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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fatty acid capped, metal oxo clusters as the smallest conceivable nanocrystal prototypes

Metal oxo clusters of the type M 6 O 4 (OH) 4 (OOCR) 12 (M = Zr or Hf) are valuable building blocks for materials science. Here, we synthesize a series of zirconium and hafnium oxo clusters with ligands that are typically used to stabilize oxide nanocrystals (fatty acids with long and/or branched chains). The fatty acid capped oxo clusters have a high solubility but do not crystallize, precluding traditional purification and single-crystal XRD analysis. We thus develop alternative purification strategies and we use X-ray total scattering and Pair Distribution Function (PDF) analysis as our main method to elucidate the structure of the cluster core. We identify the correct structure from a series of possible clusters (Zr3, Zr4, Zr6, Zr12, Zr10, and Zr26). Excellent refinements are only obtained when the ligands are part of the structure model. Further evidence for the cluster composition is provided by nuclear magnetic resonance (NMR), infrared spectroscopy (FTIR), thermogravimetry analysis (TGA), and mass spectrometry (MS). We find that hydrogen bonded carboxylic acid is an intrinsic part of the oxo cluster. Using our analytical tools, we elucidate the conversion from a Zr6 monomer to a Zr12 dimer (and vice versa), induced by carboxylate ligand exchange. Finally, we compare the catalytic performance of Zr12-oleate clusters with oleate capped, 5.5 nm zirconium oxide nanocrystals in the esterification of oleic acid with ethanol. The oxo clusters present a five times higher reaction rate, due to their higher surface area. Since the oxo clusters are the lower limit of downscaling oxide nanocrystals, we present them as appealing catalytic materials, and as atomically precise model systems. In addition, the lessons learned regarding PDF analysis are applicable to other areas of cluster science as well, from semiconductor and metal clusters, to polyoxometalates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Proposed crystal structure of carbadox, C 11 H 10 N 4 O 4

A model for the crystal structure of carbadox has been generated and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Carbadox crystallizes in space groupP2 1 (#4) witha= 13.8155(3),b= 21.4662(1),c= 16.3297(3) Å,β= 110.0931(7)°,V= 4548.10(3) Å 3 , andZ= 16. The crystal structure is characterized by approximately parallel stacking of the eight independent carbadox molecules parallel to thebc-plane. There are two different molecular configurations of the eight carbadox molecules; five are in the lower-energy configuration and three are in a ~10% higher-energy configuration. This arrangement likely achieves the lowest-energy crystalline packing via hydrogen bonding. Hydrogen bonds link the molecules both within and between the planes. Each of the amino groups forms a N–H⋯O hydrogen bond to an oxygen atom of the 1,4-dioxidoquinoxaline ring system of another molecule. The result is four pairs of hydrogen-bonded molecules, which form rings with graph setR2,2(14). Variation in specimen preparation can affect the preferred orientation of particles considerably. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

Materials Science↗
DOE OSTI2024

Increase in the effective viscosity of polyethylene under extreme nanoconfinement

Understanding polymer transport in nanopores is crucial for optimizing heterogeneously catalyzed processes in polymer upcycling and fabricating high-performance nanocomposite films and membranes. Although confined polymer dynamics have been extensively studied, the behavior of polyethylene (PE)—the most widely used commodity polymer—in pores smaller than 20 nm remains largely unexplored. We investigate the effects of extreme nanoconfinement on PE transport using capillary rise infiltration in silica nanoparticle packings with average pore radii ranging from ~1 to ~9 nm. Using in situ ellipsometry and the Lucas–Washburn model, we discover a previously unknown inverse relationship between effective viscosity (η eff ) and average pore radius (R pore ). Additonally, we determine that PE transport under these extreme conditions is primarily governed by physical confinement, rather than pore surface chemistry. We refine an existing theory to provide a generalized formalism to describe the polymer transport dynamics over a wide range of pore radii (from 1 nm and larger). Our results offer valuable insights for optimizing catalyst supports in polymer upcycling and improving infiltration processes for nanocomposite fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient structure-factor modeling for crystals with multiple components

Diffraction intensities from a crystallographic experiment include contributions from the entire unit cell of the crystal: the macromolecule, the solvent around it and eventually other compounds. These contributions cannot typically be well described by an atomic model alone, i.e . using point scatterers. Indeed, entities such as disordered (bulk) solvent, semi-ordered solvent ( e.g. lipid belts in membrane proteins, ligands, ion channels) and disordered polymer loops require other types of modeling than a collection of individual atoms. This results in the model structure factors containing multiple contributions. Most macromolecular applications assume two-component structure factors: one component arising from the atomic model and the second one describing the bulk solvent. A more accurate and detailed modeling of the disordered regions of the crystal will naturally require more than two components in the structure factors, which presents algorithmic and computational challenges. Here an efficient solution of this problem is proposed. All algorithms described in this work have been implemented in the computational crystallography toolbox ( CCTBX ) and are also available within Phenix software. These algorithms are rather general and do not use any assumptions about molecule type or size nor about those of its components.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2023

