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At least 613 records · Page 34

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DOE OSTI2024

Control over Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning

The ability to program chain conformation and structure through control over the monomer sequence of synthetic polymers has broad implications for next-generation material design. While related problems of protein-folding and de novo design have generated accurate predictions of 3D folded chain structures, generalization to synthetic polymers remains intractable due to the requirement of large structural databases and the intrinsically disordered nature of polymer building blocks. In this work, polypeptoids, a class of peptidomimetic synthetic polymers, are utilized to build a general workflow for the study of relationships between monomer sequence and dynamic 3D chain structure in solution. Furthermore, this work demonstrates how control over the monomer sequence can alter the conformational landscape of synthetic polymers to deviate dramatically from classical chain statistics. Specifically, the distribution of end-to-end distances, as measured by double electron-electron resonance spectroscopy in dilute solvent, is systematically skewed towards shorter distances with an increasing number of hydrophobes and further refined by hydrophobe arrangement in amphiphilic polypeptoid chains.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Electronic properties and optical spectra of donor–acceptor conjugated organic polymers

Organic semiconductors based on conjugated donor-acceptor (D–A) polymers are a unique platform for electronic, spintronic, and energy-harvesting devices. Understanding the electronic structure of D–A polymers with a small band gap is essential for developing next-generation technologies. Here, we investigate the electronic structure and optical spectra of cyclopentadithiophene-based closed/open-shell D–A polymers using density functional theory and the Bethe–Salpeter equation based on G 0 W 0 approximation. We explored the role of different acceptor units and chemical substitutions on the structural changes and, more importantly, electronic, optical, and dielectric behavior. We found that the computed first exciton peak of the polymers agreed well with the available experimentally measured optical gap. Furthermore, D–A polymers with open-shell character display higher dielectric constant than the closed-shell polymers. We show that the exceptional performance of polycyclopentadithiophene-thiophenylthiadiazoloquinoxaline (PCPDT-TTQ) as a scalable n-type material for Faradaic supercapacitors can be partly ascribed to its elevated dielectric constant. Consequently, these D–A polymers, characterized by their high dielectric constants, exhibit significant potential for various applications, including energy storage, organic electronics, and the production of dielectric films.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Waste heat recovery hybrid power drive

A waste heat recovery (WHR) hybrid power system can be utilized in vehicles to convert heat energy into mechanical energy. The WHR system can include a WHR power unit structured to convert thermal energy into rotation of a WHR drive shaft. A motor/generator having a motor/generator shaft can selectively operate as a motor or a generator. A mechanical linkage can be structured to selectively link a output shaft to one of the WHR drive shaft and the motor/generator drive shaft independently of the other of the WHR drive shaft and the motor/generator drive shaft.

Narula, Manik↗

DOE OSTI2023

Waste heat recovery hybrid power drive

42 ENGINEERING↗

DOE OSTI2025

Nonlinear van der Waals Metasurfaces with Resonantly Enhanced Light Generation

Efficient nonlinear wave mixing is of paramount importance for a wide range of applications. However, weak optical nonlinearities pose significant challenges for accessing nonlinear light–matter interaction in compact systems. Here, we experimentally study second harmonic generation in deeply subwavelength 3R-MoS 2 metasurfaces (<λ/13 thick). Our measurements, supported by theoretical analysis, reveal a complex interplay and coupling between geometric resonances, optical extinction, and exciton-driven strong nonlinear susceptibility dispersion. We further demonstrate >150-fold enhancement in second harmonic signal at 740 nm mediated by the A exciton resonance. Additionally, our theoretical studies predict an enhancement of more than 10 6 in second harmonic generation in <100 nm thick structures exhibiting bound states in the continuum resonance. These findings provide insight into accessing and harnessing the unprecedented 3R-MoS 2 nonlinearities at a subwavelength scale, paving the way to ultracompact nonlinear photonic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Affinity-matured homotypic interactions induce spectrum of PfCSP structures that influence protection from malaria infection

