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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Cofactor Complexes of DesD, a Model Enzyme in the Virulence-related NIS Synthetase Family

The understudied nonribosomal-peptide-synthetase-independent siderophore (NIS) synthetase family has been increasingly associated with virulence in bacterial species due to its key role in the synthesis of hydroxamate and carboxylate "stealth" siderophores. We have identified a model family member, DesD, from Streptomyces coelicolor , to structurally characterize using a combination of a wild-type and a Arg306Gln variant in apo , cofactor product AMP-bound, and cofactor reactant ATP-bound complexes. The kinetics in the family has been limited by solubility and reporter assays, so we have developed a label-free kinetics assay utilizing a single-injection isothermal-titration-calorimetry-based method. We report second-order rate constants that are 50 times higher than the previous estimations for DesD. Our Arg306Gln DesD variant was also tested under identical buffer and substrate conditions, and its undetectable activity was confirmed. These are the first reported structures for DesD, and they describe the critical cofactor coordination. This is also the first label-free assay to unambiguously determine the kinetics for an NIS synthetase.

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

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the Role of SEI Layer in Low-Temperature Performance of Lithium-Ion Batteries

Low-temperature electrolytes (LTEs) have been considered as one of the most challenging aspects for the wide adoption of lithium-ion batteries (LIBs) since the SOA electrolytes cannot sufficiently support the redox reactions at LT resulting in dramatic performance degradation. Although many attempts have been taken by employing various noncarbonate solvent electrolytes, there was a lack of fundamental understanding of the limiting factors for low-temperature operations (e.g., -20 to -40 degrees C). Here, the crucial role of the solid-electrolyte-interface (SEI) in LIB performance at low temperature using a butyronitrile (BN)-based electrolyte was demonstrated. These results suggested that an additive formed SEI with low resistance and low charge transfer dictates the LT performance in terms of capacity and cycle life, presenting a useful guideline in designing new electrolytes to address the LT issue.

25 ENERGY STORAGE↗
DOE OSTI2023

Silica Supported Organometallic Ir I Complexes Enable Efficient Catalytic Methane Borylation

Catalytic C–H borylation is an attractive method for the conversion of the most abundant hydrocarbon, methane (CH 4 ), to a mild nucleophilic building block. However, existing CH 4 borylation catalysts often suffer from low turnover numbers and conversions, which is hypothesized to result from inactive metal hydride agglomerates. Herein we report that the heterogenization of a bisphosphine molecular precatalyst, [(dmpe)Ir(cod)CH 3 ], onto amorphous silica dramatically enhances its performance, yielding a catalyst that is 12-times more efficient than the current standard for CH 4 borylation. The catalyst affords over 2000 turnovers at 150 °C in 16 h with a selectivity of 91.5% for mono- vs diborylation. Higher catalyst loadings improve yield and selectivity for the monoborylated product (H 3 CBpin) with 82.8% yield and >99% selectivity being achieved with 1255 turnovers. X-ray absorption and dynamic nuclear polarization-enhanced solid-state NMR spectroscopic studies identify the supported precatalyst as an Ir I species, and indicate that upon completion of catalysis, multinuclear Ir polyhydrides are not formed. This is consistent with the hypothesis that immobilization of the organometallic Ir species on a surface prevents bimolecular decomposition pathways. Importantly, immobilization of the homogeneous Ir I fragment onto amorphous silica represents a unique and simple strategy to improve the TON and longevity of a CH 4 borylation catalyst.

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

Taming Molecular Folding: Anion-Templated Foldamers with Tunable Quaternary Structures

Higher-order foldamers represent a unique class of supramolecules at the forefront of molecular design. Herein we control quaternary folding using a novel approach that combines halogen bonding (XBing) and hydrogen bonding (HBing). We present the first anion-templated double helices induced by halogen bonds (XBs) and stabilized by “hydrogen bond enhanced halogen bonds” (HBeXBs). Our findings demonstrate that the number and orientation of hydrogen bond (HB) and XB donors significantly affect the quaternary structure and guest selectivity of two similar oligomers. Finally, this research offers new design elements to engineer foldamers and tailor their quaternary structure for specific guest binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Universal scaling laws of keyhole stability and porosity in 3D printing of metals

Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines. We define a dimensionless number, the Keyhole number, to predict aspect ratio of a keyhole and the morphological transition from stable at low Keyhole number to chaotic at high Keyhole number. Furthermore, we discover inherent correlation between keyhole stability and porosity formation in metal 3D printing. By reducing the dimensions of the formulation of these challenging problems, the compact scaling laws will aid process optimization and defect elimination during metal 3D printing, and potentially lead to a quantitative predictive framework.

42 ENGINEERING↗
DOE OSTI2026

Impact of solvent forces and broken symmetry on the assembly of designed proteins at a liquid-solid interface

The era of protein design has enabled the creation of hybrid protein-inorganic interfaces, leading to both surface-directed self-assembly of de novo protein architectures and protein-directed formation of inorganic materials. However, the resulting patterns of protein assembly are often unexpected, implying that essential interactions are not accounted for in current design platforms. Here, we use high-speed atomic force microscopy (AFM) analyzed through machine learning to follow the assembly of protein nanorods in aqueous electrolytes on two types of mica exhibiting disparate symmetry elements, which are imprinted on the overlying hydration structure. Using Monte Carlo simulations, we reproduce the observed phases and show that an observed smectic phase, previously thought to be unstable for non-interacting rods in two dimensions, emerges when crystal symmetry introduces a directional bias. The findings demonstrate the importance of incorporating solvent forces as modulated by the hydration structure inherent to interfacial systems when designing protein assemblies at liquid-crystal interfaces. Coupling physics-based simulations that can account for these factors to de novo protein design algorithms can lead to improved design platforms for bio-inspired, hybrid materials.

bioinspired materials↗
DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗
DOE OSTI2023

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Contrasting c -axis and in-plane uniaxial stress effects on superconductivity and stripe order in La 1.885 Ba 0.115 CuO 4

The cuprate superconductor La 2−x Ba x CuO 4 (LBCO) near x = 0.125 is a striking example of intertwined electronic orders, where 3D superconductivity is anomalously suppressed, allowing spin and charge stripe order to develop. Understanding this interplay remains a key challenge in cuprates, highlighting the necessity of external tuning for deeper insight. While in-plane uniaxial stress enhances superconductivity and suppresses stripe order, the effects of c -axis compression remains largely unexplored. Here, we use muon spin rotation ( μ SR) and AC susceptibility with an in situ piezoelectric stress device to investigate the spin-stripe order and superconductivity in LBCO-0.115 under c-axis compression. The measurements reveal a gradual suppression of the superconducting transition temperature ( T c ) with increasing c -axis stress, in stark contrast to the strong enhancement observed under in-plane stress. We further show that while in-plane stress rapidly reduces both the magnetic volume fraction ( V m ) and the spin-stripe ordering temperature ( T so ), c-axis compression has no effect, with V m and T so exhibiting an almost unchanged behavior up to the highest applied stress of 0.21 GPa. These findings demonstrate a strong anisotropy in stress response.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Attention-based generative models for de novo molecular design

An implementation of attention within the variational autoencoder framework for continuous representation of molecules. The addition of attention significantly increases model performance for complex tasks such as exploration of novel chemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