Effect of Production Bias on Radiation-Induced Segregation in Ni-Cr Alloys

We present an in-depth investigation into the Radiation-Induced Segregation (RIS) phenomenon in Ni-Cr alloys. All the pivotal factors affecting RIS such as surface’s absorption efficiency, grain size, production bias, dose rate, temperature, and sink density were systematically studied. Through comprehensive simulations, the individual and collective impacts of these factors were analyzed, enabling a refined understanding of RIS. A notable finding was the significant influence of production bias on point defects’ interactions with grain boundaries/surfaces, thereby playing a crucial role in RIS processes. Production bias alters the neutrality of these interactions, leading to a preferential absorption of one type of point defect by the boundary and consequent establishment of distinct surface-mediated patterns of point defects. These spatial patterns further result in non-monotonic spatial profiles of solute atoms near surfaces/grain boundaries, corroborated by experimental observations. In particular, a positive production bias, signifying a higher production rate of vacancies over interstitials, drives more Cr depletion at the grain boundary. Moreover, a temperature-dependent production bias must be considered to recover the experimentally reported dependence of RIS on temperature. The severity of radiation damage and RIS becomes more pronounced with increased production bias, dose rate, and grain size, while high temperatures or sink density suppress the RIS severity. Model predictions were validated against experimental data, showcasing robust qualitative and quantitative agreements. The findings pave the way for further exploration of these spatial dependencies in subsequent studies, aiming to augment the comprehension and predictability of RIS processes in alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hydrogen bond arrangements in (H 2 O) 20, 24, 28 clathrate hydrate cages: Optimization and many-body analysis

Here we provide a detailed study of hydrogen bonding arrangements, relative stability, residual entropy, and an analysis of the many-body effects in the (H 2 O) 20 (D-cage), (H 2 O) 24 (T-cage), and (H 2 O) 28 (H-cage) hollow cages making up structures I (sI) and II (sII) of clathrate hydrate lattices. Based on the enumeration of the possible hydrogen bonding networks for a fixed oxygen atom scaffold, the residual entropy (S 0 ) of these three gas phase cages was estimated at 0.754 82, 0.754 44, and 0.754 17 · Nk b , where N is the number of molecules and k b is Boltzmann’s constant. A previously identified descriptor of enhanced stability based on the relative arrangement and connectivity of nearest-neighbor fragments on the polyhedral water cluster [strong-weak-effective-bond model] also applies to the larger hollow cages. The three cages contain a maximum of 7, 9, and 11 such preferable arrangements of trans nearest dimer pairs with one “free” OH bond on the donor molecule (t1d dimers). The Many-Body Expansion (MBE) up to the 4-body suggests that the many-body terms vary nearly linearly with the cluster binding energy. Using a hierarchical approach of screening the relative stability of networks starting from optimizations with the TIP4P, TTM2.1-F, and MB-pol classical potentials, subsequently refining at more accurate levels of electronic structure theory (DFT and MP2), and finally correcting for zero-point energy, we were able to identify a group of four low-lying isomers of the (H 2 O) 24 T-cage, two of which are antisymmetric and the other two form a pair of antipode configurations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Solving tricky quantum optics problems with assistance from large language models

The capabilities of modern artificial intelligence (AI) as a “scientific collaborator” are explored by engaging it with three nuanced problems in quantum optics: state populations in optical pumping, resonant transitions between decaying states (the Burshtein effect), and degenerate mirrorless lasing. Through iterative dialogue, the authors observe that AI models–when prompted and corrected–can reason through complex scenarios, refine their answers, and provide expert-level guidance, closely resembling the interaction with an adept colleague. The findings highlight that AI can democratize access to sophisticated modeling and analysis, shifting the focus in scientific practice from technical mastery to the generation and testing of ideas, and reducing the time for completing research tasks from days to minutes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Competing ionization and dissociation: Extension of the energy-dependent frame transformation to the gerade symmetry of H 2

