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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Calibration of V-Notch and Compound Weirs for Subsurface Drainage Water Level Control Structures

Highlights Accurate discharge estimation is important when evaluating edge-of-field conservation practices. V-notch weir equations were developed for three sizes of subsurface drainage water level control structures. Compound weir equations were developed for subsurface drainage water level control structures. The compound weir equation accurately estimates discharge for flows within and overtopping the V-notch. Abstract.Numerous edge-of-field conservation practices use subsurface drainage water level control structures to monitor water levels and estimate discharge. In a control structure, procedures for calculating discharge when flow depth (head) exceeds the V-notch depth and overflows in the rectangular portion of the compound weir (CW) are ambiguous. In this study, we developed calibration equations for V-notch weirs in Agri Drain inline water level control structures of different sizes for flows within the V-notch and overtopping flow events. The discharge equation for overtopping events (Q CW , L s -1 ) was determined as: Q CW = a 1 (h b1 -h 1 b1 )+a 2 (W e -W v )h 1 b2 , where h and h 1 are heads above vertex/bottom and top of V-notch (cm), respectively, W is the effective crest width of rectangular weir (cm), W v is the top width of V-notch (cm), a 1 and b 1 are parameters for V-notch weir obtained by calibration, and a 2 and b 2 are calibration parameters for rectangular weir obtained from literature. Results were compared with a weir equation available in the literature (Q V+R ), which combines a V-notch equation with a head equal to V-depth and a rectangular weir equation for flow above V-depth. Discharge at overflow was estimated with high accuracy with Q CW, whereas Q V+R underestimated discharge (e.g., PBIAS of 0.67% vs. 17.82% for a 15.2 cm structure). An example using Q V+R resulted in a 14% lower annual estimation of nitrate-N load diverted to a saturated buffer than Q CW due to underestimation of drainage discharge during overflow events. Results suggest that the developed equation (Q CW ) accurately estimates discharge and will thus improve the estimated N load compared to Q V+R . Keywords: Compound weir, Flow monitoring, Subsurface drainage, V-notch weir, Water level control structure, Weir calibration.

Agriculture↗
DOE OSTI2025

Chromatin structures from integrated AI and polymer physics model

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Structure of the EphB6 receptor ectodomain

Eph receptors are the largest group amongst the receptor tyrosine kinases and are divided into two subgroups, A and B, based on ligand binding specificities and sequence conservation. Through ligand-induced and ligand-independent activities, Ephs play central roles in diverse biological processes, including embryo development, regulation of neuronal signaling, immune responses, vasculogenesis, as well as tumor initiation, progression, and metastasis. The Eph extracellular regions (ECDs) are constituted of multiple domains, and previous structural studies of the A class receptors revealed how they interact with ephrin ligands and simultaneously mediate Eph-Eph clustering necessary for biological activity. Specifically, EphA structures highlighted a model, where clustering of ligand-bound receptors relies on two distinct receptor/receptor interfaces. Interestingly, most unliganded A class receptors also form an additional, third interface, between the ligand binding domain (LBD) and the fibronectin III domain (FN3) of neighboring molecules. Structures of B-class Eph ECDs, on the other hand, have never been reported. To further our understanding of Eph receptor function, we crystallized the EphB6-ECD and determined its three-dimensional structure using X-ray crystallography. EphB6 has important functions in both normal physiology and human malignancies and is especially interesting because this atypical receptor innately lacks kinase activity and our understanding of the mechanism of action is still incomplete. Our structural data reveals the overall EphB6-ECD architecture and shows EphB6-LBD/FN3 interactions similar to those observed for the unliganded A class receptors, suggesting that these unusual interactions are of general importance to the Eph group. We also observe unique structural features, which likely reflect the atypical signaling properties of EphB6, namely the need of co-receptor(s) for this kinase-inactive Eph. These findings provide new valuable information on the structural organization and mechanism of action of the B-class Ephs, and specifically EphB6, which in the future will assist in identifying clinically relevant targets for cancer therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Synthesis and structural characterization of orthorhombic Cu 3– δ Sb ( δ ≈ 0.1) and hexagonal Cu 3 Sb 1–x In x (x ≈ 0.2) phases