The generation of high-quality antibody responses to Plasmodium falciparum (Pf) circumsporozoite protein (PfCSP), the primary surface antigen of Pf sporozoites, is paramount to the development of an effective malaria vaccine. Here we present an in-depth structural and functional analysis of a panel of potent antibodies encoded by the immunoglobulin heavy chain variable (IGHV) gene IGHV3-33, which is among the most prevalent and potent antibody families induced in the anti-PfCSP immune response and targets the Asn-Ala-Asn-Pro (NANP) repeat region. Cryo-electron microscopy (cryo-EM) reveals a remarkable spectrum of helical antibody-PfCSP structures stabilized by homotypic interactions between tightly packed fragments antigen binding (Fabs), many of which correlate with somatic hypermutation. We demonstrate a key role of these mutated homotypic contacts for high avidity binding to PfCSP and in protection from Pf malaria infection. Together, these data emphasize the importance of anti-homotypic affinity maturation in the frequent selection of IGHV3–33 antibodies and highlight key features underlying the potent protection of this antibody family.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Modeling protein structures from predicted contacts with modern molecular dynamics potentials: accuracy, sensitivity, and refinement

Protein structure prediction has become increasingly popular and successful in recent years. An essential step for fragment-free, template-free methods is the generation of a final three-dimensional protein model from a set of predicted amino acid contacts that are often described by interresidue pairwise atomic distances. Here we explore the use of modern, open-source molecular dynamics (MD) engines, which have been continually developed over the last three decades with all-atom Hamiltonians to model biomolecular structure and dynamics, to generate accurate protein structures starting from a set of inferred pairwise distances. Additionally, the ability of MD empirical physical potentials to correct inaccuracies in the predicted geometries is tested. We rigorously characterize the effect of modeling parameters on results, the effect of different amounts of error in the predicted distances on the final structures, and test the ability of post-processing analysis to sort the best models out of a set of statistical replicas. We find that with exact distances and with noisy distances, the method can produce excellent structural models, and that the molecular dynamics force field seems to help correct errors in distance predictions, resisting the effects of applied noise.

Davidson, Russ↗

DOE OSTI2022

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

Abstract The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Impact of LWR assembly structural features on cladding burst behavior under LOCA conditions

This provides an initial scoping study on clad balloon and burst behavior for burnup extension of reactor fuel. The associated issues with burnup extension are fuel fragmentation, relocation, and dispersal in the event of cladding failure. The general finding of this work is that the structural features, spacer grids and mixing vanes, locally suppress cladding deformation but have little impact on the overall clad performance during loss-of-coolant accidents. The work detailed in a previous report by Capps et al. focused on core optimization via neutronics, thermal hydraulics and thermomechanical analysis for burnups beyond 62 GWD/tU and enrichments above 5%. Uncertainty of fuel fragmentation relocation and dispersal in high-burnup rods during accident conditions was also investigated. The dispersal aspect of fuel fragmentation depends on cladding rupture. Thus, assessing uncertainties in the rupture behavior is helpful in estimating the dispersal of high-burnup fuel. This study builds on the previous work by assessing the impact of assembly structural features on cladding balloon and burst behavior in a full-length fuel rod. In this work, the BISON fuel performance code was used to generate 2D radial and height meshes containing structural features commonly used in nuclear fuel assemblies. First, meshes were generated with spacer grids. Results were then compared to the cladding burst temperature and balloon strain results from the previous work. A mesh sensitivity study was performed to ensure that mixing vanes and spacer grid effects were appropriately considered, resulting in a more refined mesh than the previous study. The balloon deformation and burst times of the cladding were compared to the original case. Consideration was also given to the effect of rod initial pressure. In conclusion, 3D quarter rod simulations were also performed and found good agreement with the 2D simulations in clad deformation and reasonable agreement in burst times.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. In conclusion, the crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

