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Results for “Structural Optimization”

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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Structural basis for substrate specificity of meso-diaminopimelic acid decarboxylase from Corynebacterium glutamicum

Highlights: • We determined a crystal structure of meso-Diaminopimelic acid decarboxylase from Corynebacterium glutamicum (CgDAPDC). • Binding mode of the PLP cofactor of CgDAPDC were elucidated. • Substrate binding mode of CgDAPDC were elucidated. l-lysine is an essential amino acid that is widely used as a food supplement for humans and animals. meso-Diaminopimelic acid decarboxylase (DAPDC) catalyzes the final step in the de novo l-lysine biosynthetic pathway by converting meso-diaminopimelic acid (meso-DAP) into l-lysine by decarboxylation reaction. To elucidate its molecular mechanisms, we determined the crystal structure of DAPDC from Corynebacterium glutamicum (CgDAPDC). The PLP cofactor is bound at the center of the barrel domain and forms a Schiff base with the catalytic Lys75 residue. We also determined the CgDAPDC structure in complex with both pyridoxal 5′-phosphate (PLP) and the l-lysine product and revealed that the protein has an optimal substrate binding pocket to accommodate meso-DAP as a substrate. Structural comparison of CgDAPDC with other amino acid decarboxylases with different substrate specificities revealed that the position of the α15 helix in CgDAPDC and the residues located on the helix are crucial for determining the substrate specificities of the amino acid decarboxylases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets

Single-wavelength anomalous dispersion (SAD) utilizing anomalous signal from native S atoms, or other atoms with Z ≤ 20, generally requires highly redundant data collected using relatively long-wavelength X-rays. Here, the results from two proteins are presented where the anomalous signal from serendipitously acquired surface-bound Ca atoms with an anomalous data multiplicity of around 10 was utilized to drive de novo structure determination. In both cases, the Ca atoms were acquired from the crystallization solution, and the data-collection strategy was not optimized to exploit the anomalous signal from these scatterers. The X-ray data were collected at 0.98 Å wavelength in one case and at 1.74 Å in the other (the wavelength was optimized for sulfur, but the anomalous signal from calcium was exploited for structure solution). Similarly, using a test case, it is shown that data collected at ~1.0 Å wavelength, where the f '' value for sulfur is 0.28 e, are sufficient for structure determination using intrinsic S atoms from a strongly diffracting crystal. Interestingly, it was also observed that SHELXD was capable of generating a substructure solution from high-exposure data with a completeness of 70% for low-resolution reflections extending to 3.5 Å resolution with relatively low anomalous multiplicity. Considering the fact that many crystallization conditions contain anomalous scatterers such as Cl, Ca, Mn etc. , checking for the presence of fortuitous anomalous signal in data from well diffracting crystals could prove useful in either determining the structure de novo or in accurately assigning surface-bound atoms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Elucidating the Solvation Structure and Dynamics of Lithium Polysulfides Resulting from Competitive Salt and Solvent Interactions

Designing optimal electrolytes is key to enhancing the performance of energy storage devices,especially relating to cycle life, efficiency, and stability.1 Specifically, for future beyond-Li ion energy storage, there is still ample room for electrolyte improvements. Among the candidates for higher gravimetric energy storage, the Li-S battery is considered quite promising, owing to its theoretical specific energy density (2600 Wh/kg) and specific capacity (1675 mAh/g) and significantly lower cost as compared to state-of-art lithium-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Crystal Structural Effect of AuCu Alloy Nanoparticles on Catalytic CO Oxidation

Controlling the physical and chemical properties of alloy nanoparticles (NPs) is an important approach to optimize NP catalysis. Unlike other tuning knobs, such as size, shape, and composition, crystal structure has received limited attention and not been well understood for its role in catalysis. This deficiency is mainly due to the difficulty in synthesis and fine-tuning of the NPs’ crystal structure. Here, Exemplifying by AuCu alloy NPs with face centered cubic (fcc) and face centered tetragonal (fct) structure, we demonstrate a remarkable difference in phase segregation and catalytic performance depending on the crystal structure. During the thermal treatment in air, the Cu component in fcc-AuCu alloy NPs segregates more easily onto the alloy surface as compared to that in fct-AuCu alloy NPs. As a result, after annealing at 250 °C in air for 1 h, the fcc- and fct-AuCu alloy NPs are phase transferred into Au/CuO and AuCu/CuO core/shell structures, respectively. More importantly, this variation in heterostructures introduces a significant difference in CO adsorption on two catalysts, leading to a largely enhanced catalytic activity of AuCu/CuO NP catalyst for CO oxidation. Furthermore, the same concept can be extended to other alloy NPs, making it possible to fine-tune NP catalysis for many different chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Optimization of processing parameters on the controlled growth of c-axis oriented ZnO nanorod arrays

Optimization of the growth time parameter was conducted to synthesize high-quality c-axis ZnO nanorod arrays. The effects of the parameter on the crystal growth and properties were systematically investigated. Our studies confirmed that the growth time influence the properties of ZnO nanorods where the crystallite size of the structures was increased at higher deposition time. Field emission scanning electron microsope analysis confirmed the morphologies structure of the ZnO nanorods. The ZnO nanostructures prepared under the optimized growth conditions showed an intense XRD peak which reveal a higher c-axis oriented ZnO nanorod arrays thus demonstrating the formation of defect free structure.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Design of a high power TM01 mode launcher optimized for manufacturing by milling

