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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chiral Kirigami for Bend‐Tolerant Reconfigurable Hologram with Continuously Variable Chirality Measures

Abstract Despite the commonality of static holograms, the holography with multiple information layers and reconfigurable grey‐scale images at communication frequencies remain a confluence of scientific challenges. One well‐known difficulty is the simultaneous modulation of phase and amplitude of electromagnetic wavefronts with a high modulation depth. A less appreciated challenge is scrambling of the information and images with hologram bending. Here, this work shows that chirality‐guided pixelation of plasmonic kirigami sheets enables tunable multiplexed holography at terahertz (THz) frequencies. The convex and concave structures with slanted Au strips exhibit gradual variations in geometries facilitating modulation of light ellipticity reaching 40 deg. Real‐time switching of 3D images of the letter “ M ” and the Mona Lisa demonstrates the possibility of complex grey‐scale information content and importance of continuously variable mirror asymmetry. Microscale chirality measures of each pixel experiences little change with bending while retaining controllable reconfigurability upon stretching, which translates to remarkable resilience of chiral holograms to bending. Simplicity of their design with local chirality measures opens the door to information technologies with fault‐tolerant THz encryption, wearable holographic devices, and new communication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Earth mover’s distance as a measure of CP violation

We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover’s) distance. The efficacy of the test statistic is shown via two examples of CP asymmetric distributions with varying sample sizes: the Dalitz distributions of B 0 → K + π – π 0 and of D 0 → π + π – π 0 decays. The windowed version of the Wasserstein distance test statistic is shown to have comparable sensitivity to CP violation as the commonly used energy test statistic, but also retains information about the localized distributions of CP asymmetry over the Dalitz plot. For large statistic datasets we introduce two modified Wasserstein distance based test statistics — the binned and the sliced Wasserstein distance statistics, which show comparable sensitivity to CP violation, but improved computing time and memory scalings. Finally, general extensions and applications of the introduced statistics are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

An analytic approximation to the covariance between pre- and post-reconstruction galaxy two-point statistics

We present a simple analytic approximation for the covariance between pre-reconstruction galaxy power spectrum measurements and post-reconstruction two-point correlation functions. This cross-covariance is essential for joint analyses that combine full-shape clustering information with baryon acoustic oscillation (BAO) measurements, as commonly performed in modern spectroscopic surveys. Our model builds on the disconnected contribution to the covariance and accounts for the damping of correlations due to the BAO reconstruction process. We validate our analytic prescription against numerical simulations from the Dark Energy Spectroscopic Instrument (DESI), testing both idealized cubic geometries and realistic survey configurations including complex footprints and fiber assignment effects. Despite neglecting survey window functions in the analytic calculation, we find excellent agreement with simulation-based covariances and demonstrate that cosmological parameter constraints are virtually unchanged when using our approximation. Our results show that the pre-post cross-covariance is sufficiently small that even approximate treatments are adequate for cosmological inference, opening a pathway toward fully analytic covariance matrices for next-generation galaxy surveys.

baryon acoustic oscillations↗
DOE OSTI2023

Multislice forward modeling of coherent surface scattering imaging on surface and interfacial structures

To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle X-ray scattering are commonly used to yield an averaged statistical structural information of the surface sample. Grazing incidence geometry can probe the absolute three-dimensional structural morphology of the sample if a highly coherent beam is used. Coherent surface scattering imaging (CSSI) is a powerful yet non-invasive technique similar to coherent X-ray diffractive imaging (CDI) but performed at small angles and grazing-incidence reflection geometry. A challenge with CSSI is that conventional CDI reconstruction techniques cannot be directly applied to CSSI because the Fourier-transform-based forward models cannot reproduce the dynamical scattering phenomenon near the critical angle of total external reflection of the substrate-supported samples. To overcome this challenge, we have developed a multislice forward model which can successfully simulate the dynamical or multi-beam scattering generated from surface structures and the underlying substrate. The forward model is also demonstrated to be able to reconstruct an elongated 3D pattern from a single shot scattering image in the CSSI geometry through fast-performing CUDA-assisted PyTorch optimization with automatic differentiation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Information transfer as a framework for optimized phase imaging

