with the goal of elevating the efficiency of this process. Our approach is to use MEMS (micro-electromechanical systems)–based sensors to capture the fine vibration signals generated during the polishing process and combine them with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, in this work, we describe the multiple fronts we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used to ensure the progress of the run as well as a means for faster optimization.« less