42 ENGINEERING↗

DOE OSTI2024

Cross Correlating Cosmological Probes for LSST & CMB-S4

The upcoming years will be populated with state-of-the-art Stage-IV cosmological surveys. This paper forecasts the cosmological information we expect to constrain using probes from the Rubin Observatory Legacy Survey of Space and Time (LSST) and CMB-S4. We explore the effectiveness of different large-scale-structure probes, including the power spectra of galaxy weak lensing, galaxy position, Cosmic Microwave Background (CMB) lensing, and their cross-correlations. We use correlated lognormal simulations with the expected redshift distributions, galaxy number densities, and noise levels of the LSST survey. For CMB weak lensing, we use CMB-S4 lensing simulations with anticipated noise levels to obtain the expected error bars of each probe. We investigate the constraining power of the cosmological parameters for the individual probes and their combinations in an idealized scenario. We do not take into account astrophysical and observational parameters such as galaxy bias variations and photometric redshift un- certainties. Overall, we find the auto-correlated probes hold stronger constraints on cosmological parameters than the cross-correlated probes due to kernel differences. However, the cross-correlation of galaxy clustering and CMB lensing is very comparable to the auto-correlation of galaxy clustering with only an 8% stronger constraint in S8. It will be important for future surveys to use these auto and cross probes in combination due to the different astrophysical and systematic effects involved in CMB and late-time galaxy data. When systematics are added to this analysis, the cross probes will serve as a check for systematic biases in individual probes. In addition, we find a significant increase in constraint using all four probes in combination. This can be illustrated by the increase in constraint of S8 by 93.2% comparing galaxy clustering auto-correlation to a combination of all probes. Our study is a first step toward forecasting the high-precision cosmological constraints we expect to obtain using the next generation of large-scale structure probes.

Gibbins, Grace↗

DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Broadband‐Tunable Third‐Harmonic Generation Using Phase‐Change Chalcogenides

Despite remarkable progress in passive nonlinear nanophotonics, dynamically controlled ultracompact nonlinear optical sources with high efficiency remain elusive. Nonvolatility, large refractive index contrast between amorphous and crystalline phases, low thermal threshold for crystallization, and particularly high‐third‐order nonlinear optical susceptibility of phase‐change alloy Ge 2 Sb 2 Te 5 (GST) make it a promising candidate for active subwavelength metaphotonic structures for realization of third‐harmonic generation (THG) devices. Herein, a GST‐based asymmetric Fabry–Perot cavity is numerically designed and experimentally demonstrated to show a dynamically reconfigurable structure with a large shift of the THG resonant band. In addition, continuous resonant spectral shifting bridged by a precisely controlled semicrystalline phase of GST is realized. Tunable THG with the fundamental wavelength ranging from 1150 to 1400 nm is presented, which corresponds to a broadband THG source in the violet‐blue visible wavelength range. Herein, the potential of GST subwavelength structures as a reliable platform for realization of the broadband‐tunable frequency‐conversion sources for applications such as THG microscopy and optical communication is indicated.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Experimental observations of amorphization in multiple generations of boron carbide

Boron carbide is an excellent armor material due to its light weight and ultrahigh hardness. However, high-rate mechanical behavior can be degraded by stress-induced amorphization. In this paper, we review the progressive advances in the understanding of amorphization in three successive generations of boron carbide: stoichiometric (undoped), B-rich, and B/Si codoped boron carbides. For each generation of boron carbide, the crystal structure and microstructure are first discussed. Then, we outline the experimental observations of amorphization made by Raman spectroscopy and transmission electron microscopy. The susceptibility of amorphization in each generation of boron carbide will be compared and the fundamental mechanisms that explain the reduction in amorphization for B-rich and B/Si codoped boron carbides elucidated. Furthermore, comments on future research directions to further broaden and deepen the understanding of stress-induced amorphization of boron carbide are also provided.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The role of Li doping in layered/layered Na x Li y Ni 0.4 Fe 0.2 Mn 0.4 O 2 intergrowth electrodes for sodium ion batteries