Cu 3 Sb is a known copper-rich phase in the Cu–Sb binary phase diagram. It is reported to be dimorphic, with a low-temperature form adopting the orthorhombic Cu 3 Ti structure type (space group Pmmn , No. 59). The high-temperature form crystallizes in the cubic space group F m 3 - m $Fm‾{3}m$ (No. 225), and is isostructural with BiF 3 . Neither polymorph has been carefully characterized to date, with both structures being assigned to the respective structure type, but never refined. With this study, we provide structural evidence, based on single-crystal and powder X-ray diffraction data that the low-temperature orthorhombic phase exists with a significant amount of defects on one of the Cu-sites. As a result, its composition is not Cu 3 Sb, but rather Cu 3– δ Sb ( δ = 0.13(1)). The cubic form could not be accessed as a part of this study, but another Cu-rich phase, Cu 3 Sb ≈0.8 In ≈0.2 , was also identified. It adopts the hexagonal Ni 3 Sn structure type (space group P 6 3 / mmc , No. 194) and represents an In-substituted variant of a hitherto unknown structural modification of Cu 3 Sb. Whether the latter can exist as a binary phase, or what is the minimum amount of In inclusions needed to stabilize it remains to be determined. Measurements of the thermopower of Cu 3– δ Sb ( δ = 0.13(1)) were conducted in the range of 300–600 K and demonstrated a maximum value of ca. 50 μV/K at 600 K, indicative of a p -type transport mechanism. Electrical resistivity measurements for the same sample confirmed that it exhibits metallic-like behavior, with a room temperature value of 0.43 mΩ cm. Electronic structure calculations show the absence of a band gap. Thermal analysis was utilized to ascertain the congruent melting of both phases.

Crystallography↗
DOE OSTI2020

Understanding Structures of Cyber Competition in an Era of Major Power Rivalry

Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Fracture toughness evaluations for spent nuclear fuel dry storage canister welds and spent nuclear fuel clad-pellet structures

Integrity of spent nuclear fuel (SNF) interim storage canisters is very important to the safety of the back-end nuclear fuel cycle. Stress corrosion cracking (SCC) potential of interim storage canister has been considered as a high priority. Because no post-weld heat treatment was required for forming these canisters, the high tensile residual stress existed within these canister welds. This can change the fracture resistance capacity significantly as well as increase SCC potential. Due to relative thin shell thickness of a canister weldment, the spiral notch torsion test (SNTT) method was used to estimate the canister weldment fracture toughness. SNTT was developed to measure the intrinsic fracture toughness of structural materials using small specimens. The SNTT method has been applied to a wide variety of structural materials, such as low-alloy steels, stainless steel, aluminum alloy, ceramics, concrete, and composites. The SNTT system operates by applying pure torsion to cylindrical specimens with a notch line that spirals around the specimen at a 45° pitch. In order to carry out pure torsion load mode, biaxial tension/torsion tester was developed accordingly to perform SNTT protocol. Moreover, applying fracture mechanics approach to SNF system reliability investigation is warranted due to the inherent flaws and hydride structures existed in a SNF system after nuclear reactor operation. However, none of the existing fracture toughness data deal with fuel cladding specific geometry or spent fuel material conditions, such as cladding structure with the pellet-inserts and the associated pellet clad mechanical interactions induced mixed-mode damage mechanisms. Thus, the development of an intrinsic fracture mechanics approach that is suitable for SNF materials is needed. Furthermore, due to thin wall and small dimension of clad tubing structure, the SNTT method was used to estimate the clad tubing structure fracture toughness. Fracture testing were performed on the received stainless steel canister weldment, most SNTT weld samples fracture initiation sites are at heat-affected zone (HAZ) regions. The estimated fracture toughness J Q ’ for the baseline SS304 steel is at 283 kJ/m². The estimated JQ’ for the SS304/308 weld and baseline metals are 148 kJ/m 2 and 459 kJ/m 2 , respectively. Out of cell fracture testing for spent fuel structure were carried out on the surrogate rods made of Zr-4 clad and alumina inserts, the estimated fracture toughness values for baseline Zr-4 cladding with alumina-pellet inserts are: (1) For SNTT samples with a short or medium crack length, between 5.4-mm and 8-mm, the estimate J IQ upon fracture initiation for the baseline Zr-4 cladding is at 50 kJ/m 2 with 2-sigma uncertainty of 3.26 kJ/m 2 , and the associated K IQ is at 67.46 MPa$\sqrt{m}$; and (2) For SNTT samples with a long crack length, around 13-mm, the crack initialization is deviated from that of the Mode-I tensile fracture and appears to be a mixed-mode fracture of Mode I - tensile stress and Mode III - out of plane shear stress; the estimated J MQ is at 18.9 kJ/m 2 , the associated K MQ is at 41.4 MPa$\sqrt{m}$.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Revisiting Multi-Material Composite Structures with Homogenized Composite Properties