In order to efficiently image a non-absorbing sample (a phase object), dedicated phase contrast optics are required. Typically, these optics are designed with the assumption that the sample is weakly scattering, implying a linear relation between a sample’s phase and its transmission function. In the strongly scattering, nonlinear case, the standard optics are ineffective, and the transfer functions used to characterize them are uninformative. We use the Fisher information (FI) to assess the efficiency of various phase imaging schemes and to calculate an information transfer function (ITF). We show that a generalized version of Zernike phase contrast is efficient given sufficient prior knowledge of the sample. We show that with no prior knowledge, a random sensing measurement yields a significant fraction of the available information. Finally, we introduce a generalized approach to common path interferometry that can be optimized to prioritize sensitivity to particular sample features. Each of these measurements can be performed using Fourier lenses and phase masks.

Koppell, Stewart↗
DOE OSTI2020

Comparison Study of Upper Subcritical Limits Derived Using Sensitivity/Uncertainty Tools Case Studies of Benchmarks and Applications

Neutron transport methods used to establish subcriticality require validation by comparison to critical experiments considered to be benchmarks. Whisper is a sensitivity/uncertainty analysis tool developed to assist with the task of validation in nuclear criticality safety. Details on the Whisper methodology can be found in References 1-3 on the MCNP ® reference collection website at https://mcnp.lanl.gov. Whisper-1.0 was originally developed in 2014 and used to assist with nuclear criticality safety validation at Los Alamos National Laboratory. Whisper was upgraded in 2016 to Whisper-1.1 and prepared for release with MCNP6.2. Whisper contains a library of over 1100 critical experiment benchmarks and quantifies neutronic similarity of an application to benchmarks in the library. Using highest similarity benchmarks, Whisper computes a calculational margin (CM) encompassing of the worst-case bias and bias uncertainty at a 99% confidence level for each application. In addition, portions of the margin of subcriticality (MOS) for nuclear data uncertainty and potential code errors are computed. The baseline upper subcritical limit (USL) computed by Whisper is comprised of the CM, MOS nuclear data , and MOS code errors . The Whisper baseline USL is absent a portion of the MOS due to the area of application, which is applied based upon judgment by the criticality safety analyst. An objective of this paper is to present the baseline USL, CM and portions of the MOS as computed by Whisper for comparison with similar sensitivity/uncertainty tools, such as those used by IRSN and ORNL. The initial comparison involves four critical experiment benchmarks: HEU-MET-FAST-013-001, HEU-SOLTHERM-001-008, PU-MET-FAST-022-001, AND PU-SOL-THERM-001-001, which have been: 1. modeled independently by LANL, IRSN, and ORNL based upon information provided in the ICSBEP Handbook, 2. are common in S/U libraries for LANL, IRSN, and ORNL, 3. span a range of energy spectrum and fissile material, and 4. taken as applications for the purposes of this study and therefore excluded from use as a benchmark for calculating the upper subcritical limit. Results presented in this paper have been computed using covariance data for all isotopes in ENDF/BVII.0 using a 44-group energy structure. Benchmarks in the Whisper library were run in MCNP6.2 using 100,000 neutrons per cycle, skipping 100 cycles for 500 active cycles. Reference 10 also compares the results for baseline USL with an order of magnitude greater neutrons, using the same total number of cycles with 1,000,000 neutrons per cycle. Subsequent to the results presented in Reference 10 changes were made to the benchmark library, as discussed in Reference 11. Newer results using the revised benchmark library are presented herein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Navigating Common Electric Vehicle and Charging Infrastructure Concerns

Increasing knowledge about electric vehicles (EVs) can increase Tribal member comfort level with the technology, leading to increased EV adoption. This fact sheet lists common EV and charging infrastructure concerns and provides information to help address these hurdles during engagement with Tribal communities.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

1-D coupled surface flow and transport equations revisited via the physics-informed neural network approach