This article solves two major tasks that frequently arise in the theory of electron collisions with a target molecular cation. First, it extends the energy-dependent frame transformation (EDFT) treatment, which is needed to map fixed-nuclei electron-molecule scattering matrices into an energy-dependent laboratory-frame scattering matrix with vibrational channel indices. The EDFT mapping can now be carried out even when the target molecule possesses multiple low-energy potential curves, significantly transcending previous applications. Second, it implements a method to extract the rest of the full laboratory-frame scattering matrix, i.e., the columns and rows describing input and/or output dissociation channels. The treatment is benchmarked in this article against the essentially exact solution of a refined two-dimensional model of the singlet gerade Σ symmetry of H 2 . Our tests demonstrate that the theory accurately maps fixed-nuclei scattering information, of the type provided by existing electron-molecule computer codes, into a laboratory-frame scattering matrix that includes both ionization and dissociation. Furthermore, this treatment can provide a general framework applicable to a broad class of electron collision processes involving diatomic target ions, suitable for an accurate description of challenging processes such as dissociative recombination.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Predicting interface structure using the minima hopping method

Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ⁢3(111)[110] and Σ⁢3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ⁢3(111)[110] Γ=1, ±0.5 and Σ⁢3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.

density functional theory↗
DOE OSTI2026

ROADRUNNER uranium nitride MiniFuel: Experimental design, fabrication and pre-irradiation baseline characterization for accelerated burnup testing

Uranium nitride (UN) is a promising fuel candidate for advanced reactor systems owing to its high uranium density and thermal conductivity; however, its qualification remains constrained by the scarcity of well-controlled irradiation performance data. Here, to address this limitation, the ROADRUNNER (Research On ADvancing the peRformance of UraNium Nitrides in Extreme enviRonments) campaign employs the MiniFuel platform in the High Flux Isotope Reactor (HFIR) to enable accelerated burnup irradiation testing under tightly controlled and largely isothermal conditions. This paper presents the experimental design, fuel fabrication, and pre-irradiation baseline characterization of the ROADRUNNER UN MiniFuel campaign. Thirty-six UN minidisc specimens were fabricated with systematically varied as-fabricated density (86–96% of theoretical density), carbon impurity content (961–5240 ppm), oxygen content (≤ ∼2000 ppm), and grain size (2.5–24 μm). The irradiation matrix spans nominal fuel temperatures of 873 K, 1173 K, and 1473 K and target burnups of 3.75%, 6.0%, and 7.5% fissions per initial metal atom (FIMA). Neutronic and thermal analyses were performed to define specimen-specific burnup accumulation and temperature histories, establishing the boundary conditions for subsequent in-pile behavior. Comprehensive pre-irradiation characterization—including dimensional metrology, density verification, impurity analysis, X-ray diffraction, Raman spectroscopy, scanning electron microscopy, X-ray computed tomography, and confocal profilometry—provides a detailed baseline for post-irradiation examination. Pre-irradiation data were further used to generate predictive estimates of fission gas release and swelling using existing empirical correlations. This quantitative comparison reveals substantial inter-model divergence at intermediate and elevated temperatures that exceeds propagated input uncertainties, highlighting structural gaps in the historical irradiation database. The ROADRUNNER irradiation campaign is currently underway in HFIR, with initial firs cycle completed in late 2025 and remaining targets scheduled through 2027. The experimental design and baseline dataset presented here establish the framework needed to interpret forthcoming post-irradiation measurements and to provide discriminating data for the validation and refinement of physics-based UN fuel performance models.

Lopes, Denise Adorno [Oak Ridge National Laborator↗
DOE OSTI2023

Probing Molecular Packing of Amorphous Pharmaceutical Solids Using X-ray Atomic Pair Distribution Function and Solid-State NMR

The structural investigation of amorphous pharmaceuticals is of paramount importance in comprehending their physicochemical stability. However, it has remained a relatively underexplored realm primarily due to the limited availability of high-resolution analytical tools. Here in this study, we utilized the combined power of X-ray pair distribution functions (PDFs) and solid-state nuclear magnetic resonance (ssNMR) techniques to probe the molecular packing of amorphous posaconazole and its amorphous solid dispersion at the molecular level. Leveraging synchrotron X-ray PDF data and employing the empirical potential structure refinement (EPSR) methodology, we unraveled the existence of a rigid conformation and discerned short-range intermolecular C–F contacts within amorphous posaconazole. Encouragingly, our ssNMR 19 F– 13 C distance measurements offered corroborative evidence supporting these findings. Furthermore, employing principal component analysis on the X-ray PDF and ssNMR data sets enabled us to gain invaluable insights into the chemical nature of the intermolecular interactions governing the drug–polymer interplay. These outcomes not only furnish crucial structural insights facilitating the comprehension of the underlying mechanisms governing the physicochemical stability but also underscore the efficacy of synergistically harnessing X-ray PDF and ssNMR techniques, complemented by robust modeling strategies, to achieve a high-resolution exploration of amorphous structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enabling Real-time Scattering Data Analysis with Scalable Optimization [Slides]

