Abstract: The evolution of the uranium chemical state in uranium compounds, principally in the oxides, is of concern in the context of nuclear fuel degradation under storage and repository conditions, and in accident scenarios. The U–O system shows complicated phase relations between single-valence uranium dioxide (UO₂) and different mixed-valence compounds (<italic>e.g.</italic>U₄O₉, U₃O₇and U₃O₈). To try resolving the electronic structure associated with unique atomic positions, a combined application of diffraction and spectroscopic techniques, such as diffraction anomalous fine structure (DAFS), can be considered. Reported here is the application of two newly developed routines for assessing a DAFS data set, with the aim of refining the uranium X-ray dispersion corrections. High-resolution anomalous diffraction data were acquired from polycrystalline powder samples of UO₂(containing tetravalent uranium) and potassium uranate (KUO₃, containing pentavalent uranium) using synchrotron radiation in the vicinity of the U<italic>L</italic>₃edge (17.17 keV). Both routines are based on an iterative refinement of the dispersion corrections, but they differ in either using the intensity of a selection of reflections or doing a full-pattern (Rietveld method) refinement. The uranium dispersion corrections obtained using either method are in excellent agreement with each other, and they show in great detail the chemical shifts and differences in fine structure expected for tetravalent and pentavalent uranium. This approach may open new possibilities for the assessment of other, more complicated, materials such as mixed-valence compounds. Additionally, the DAFS methodology can offer a significant resource optimization because each data set contains both structural (diffraction) and chemical (spectroscopy) information, which can avoid the requirement to use multiple experimental stations at synchrotron sources.

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image

Abstract: The basal plane of graphene can function as a selective barrier that is permeable to protons but impermeable to all ions and gases, stimulating its use in applications such as membranes, catalysis and isotope separation. Protons can chemically adsorb on graphene and hydrogenate it, inducing a conductor–insulator transition that has been explored intensively in graphene electronic devices. However, both processes face energy barriersand various strategies have been proposed to accelerate proton transport, for example by introducing vacancies, incorporating catalytic metalsor chemically functionalizing the lattice. But these techniques can compromise other properties, such as ion selectivity or mechanical stability. Here we show that independent control of the electric field,<italic>E</italic>, at around 1 V nm⁻¹, and charge-carrier density,<italic>n</italic>, at around 1 × 10¹⁴ cm⁻², in double-gated graphene allows the decoupling of proton transport from lattice hydrogenation and can thereby accelerate proton transport such that it approaches the limiting electrolyte current for our devices. Proton transport and hydrogenation can be driven selectively with precision and robustness, enabling proton-based logic and memory graphene devices that have on–off ratios spanning orders of magnitude. Our results show that field effects can accelerate and decouple electrochemical processes in double-gated 2D crystals and demonstrate the possibility of mapping such processes as a function of<italic>E</italic>and<italic>n</italic>, which is a new technique for the study of 2D electrode–electrolyte interfaces.

Abstract: Within the framework of the nonlinear Ginzburg-Landau theory, we calculated the full phase diagram for a superconducting disk with radius R = 4 (T = 0) and we studied the behavior of the penetration and expulsion fields as a function of temperature for multivortex and giant vortex states.