Composite structures inherently develop residual stresses during their curing process. Driven predominately by mismatched thermal strains between differing materials or ply orientations, but also affected by curing process phenomena like polymer shrinkage, these residual stresses can lead to failure within composite structures. There are several methods varying in complexity that can be used to model the development of residual stresses, all of which are capable of capturing sufficient detail to understand the residual stress state at the ply level. However, explicitly modeling all plies of a layup in a composite structure can be prohibitively expensive based on the number of plies, structure size, and required element size. The computational cost can be reduced through the homogenization of the composite layup without losing much fidelity of the overall response of the structure. The homogenization process reduces the many plies of a laminate to a single lamina that reduces complexity and increases the mesh size where a single element can span multiple plies. This report focuses on verification and validation efforts for a homogenization process using a suite of finite element simulations rather than an analytic solution derived from classical laminate theory. Initial verification using representative element volumes indicated there was minimal error in the homogenization process; however, this compounded to a small, but acceptable error in strip and split ring experimental composite structures. The error does under predict the residual stress state in the strip and split ring and should be accounted for when simulating composite structures with homogenized properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Correlating Tomographic Chemical Inhomogeneity and Low Energy Electronic Structure in Layered Quantum Materials

Photoemission spectroscopy (PES) is a suite of experimental tools to learn about the electronic and chemical structure of materials and surfaces. Normally implemented in a surface-sensitive manner, the research performed under this grant focused on pushing PES into less explored regimes, to reveal bulk electronic structure, to reveal tomographic (layer-resolved) chemistry and electronic structure of layered materials and heterostructures, and to reveal interface phenomena at the junction of two different materials. Standing wave (SW) spectroscopies have also been applied to PdCoO2, a material of interest due to its high conductivity and electron-hydrodynamic tendencies. This material can be modeled as an alternating layered structure consisting of metallic Pd layers and insulating CoO2 layers. Using SW XPS, the total electronic structure has been decomposed into contributions from the two layers, and computations highlighted the different many-body interactions in the two layers (Comm. Phys. 4, 143 (2021)). We have also used hard-x-ray angle-resolved photoemission spectroscopy (ARPES), to investigate LaB6, a technologically important material with widespread application as a cathode material for electron microscopes. We measured the bulk electronic structure of this material and found that the one-step model of photoemission better captured the electronic structure and correlations. This model treats the three steps of the photoemission process—excitation, transport of the photoelectron to the crystal surface, and escape into the vacuum—as a single quantum mechanically coherent process (Phys. Rev. Mater. 5, 055002 (2021)). We also applied x-ray photoelectron spectroscopy, implemented in a near total reflection grazing incidence geometry to elucidate technologically relevant interfaces, such as those between a substrate and photoresist (J. Phys. D: Appl. Phys. 54 464002 (2021)).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural Evolution of the Hogback Monocline and Its Tectonic Significance in the San Juan Basin