The de Saint-Venant equation (SVE) and advection–diffusion equation (ADE) are commonly employed to solve solute transport problems in surface water. In this work, we propose a mesh-free method based on the physics-informed neural network (PINN) to solve the one dimensional (1-D) SVE, ADE, and the coupled SVE and ADE (SVE-ADE) under various initial and boundary conditions. The PINN model extends the architecture of deep neural networks (DNNs) with implementation of loss function, which are additionally subject to constraints imposed by the physical laws of SVE and ADE, along with their initial and boundary conditions. In such a manner, PINNs can be quickly steered to the true solution while obeying the physical laws. The results of PINN model are compared with the analytical and/or numerical solutions under various conditions to investigate its accuracy and efficiency in solving the SVE, ADE, and SVE-ADE. Our results indicate PINN can accurately simulate the shock wave morphology and avoid numerical dissipation in unsteady flow condition. The PINN method outweighed traditional numerical methods in several aspects, including its ability to function with small amounts of data, no grid discretization, and random selection of sampling points, etc. Additionally, the PINN method is also suitable for solving inverse problems with sparse and noisy data. With 1% noise and 2000 initial and boundary condition points (N u ), the errors of the estimated flow rate (v) and diffusion coefficient (D) are 0.003% and 0.105%, respectively, which indicate the accuracy and robustness of the proposed method. Finally, our results indicate the capability and robustness of the proposed PINN methodology for solving multi-physics problems, irrespective of the presence of sparse and noisy data in the training dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts

Antifungal drug resistance is a major challenge in fungal infection management. Numerous genomic changes are known to contribute to acquired drug resistance in clinical isolates of specific pathogens, but whether they broadly explain natural resistance across entire lineages is unknown. We leveraged genomic, ecological, and phenotypic trait data from naturally sampled strains from nearly all known species in subphylum Saccharomycotina to examine the evolution of resistance to eight antifungal drugs. The phylogenetic distribution of drug resistance varied by drug; fluconazole resistance was widespread, while 5-fluorocytosine resistance was rare, except in Lipomycetales. A random forest algorithm trained on genomic data predicted drug-resistant yeasts with 54–75% accuracy. Fluconazole resistance was consistently predicted with the highest accuracy (75.2%). Furthermore, fluconazole resistance prediction accuracy was similar between models trained on genome-wide variation in the presence and number of InterPro protein annotations across Saccharomycotina (75.2%) and those trained on amino acid sequence alignment data of Erg11, a protein known to be involved in fluconazole resistance (74.3-74.9%). Interestingly, the top Erg11 residues for predicting fluconazole resistance across Saccharomycotina do not overlap with, are not spatially close to, and are less conserved than those previously linked to resistance in clinical isolates of Candida albicans. In silico deep mutational scanning of the C. albicans Erg11 protein reveals that amino acid variants implicated in clinical cases of resistance are almost universally destabilizing while variants in our most informative residues are energetically more neutral, explaining why the latter are much more common than the former in natural populations. Importantly, previous experimental analyses of C. albicans Erg11 have shown that amino acid variation in our most informative residues, despite having never been directly implicated in clinical cases, can directly contribute to resistance. Our results suggest that studies of natural resistance in yeast species never encountered in the clinic will yield a fuller understanding of antifungal drug resistance.

