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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The Significant Role of New Particle Composition and Morphology on the HNO 3 -Driven Growth of Particles down to Sub-10 nm
New particle formation and growth greatly influence air quality and the global climate. Recent CERN Cosmics Leaving OUtdoor Droplets (CLOUD) chamber experiments proposed that in cold urban atmospheres with highly supersaturated HNO 3 and NH 3 , newly formed sub-10 nm nanoparticles can grow rapidly (up to 1000 nm h –1 ). Here, we present direct observational evidence that in winter Beijing with persistent highly supersaturated HNO 3 and NH 3 , nitrate contributed less than ~14% of the 8–40 nm nanoparticle composition, and overall growth rates were only ~0.8–5 nm h –1 . To explain the observed growth rates and particulate nitrate fraction, the effective mass accommodation coefficient of HNO 3 (α HNO 3 ) on the nanoparticles in urban Beijing needs to be 2–4 orders of magnitude lower than those in the CLOUD chamber. We propose that the inefficient uptake of HNO 3 on nanoparticles is mainly due to the much higher particulate organic fraction and lower relative humidity in urban Beijing. To quantitatively reproduce the observed growth, we show that an inhomogeneous “inorganic core–organic shell” nanoparticle morphology might exist for nanoparticles in Beijing. Finally, this study emphasized that growth for nanoparticles down to sub-10 nm was largely influenced by their composition, which was previously ignored and should be considered in future studies on nanoparticle growth.
Sustaining Irrigation Supplies through Immobilization of Groundwater Arsenic In Situ
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Structure and Negative Thermal Expansion in Zr 0.3 Sc 1.7 Mo 2.7 V 0.3 O 12
A 2 M 3 O 12 -based materials have received considerable attention owing to their wide range of negative thermal expansion (NTE) and chemical flexibility toward novel materials design. However, the structure and NTE mechanism remain challenging. In this paper, Zr 4+ and V 5+ are used as a unit to compensatorily replace Sc 3+ and Mo 6+ in Sc 2 Mo 3 O 12 to tune its thermal expansion. Its crystal structure, phase transition, NTE property, and corresponding mechanisms are studied by high-resolution synchrotron X-ray diffraction, powder X-ray diffraction, ultralow-frequency Raman spectroscopy, and density functional theory calculations. The results show that Zr 0.3 Sc 1.7 Mo 2.7 V 0.3 O 12 adopts an orthorhombic (Pbcn) structure at room temperature, with V atoms occupying the position of Mo1 atoms and Zr atoms occupying the position of Sc atoms, and transforms to monoclinic (P2 1 /a) structure at similar to ~133 K (45 K lower than that of Sc 2 Mo 3 O 12 ). It exhibits excellent NTE in a broader range. Most of the phonon modes below 350 cm -1 have negative Grüneisen parameters, of which the lowest and next-lowest frequency (38.5 and 45.8 cm -1 ) optical phonon modes arising from the translational vibrations of the Sc/Zr and Mo/V atoms in the plane of the nonlinear linkage Sc/Zr-O-Mo/V have the largest and next-largest negative Grüneisen parameters and positive total anharmonicity, and contribute most to the NTE.
Understanding Negative Thermal Expansion of Zn[subscript 2]GeO[subscript 4] through Local Structure
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Sample Dependence of Magnetism in the Next-Generation Cathode Material LiNi 0.8 Mn 0.1 Co 0.1 O 2
We present a structural and magnetic study of two batches of polycrystalline LiNi 0.8 Mn 0.1 Co 0.1 O 2 (commonly known as Li NMC 811), a Ni-rich Li ion battery cathode material, using elemental analysis, X-ray and neutron diffraction, magnetometry, and polarized neutron scattering measurements. We find that the samples, labeled S1 and S2, have the composition Li 1–x Ni 0.9+x–y Mn y Co 0.1 O 2 , with x = 0.025(2), y = 0.120(2) for S1 and x = 0.002(2), y = 0.094(2) for S2, corresponding to different concentrations of magnetic ions and excess Ni 2+ in the Li + layers. Both samples show a peak in the zero-field-cooled (ZFC) dc susceptibility at 8.0(2) K, but the temperature at which the ZFC and FC (field-cooled) curves deviate is substantially different: 64(2) K for S1 and 122(2) K for S2. The ac susceptibility measurements show that the transition for S1 shifts with frequency whereas no such shift is observed for S2 within the resolution of our measurements. Our results demonstrate the sample dependence of magnetic properties in Li NMC 811, consistent with previous reports on the parent material LiNiO 2 . Here, we further establish that a combination of experimental techniques is necessary to accurately determine the chemical composition of next-generation battery materials with multiple cations.
Facile Fabrication of CeO[subscript 2]-Al[subscript 2]O[subscript 3] Hollow Sphere with Atomically D
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High-Pressure and High-Temperature Synthesis and In Situ High-Pressure Synchrotron X-ray Diffraction
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Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements
In this work, we integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La–Si–P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La–Si–P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La 5 SiP 3 and La 2 SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La–Si–P structures from our ML-guided predictions, a number of low-energy La–X–P phases (X = Ge, Sn, Pb) are predicted.
Pt-Driven Stable and Metastable Ternary Compounds with Tunable Partial Flat Bands in Immiscible Si–Sn System
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Reinforcement Learning Configuration Interaction
Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. Furthermore, we explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory.
Stochastic Real-Time Second-Order Green’s Function Theory for Neutral Excitations in Molecules and Nanostructures
We present a real-time second-order Green’s function (GF) method for computing excited states in molecules and nanostructures, with a computational scaling of O(N e 3 ), where N e is the number of electrons. The cubic scaling is achieved by adopting the stochastic resolution of the identity to decouple the 4-index electron repulsion integrals. To improve the time propagation and the spectral resolution, we adopt the dynamic mode decomposition technique and assess the accuracy and efficiency of the combined approach for a chain of hydrogen dimer molecules of different lengths. We find that the stochastic implementation accurately reproduces the deterministic results for the electronic dynamics and excitation energies. Furthermore, we provide a detailed analysis of the statistical errors, bias, and long-time extrapolation. Overall, the approach offers an efficient route to investigate excited states in extended systems with open or closed boundary conditions.
Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset
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Approximate Exponential Integrators for Time-Dependent Equation-of-Motion Coupled Cluster Theory
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A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-Time Dyson Expansion
Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on subfemtosecond time scales. The interpretation of experimental results is often enhanced by parallel numerical studies, which can provide insight and validation for experimental hypotheses. However, developing a theoretical framework for simulating time-resolved spectra remains a significant challenge. The most suitable approach involves the many-body nonequilibrium Green's function formalism, which accounts for crucial dynamical many-body correlations during time evolution. While these dynamical correlations are essential for observing emergent behavior in time-resolved spectra, they also render the formalism prohibitively expensive for large-scale simulations. Substantial effort has been devoted to reducing this computational cost─through approximations and numerical techniques─while preserving the key dynamical correlations. The ultimate goal is to enable first-principles simulations of time-dependent systems ranging from small molecules to large, periodic, multidimensional solids. Here, in this perspective, we outline key challenges in developing practical simulations for time-resolved spectroscopy, with a particular focus on Green's function methodologies. We highlight a recent advancement toward a scalable framework: the real-time Dyson expansion (RT-DE) [Phys. Rev. Lett. 2024, 133, 226902]. We introduce the theoretical foundation of RT-DE and discuss strategies for improving scalability, which have already enabled simulations of system sizes beyond the reach of previous fully dynamical approaches. We conclude with an outlook on future directions for extending RT-DE to first-principles studies of dynamically correlated, nonequilibrium systems.