The San Juan Basin is recognized as a Laramide foreland basin. It is located within the Colorado Plateau, a broad tectonic province characterized by a thick sedimentary sequence that was segmented into smaller sub basins during the Late Cretaceous to Paleogene Laramide orogeny. The Hogback Monocline lies along the northwestern margin of the San Juan Basin and is considered a Laramide-age structure formed in response to compressional stress. In this study, we interpret surface and subsurface datasets to construct a structural geological model and evaluate its tectonic significance. Through seismic data, we identify key fault and fold geometries at depth. The seismic dataset used in this study was reprocessed in depth and constrained with well log velocity data to enhance seismic imaging quality. Additionally, we performed well log correlations to identify formation tops and assess variations in basin infill and thickness geometry. A series of structural cross-sections, constructed using seismic data and a high density of boreholes, are presented to evaluate geometric variations along the structure and its evolution during basin development. Furthermore, kinematic restoration and forward modeling analyses were conducted to validate our structural interpretation. This work suggests that the Hogback Monocline formed through fault-propagation folding and flexural slip affecting the pre-Laramide sedimentary sequence under compressional stresses associated with the Laramide orogeny. This structure is interpreted as a high-angle reverse fault that influenced the geometry of the late basin infill. Additionally, monocline bending along the structure may have been controlled by fault relay systems and, in some cases, influenced by strike-slip faulting.

Reyes, Martin [New Mexico Bureau o fGeology and Mi↗
DOE OSTI2024

Thermo-Mechanical Modeling and Evaluation of the Cracking Response of Additively Manufactured Monolithic SiC Lattice Structures Subjected to Laser Heating

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Owing to its high temperature stability combined with excellent thermal and optical properties, SiC has been the material of choice for application in high-temperature solar receivers. The state-of-the-art SiC volumetric concentrating solar air receivers such as honeycomb design have been demonstrated in field tests to achieve exit air temperatures approaching 800oC. However, successful application of CST systems for decarbonization requires significant increase in temperature capability of SiC receiver technology. Supported by an award from the Solar Technology Office (SETO), US Department of Energy (DOE), GE Research in collaboration with Heliogen Holdings Inc, is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. The program objective is to design and to demonstrate a techno-economically viable SiC air receiver technology to achieve exit air temperature of 1100oC for CST applications. This report summarizes the learnings of a computational study on additively manufactured SiC lattice structures subjected to laser heating. The results from this study aim to help guide the design of a SiC receiver through a better understanding of lattice structure geometric parameters and their implications on the cracking response of the structure. In the study, a heat flux was applied to the surface of a 2”-diameter cylindrical lattice structure to simulate a 4kW CO2 laser. The resulting temperature distribution was applied to a structural model to approximate the stress distribution within the lattice structure and a Weibull analysis was performed to gain insight into the probability of failure and to evaluate the cracking response of the structure. With this approach, the influence of lattice density on temperature, stress, and probability of failure was explored for two different lattice beam spacings. A discussion on modeling assumptions, a comparison with experimental results, and an evaluation of the lattice cracking response is provided.

14 SOLAR ENERGY↗
DOE OSTI2021

Structural Polymorphism of Chitin and Chitosan in Fungal Cell Walls From Solid-State NMR and Principal Component Analysis

Chitin is a major carbohydrate component of the fungal cell wall and a promising target for novel antifungal agents. However, it is technically challenging to characterize the structure of this polymer in native cell walls. Here, we recorded and compared 13 C chemical shifts of chitin using isotopically enriched cells of six Aspergillus, Rhizopus, and Candida strains, with data interpretation assisted by principal component analysis (PCA) and linear discriminant analysis (LDA) methods. The structure of chitin is found to be intrinsically heterogeneous, with peak multiplicity detected in each sample and distinct fingerprints observed across fungal species. Fungal chitin exhibits partial similarity to the model structures of α- and γ-allomorphs; therefore, chitin structure is not significantly affected by interactions with other cell wall components. Addition of antifungal drugs and salts did not significantly perturb the chemical shifts, revealing the structural resistance of chitin to external stress. In addition, the structure of the deacetylated form, chitosan, was found to resemble a relaxed two-fold helix conformation. This study provides high-resolution information on the structure of chitin and chitosan in their cellular contexts. The method is applicable to the analysis of other complex carbohydrates and polymer composites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Evolution of the Fermi Surface of 1T-VSe 2 across a Structural Phase Transition