Harrison, Marie-Claire [Vanderbilt Univ., Nashvill↗
DOE OSTI2024

Retrieval Augmented Generation for Robust Cyber Defense

In cybersecurity, the ability to efficiently analyze and respond to vulnerabilities, weaknesses, attack patterns, and threat tactics is critical for effective defense strategies. With the increasing complexity and volume of cybersecurity data, traditional methods of querying and retrieving information are often inadequate. To address this challenge, we implemented Retrieval-Augmented Generation (RAG) systems—CyRAG and GraphCyRAG—that integrate large language models (LLMs) with both structured data from relational databases and knowledge graphs such as Neo4j. CyRAG is designed to handle structured data, focusing on CVE (Common Vulnerabilities and Exposures) and CWE (Common Weakness Enumeration) entities to generate accurate and context-rich responses. In contrast, GraphCyRAG leverages Neo4j knowledge graphs to retrieve interconnected information from CVE, CWE, CAPEC (Common Attack Pattern Enumeration and Classification), and ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) datasets. By utilizing Neo4j’s graph-based framework, GraphCyRAG enables deeper traversal of relationships between vulnerabilities and attack patterns, providing cybersecurity analysts with more comprehensive insights into potential attack vectors and mitigation strategies. Our preliminary results demonstrate that integrating knowledge graphs with RAG significantly enhances both the accuracy and depth of threat analysis, allowing for the retrieval of dynamic, real-time data and the generation of contextually aware responses. This approach helps analysts uncover hidden relationships between cyber entities, predict exploit paths, and prioritize mitigation efforts effectively. The integration of RAG with cybersecurity knowledge graphs represents a significant advancement in cybersecurity threat intelligence, enabling more informed decision-making and stronger defense strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Retrofit-ability: A supplementary metric to inform energy efficiency policies and programs

Building Energy Use Intensity (EUI) has been commonly used to facilitate policy makers and utilities to design energy efficacy programs and help building managers prioritize investment in building upgrades. Buildings with higher EUIs are usually chosen as better candidates for retrofit. Our study of nearly one hundred buildings in Seattle’s Building Tune Up Accelerator program and thousands of buildings in the Asset Score database reveals that EUI (after weather/location/use type normalization) alone is not the best indicator of a building’s potential to save energy, especially for those buildings in the mid-range (30-70 percentile EUI). In this paper, we introduce the concept of “retrofit-ability” — a building’s real potential to reduce its energy use cost-effectively — as a supplementary metric to inform energy efficiency policy and investment. Currently, such potential analyses are predicted for a building stock using prototypical building models or statistical data. However, typical buildings or general historical data may not sufficiently represent building configurations in a portfolio and predict their saving potentials. We utilized machine learning to investigate how key building characteristics (such as envelope attributes, HVAC type, location, use type, etc.) affect a building’s improvement potentials and developed a low-cost method to quantify such potentials for a portfolio of buildings. The supplementary perspective provided by “retrofit-ability” highlights which building assets correlate most with a building’s energy savings potential across region, building use type and more.

Wang, Na↗
DOE OSTI2024

Efficient species identification for Pacific salmon genetic monitoring programs

Abstract Genetic monitoring of Pacific salmon in the Columbia River basin provides crucial information to fisheries managers that is otherwise challenging to obtain using traditional methods. Monitoring programs such as genetic stock identification (GSI) and parentage‐based tagging (PBT) involve genotyping tens of thousands of individuals annually. Although rare, these large sample collections inevitably include misidentified species, which exhibit low genotyping success on species‐specific Genotyping‐in‐Thousands by sequencing (GT‐seq) panels. For laboratories involved in large‐scale genotyping efforts, diagnosing non‐target species and reassigning them to the appropriate monitoring program can be costly and time‐consuming. To address this problem, we identified 19 primer pairs that exhibit consistent cross‐species amplification among salmonids and contain 51 species informative variants. These genetic markers reliably discriminate among 11 salmonid species and two subspecies of Cutthroat Trout and have been included in species‐specific GT‐seq panels for Chinook Salmon, Coho Salmon, Sockeye Salmon, and Rainbow Trout commonly used for Pacific salmon genetic monitoring. The majority of species‐informative amplicons (16) were newly identified from the four existing GT‐seq panels, thus demonstrating a low‐cost approach to species identification when using targeted sequencing methods. A species‐calling script was developed that is tailored for routine GT‐seq genotyping pipelines and automates the identification of non‐target species. Following extensive testing with empirical and simulated data, we demonstrated that the genetic markers and accompanying script accurately identified species and are robust to missing genotypic data and low‐frequency, shared polymorphisms among species. Finally, we used these tools to identify Coho Salmon incidentally caught in the Columbia River Chinook Salmon sport fishery and used PBT to determine their hatchery of origin. These molecular and computing resources provide a valuable tool for Pacific salmon conservation in the Columbia River basin and demonstrate a cost‐effective approach to species identification for genetic monitoring programs.