Diffraction experiments produce datasets with rich multidimensional physics information such as microstructure, equations of state, crystal structure, elastoplastic properties, and other key inputs to LANL mission-essential multiphysics models. This information is typically extracted through a process called Rietveld refinement, which involves selecting appropriate models of the instrument, crystal structure, and microstructure, identifying suitable starting values, and then fitting often hundreds of model parameters using a sequence of empirical parameter turnon/off sequences within a non-global gradient-based optimization. Extensive user expertise is required to properly setup a refinement, identify appropriate models, and select initial parameter values close to truth, such that the refinement will yield parameter values that are optimally predictive. This is a very tedious manual process performed far after the beamline campaign has ended. As facilities have become capable of generating larger volumes of data, the limitation in throughput due to Rietveld refinement has led to a dramatic increase in unanalyzed data as opposed to an intended increase in new science. In our FY22 TED, we demonstrated an integrated toolset providing near real-time automated Rietveld analysis. If this toolset can be optimized to provide automated Rietveld analysis in real-time, this could alleviate the bottleneck in unanalyzed diffraction data, aid in decision-making during experiments, and increase efficiency of the facility.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Improved Neutron Lifetime Measurement with UCN$\tau$

In this work, we report an improved measurement of the free neutron lifetime $\tau_n$ using the UCN$\tau$ apparatus at the Los Alamos Neutron Science Center. We count a total of approximately 38×10 6 surviving ultracold neutrons (UCNs) after storing in UCN$\tau$’s magnetogravitational trap over two data acquisition campaigns in 2017 and 2018. We extract $\tau$ n from three blinded, independent analyses by both pairing long and short storage time runs to find a set of replicate $\tau$ n measurements and by performing a global likelihood fit to all data while self-consistently incorporating the β-decay lifetime. Both techniques achieve consistent results and find a value $\tau$ n =877.75±0.28 stat +0.22/–0.16 syst s. With this sensitivity, neutron lifetime experiments now directly address the impact of recent refinements in our understanding of the standard model for neutron decay.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Towards interpretable Cryo-EM: disentangling latent spaces of molecular conformations

Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Photo- and Electro-Induced Hadron Production from Nuclei at Jefferson Laboratory

Understanding many-body knockout processes is crucial for nuclear physics, particularly in photo- and electro-induced reactions. In turn, understanding two- and three-body forces, including higher-order forces, is vital for a complete understanding of atoms. We present photo-induced many-proton knockout processes, with multiplicities from 1 to 6, using 12C, CH2, and C4H9OH targets in the g9a FROST dataset. Our analysis covers photon energies from 600 to 4500 MeV, significantly expanding current world data. Comparing our experimental data to the state-of-the-art GiBUU model offers a new challenge in the model’s theoretical description of many-body processes. GiBUU reasonably describes the data at lower photon energies but struggles at higher energies and missing masses, likely due to missing processes, such as initial 3-pion photoproduction. Our results will inform future developments in describing proton knockout processes, indicating GiBUU’s overall reasonable description of many-proton knockout data up to around 2.2 GeV. We also assess various electro-induced reactions using 2D, 12C, and 40Ar targets in the RGM dataset. Our results, obtained at electron beam energies of 2, 4, and 6 GeV, are compared in detail to GENIE and GiBUU, two widely used theory models in neutrino oscillation experiments. Discrepancies between model predictions and experimental data underscore the need for refining the two theoretical models. Despite discrepancies, GiBUU provides a more accurate modelling of electro-induced reactions, especially for 40Ar - crucial for future neutrino oscillation facilities such as DUNE. Understanding the fundamental nuclear physics involved in neutrino-nuclei interactions is essential for reducing the systematic uncertainties in extracting neutrino oscillation parameters. Many-body processes significantly contribute to the background processes observed in neutrino-nuclei interactions, hence the results from both analyses are crucial for developing the theoretical framework for the underlying nuclear physics.

Williams, Rhidian↗
DOE OSTI2024

Solvent organization in the ultrahigh-resolution crystal structure of crambin at room temperature

Ultrahigh-resolution structures provide unprecedented details about protein dynamics, hydrogen bonding and solvent networks. The reported 0.70 Å, room-temperature crystal structure of crambin is the highest-resolution ambient-temperature structure of a protein achieved to date. Sufficient data were collected to enable unrestrained refinement of the protein and associated solvent networks using SHELXL . Dynamic solvent networks resulting from alternative side-chain conformations and shifts in water positions are revealed, demonstrating that polypeptide flexibility and formation of clathrate-type structures at hydrophobic surfaces are the key features endowing crambin crystals with extraordinary diffraction power.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