Periodic lattice distortion, known as the charge density wave, is generally attributed to electron–phonon coupling. This correlation is expected to induce a pseudogap at the Fermi level in order to gain the required energy for stable lattice distortion. The transition metal dichalcogenide 1T-VSe 2 also undergoes such a transition at 110 K. Here, we present detailed angle-resolved photoemission spectroscopy experiments to investigate the electronic structure in 1T-VSe 2 across the structural transition. Previously reported warping of the electronic structure and the energy shift of a secondary peak near the Fermi level as the origin of the charge density wave phase are shown to be temperature independent and hence cannot be attributed to the structural transition. Our work reveals new states that were not resolved in previous studies. Earlier results can be explained by the different dispersion natures of these states and temperature-induced broadening. Only the overall size of the Fermi surface is found to change across the structural transition. These observations, quite different from the charge density wave scenario commonly considered for 1T-VSe 2 and other transition metal dichalcogenides, bring fresh perspectives toward correctly describing structural transitions. Therefore, these new results can be applied to material families in which the origin of the structural transition has not been resolved.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural Basis of Clostridium perfringens Enterotoxin Activation and Oligomerization by Trypsin

Clostridium perfringens enterotoxin (CpE) is a β-pore forming toxin that disrupts gastrointestinal homeostasis in mammals by binding membrane protein receptors called claudins. Although structures of CpE fragments bound to claudins have been determined, the mechanisms that trigger CpE activation and oligomerization that lead to the formation of cytotoxic β-pores remain undetermined. Proteolysis of CpE in the gut by trypsin has been shown to play a role in this and subsequent cytotoxicity processes. Here, we report solution structures of full-length and trypsinized CpE using small-angle X-ray scattering (SAXS) and crystal structures of trypsinized CpE and its C-terminal claudin-binding domain (cCpE) using X-ray crystallography. Mass spectrometry and SAXS uncover that removal of the CpE N-terminus by trypsin alters the CpE structure to expose areas that are normally unexposed. Crystal structures of trypsinized CpE and cCpE reveal unique dimer interfaces that could serve as oligomerization sites. Moreover, comparisons of these structures to existing ones predict the functional implications of oligomerization in the contexts of cell receptor binding and β-pore formation. This study sheds light on trypsin’s role in altering CpE structure to activate its function via inducing oligomerization on its path toward cytotoxic β-pore formation. Its findings can incite new approaches to inhibit CpE-based cytotoxicity with oligomer-disrupting therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

On the Crystal Structure of Colloidally Prepared Metastable Ag2Se Nanocrystals

Structural polymorphism is known for many bulk materials; however, on the nanoscale metastable polymorphs tend to form more readily than in the bulk, and with more structural variety. One such metastable polymorph observed for colloidal Ag2Se nanocrystals has traditionally been referred to as the “tetragonal” phase of Ag2Se. While there are reports on the chemistry and properties of this metastable polymorph, its crystal structure, and therefore electronic structure, has yet to be determined. We report that an anti-PbCl2-like structure type (space group P21/n) accurately describes the powder X-ray diffraction and X-ray total scattering patterns of colloidal Ag2Se nanocrystals prepared by several different methods. Density functional theory (DFT) calculations indicate that the anti-PbCl2-like Ag2Se polymorph is a dynamically stable, narrow-band gap semiconductor. DFT results reveal a dense theoretical Ag2Se phase space with many low-energy polymorphs, which helps explain the large number of polymorphs reported in the literature. Analysis and calculation data are stored in the zip archive. The `ag2se-calcs.aiida.` contains the provenance of the calculations and can be imported into an AiiDA database instance. The antiPbCl2like_Ag2Se_laboratory.cif file is the Reitveld refined Ag2Se structure starting from the PbCl2 structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