Robinson, Zachary L.↗
DOE OSTI2022

A Comparison of Homogeneous and Heterogeneous Uranium Metal-Water Systems using Calculated and Experimental Critical Data

Heterogeneous effects of fissile units latticed in water has been documented in several forms in commonly referenced handbooks and guides. Many of these documents provide guidance for when heterogeneous systems are more reactive than their homogenous counterparts if controlling fissile mass or volume. This information provides insight into when heterogeneity should be considered for conservatism, however this information is not always displayed in a manner that is comparable to commonly referenced data, such as critical curves based on spherical systems. This paper aims to provide a juxtaposition of homogenous and heterogeneous uranium metal-water systems using both experimental and calculated critical data displayed in the format of commonly referenced critical curves. This format allows for easy comparison between the two systems against parameters that are often considered for single unit analysis such as: uranium density, fissile mass, system volume, and total system mass (fissile mass plus moderator mass). Calculated critical spherical systems provide an equal comparison between latticed geometry types as the latticed array can be cut off or restricted in the same manner in each series of models, which is not true for experimental values. Additionally, enrichment of the latticed units can be made uniform in the calculated systems, which is also difficult to achieve when referencing experimental data of varying latticed geometries. A comparison of calculated critical values and available experimental values is provided to show how these parameters can impact the critical spherical system result.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Comparison of Homogeneous and Heterogeneous Uranium Metal-Water Systems using Calculated and Experimental Critical Data

Heterogeneous effects of fissile units latticed in water has been documented in several forms in commonly referenced handbooks and guides. Many of these documents provide guidance for when heterogeneous systems are more reactive than their homogenous counterparts if controlling fissile mass or volume. This information provides insight into when heterogeneity should be considered for conservatism, however this information is not always displayed in a manner that is comparable to commonly referenced data, such as critical curves based on spherical systems. This paper aims to provide a juxtaposition of homogenous and heterogeneous uranium metal-water systems using both experimental and calculated critical data displayed in the format of commonly referenced critical curves. This format allows for easy comparison between the two systems against parameters that are often considered for single unit analysis such as: uranium density, fissile mass, system volume, and total system mass (fissile mass plus moderator mass). Calculated critical spherical systems provide an equal comparison between latticed geometry types as the latticed array can be physically cut off or restricted in the same manner in each series of models, which is not true for experimental values. Additionally, enrichment of the latticed units can be made uniform in the calculated systems, which is difficult to achieve when referencing experimental data of varying latticed geometries. A comparison of calculated critical values and available experimental values is provided to show how these parameters can impact the critical spherical system result.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Value of Sharing and Consolidating Critical Community, Electricity, and Natural Hazard Information

The California Public Utilities Commission (CPUC) sought support for the development of database schema specifications for a bi-directional data portal that would foster cross-jurisdictional collaboration necessary to improve resilient energy infrastructure planning processes. This technical assistance activity involved Berkeley Lab researchers reviewing 34 local/tribal government hazard mitigation plans to assess (1) the natural hazards that communities are most concerned about; (2) the variety of–and terminology used to describe–critical community infrastructure; and (3) the availability of GIS information that could be incorporated into CPUC-mandated "Microgrid Planning Portals". In addition, we develop a common, but generic data taxonomy showing what fields to collect to encourage consolidating and sharing of this information in the future. Finally, we partnered with the Bishop Paiute tribal government to demonstrate the value of combining electricity infrastructure, natural hazard layers, and critical community infrastructure into a series of maps. The project demonstrated that a single system containing both electric utility and community infrastructure data—as well as information about natural hazards—will help the state of California, the IOUs, first responders, and long-term planners better prepare for—and thus lower their exposure to these ongoing and emerging hazards. The project also identified a number of challenges that will need to be overcome before it is possible to stand up a single system to display this information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Soluble limit and criticality of fermions in $\mathbb Z_2$ gauge theories