Here, the layered NaTMO 2 (TM = Ni, Fe, Mn) materials with the O3-type structure are attractive as positive electrodes for sodium ion batteries because of their high theoretical capacity. Additionally, Li doping in these materials has been shown to offer substantial enhancements to their electrochemical properties by promoting the formation of intergrowth structures, which are combinations of specific phases. However, the mechanism by which the intergrowth modifies the electrochemical properties is often unclear. Systematic variation of Li content in Na x Li y Ni 0.4 Fe 0.2 Mn 0.4 O 2 (NFM-Li y ) was conducted to identify its role in structural modification and electrochemical performance. Li contents of 0.15 and greater generate a layered/layered Na-O3/Li-O’3 intergrowth structure. 7 Li and 23 Na nuclear magnetic resonance and x-ray absorption spectroscopy identify that when the total solubility for alkali ions in the layered structure is exceeded, Li continues to form the Li-O’3 phase while the excess Na forms residual sodium compounds such as Na 2 O. Higher Li content is associated with improved capacity retention in the initial cycles from the superior stability of the mechanically linked NaO3/Li-O’3 structure that suppresses the P3 to OP2 phase transition during charge. However, high Li contents are associated with increased rates of parasitic side reactions that reduce long-term cycling stability. These side reactions are associated with the instability of the cathode-electrolyte interphase, which can be partially mitigated by atomic layer deposition (ALD) coating with alumina, which significantly enhances the capacity retention and Coulombic efficiency over many cycles. Overall, we find that the layered/layered Na-O3/Li-O’3 intergrowth structure is able to provide structural stability and suppress undesired phase transformations but is overwhelmed by the increased reactivity of the surface if not protected by surface coating.

25 ENERGY STORAGE↗

DOE OSTI2023

DNA-encoded chemical libraries yield non-covalent and non-peptidic SARS-CoV-2 main protease inhibitors

The development of SARS-CoV-2 main protease (M pro ) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate M pro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of M pro . An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using M pro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of M pro with low nanomolar K i values. Furthermore, these compounds demonstrate efficacy against mutant forms of M pro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Deep learning‑based metal artefact reduction in X-ray computed tomography of TRISO fuel compacts

TRISO (TRi-structural ISOtropic) – compact-type micro-particle fuels – are next generation nuclear fuel compacts designed with safety in mind. Structural integrity and characterisation before and after irradiation are important to determine the performance of the fuel compacts under real reactor conditions. X-ray computed tomography can be an important tool for non-destructive evaluation of these fuel compacts. The fuel particles are highly attenuating for X-rays which creates metal artefact, rendering the images unusable. Our artefact correction can mitigate these artefacts significantly. The proposed method works by first segmenting highly-attenuating structures (metal) and forward projecting to localise the source of artefacts in the projection domain, then, using a traditional or U-net-based deep learning architecture, contextually interpolating those regions to remove the artefacts. Finally the reconstruction from the modified projection is fused with the segmented metal. The proposed method shows a significant improvement in the image quality while significantly reducing the reconstruction time compared to the standard technique.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗

DOE OSTI2021

Coordination Sphere of Lanthanide Aqua Ions Resolved with Ab Initio Molecular Dynamics and X-ray Absorption Spectroscopy

To resolve the fleeting structures of lanthanide Ln 3+ aqua ions in solution, we (i) performed the first ab initio molecular dynamics (AIMD) simulations of the entire series of Ln 3+ aqua ions in explicit water solvent using pseudopotentials and basis sets recently optimized for lanthanides and (ii) measured the symmetry of the hydrating waters about Ln 3+ ions (Nd 3+ , Dy 3+ , Er 3+ , Lu 3+ ) for the first time with extended X-ray absorption fine structure (EXAFS). EXAFS spectra were measured experimentally and generated from AIMD trajectories to directly compare simulation, which concurrently considers the electronic structure and the atomic dynamics in solution, with experiment. In this study, we performed a comprehensive evaluation of EXAFS multiple-scattering analysis (up to 6.5 Å) to measure Ln–O distances and angular correlations (i.e., symmetry) and elucidate the molecular geometry of the first hydration shell. This evaluation, in combination with symmetry-dependent L 3 - and L 1 -edge spectral analysis, shows that the AIMD simulations remarkably reproduces the experimental EXAFS data. The error in the predicted Ln–O distances is less than 0.07 Å for the later lanthanides, while we observed excellent agreement with predicted distances within experimental uncertainty for the early lanthanides. Our analysis revealed a dynamic, symmetrically disordered first coordination shell, which does not conform to a single molecular geometry for most lanthanides. This work sheds critical light on the highly elusive coordination geometry of the Ln 3+ aqua ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