Quantum information theory and strongly correlated electron systems share a common theme of macroscopic quantum entanglement. In both topological error correction codes and theories of quantum materials (spin liquid, heavy fermion and high-$T_c$ systems), entanglement is implemented by means of an emergent gauge symmetry. Inspired by these connections, in this paper we introduce a simple model for fermions moving in the deconfined phase of a $\mathbb Z_2$ gauge theory by coupling Kitaev's toric code to mobile fermions. This permits us to exactly solve the ground state of this system and map out its phase diagram. Reversing the sign of the plaquette term in the toric code permits us to tune the ground state between an orthogonal metal and an orthogonal semimetal in which gapless quasiparticles survive despite a gap in the spectrum of original fermions. The small-to-large Fermi surface transition between these two states occurs in a stepwise fashion with multiple intermediate phases. By using a diagrammatic technique, we are able to explore physics beyond the integrable point to examine various instabilities of the deconfined phase and to derive the critical theory at the transition between deconfined and confined phases. We outline how the fermionic toric code can be implemented as a quantum circuit, thus providing an important link between quantum materials and quantum information theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Standard Analytical Methods for Pyrolysis Bio-Oils

There has been significant recent interest in the production of renewable fuels and chemicals from biomass and waste feedstocks. Pyrolysis pathways produce a liquid bio-oil product, which must be processed further, or upgraded, to yield fuel or chemical products. Bio-oils are very complex and often unstable samples, and research and development on upgrading processes needs reliable analytical information. In particular, chemical characterization techniques are needed to quantify both functional groups and individual compounds present in bio-oils. Reliable analytics are also needed to enable the bioenergy industry, as industrial facilities often have different analytical needs and capabilities than research facilities. In this presentation, we will discuss the development of a suite of standard analytical methods for pyrolysis bio-oils. Analytical methods to be discussed include: Determination of Carbon, Hydrogen, Nitrogen, and Oxygen in bio-oils; Accelerated Aging of Fast Pyrolysis Bio-oil using Carbonyl Titration; Determination of Water Content in Bio-oils by Volumetric Karl Fischer Titration; Determination of Carbon Functional Groups; Elemental Analysis of Bio-oils by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) - Na, K, Mg, Ca, S, P, and Fe; Determination of Phenolic Groups in Bio-oils using Revised Folin-Ciocalteu Methods: Single Cuvette and Plate Reader; Corrosivity of Bio-oils: Screening Test using Metal Leaching; Determination of Biogenic Content by 14C Measurement using Liquid Scintillation Counter. These new analytical methods are publicly available as Laboratory Analytical Procedures (https://www.nrel.gov/bioenergy/bio-oil-analysis.html), along with previously developed standard methods: GC-MS, Acid Titration, Carbonyl Titration, and 31P NMR. Additionally, the development of diffusion ordered NMR for characterization of bio-oil molecular weight will be discussed. Collectively, this suite of analytical methods represents the most comprehensive set of standard methods available for pyrolysis bio-oils. These standard methods are commonly used by the bioenergy community, and provide reliable information that enables research, scaleup, and industrial processing of biomass to produce renewable fuels and chemicals.

analytical↗
DOE OSTI2021

AlphaFold Protein Structure Database for Sequence-Independent Molecular Replacement

Crystallographic phasing recovers the phase information that is lost during a diffraction experiment. Molecular replacement is a commonly used phasing method for crystal structures in the protein data bank. In one form it uses a protein sequence to search a structure database to find suitable templates for phasing. However, sequence information is not always available, such as when proteins are crystallized with unknown binding partner proteins or when the crystal is of a contaminant. The recent development of AlphaFold published the predicted protein structures for every protein from twenty distinct species. In this work, we tested whether AlphaFold-predicted E. coli protein structures were accurate enough to enable sequence-independent phasing of diffraction data from two crystallization contaminants of unknown sequence. Using each of more than 4000 predicted structures as a search model, robust molecular replacement solutions were obtained, which allowed the identification and structure determination of YncE and YadF. Our results demonstrate the general utility of the AlphaFold-predicted structure database with respect to sequence-independent crystallographic phasing.

59 BASIC BIOLOGICAL SCIENCES↗