Recent research on high-gradient rf acceleration found that hard metals, such as hard copper and hard copper-silver, have lower breakdown rate than soft metals. Traditional high-gradient accelerating structures are manufactured with parts joined by high-temperature brazing. The high temperature used in brazing makes the metal soft; therefore, this process cannot be used to manufacture structures out of hard metal alloys. In order to build the structure with hard metals, the components must be designed for joining without high-temperature brazing. One method is to build the accelerating structures out of two halves, and join them by using a low-temperature technique, at the symmetry plane along the beam axis. The structure has input and output rf power couplers. We use a TM01 mode launcher as a rf power coupler, which was introduced during the Next Linear Collider (NLC) work. The part of the mode launcher will be built in each half of the structure. This paper presents a novel geometry of a mode launcher, optimized for manufacturing by milling. The coupler was designed for the CERN CLIC working frequency f = 11.9942 GHz; the same geometry can be scaled to any other frequency.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning

This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly relevant to nonlinear dynamical systems where modelling approaches involve the use of physics-based and data-driven components. In such cases, the data-driven components, where analytical descriptions of the physical processes are not readily available, are often prone to overfitting, meaning that the empirical aspects of these models will often involve the calibration of an unnecessarily large number of parameters. While an overparameterized model may fit the observed data well, such models may be inadequate for making predictions in regimes that are different from those wherein the data were recorded. In view of this, it is desirable to not only calibrate the model parameters, but also identify the optimal compromise between data fit and model complexity. In this article, we exhibit the optimal model discovery for an aeroelastic system wherein the structural dynamics are well-known and described by a differential equation model, coupled with a semi-empirical aerodynamic model for laminar separation flutter, resulting in low-amplitude limit cycle oscillations (LCO). To illustrate the performance of the algorithm, in this article, we use synthetic data and demonstrate the ability of the algorithm to correctly rediscover the optimal model and model parameters, given a known data-generating model. The synthetic data are generated from a forward simulation of a known differential equation model with parameters selected so as to mimic the dynamics observed in wind-tunnel experiments. Subsequently, we demonstrate the performance of the algorithm for model selection using noisy LCO data from wind tunnel experiments. As there is no ground truth available for the experimental data case, we provide a comparison between NSBL and Bayesian model selection to validate the results, and demonstrate the use of NSBL as an efficient alternative to traditional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Metal Amorphous Nanocomposite (MANC) Alloy Cores with Spatially Tuned Permeability for Advanced Power Magnetics Applications

Metal amorphous nanocomposite (MANC) alloys are an emerging class of soft magnetic materials showing promise for a range of inductive components targeted for higher power density and higher efficiency power conversion applications including inductors, transformers, and rotating electrical machinery. Magnetization reversal mechanisms within these alloys are typically determined by composition optimization as well as controlled annealing treatments to generate a nanocomposite structure composed of nanocrystals embedded in an amorphous precursor. Here in this paper we demonstrate the concept of spatially varying the permeability within a given component for optimization of performance by using the strain annealing process. The concept is realized experimentally through the smoothing of the flux profile from the inner to outer core radius achieved by a monotonic variation in tension during the strain annealing process. Great potential exists for an extension of this concept to a wide range of other power magnetic components and more complex spatially varying permeability profiles through advances in strain annealing techniques and controls.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Structural Mass Saving Potential of a 5-MW Direct-Drive Generator Designed for Additive Manufacturing

As wind turbine blade diameters and tower height increase to capture more energy in the wind, higher structural loads results in more structural support material increasing the cost of scaling. Weight reductions in the generator transfer to overall cost savings of the system. Additive manufacturing facilitates a design-for-functionality approach, thereby removing traditional manufacturing constraints and labor costs. The most feasible additive manufacturing technology identified for large, direct-drive generators in this study is powder-binder jetting of a sand cast mold. A parametric finite element analysis optimization study is performed, optimizing for mass and deformation. Also, topology optimization is employed for each parameter-optimized design.The optimized U-beam spoked web design results in a 24 percent reduction in structural mass of the rotor and 60 percent reduction in radial deflection.

17 WIND ENERGY↗
DOE OSTI2025

HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.

Maiterth, Matthias [ORNL] (ORCID:000000018698460X)↗
DOE OSTI2019

Protein and RNA dynamical fingerprinting

Protein structural vibrations impact biology by steering the structure to functional intermediate states; enhancing tunneling events; and optimizing energy transfer. Strong water absorption and a broad continuous vibrational density of states have prevented optical identification of these vibrations. Recently spectroscopic signatures that change with functional state were measured using anisotropic terahertz microscopy. The technique however has complex sample positioning requirements and long measurement times, limiting access for the biomolecular community. Here we demonstrate that a simplified system increases spectroscopic structure to dynamically fingerprint biomacromolecules with a factor of 6 reduction in data acquisition time. Using this technique, polarization varying anisotropy terahertz microscopy, we show sensitivity to inhibitor binding and unique vibrational spectra for several proteins and an RNA G-quadruplex. The technique’s sensitivity to anisotropic absorbance and birefringence provides rapid assessment of macromolecular dynamics that impact biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mechanistic insights into superionic thioarsenate argyrodite solid electrolytes via machine learning interatomic potentials

The lithium argyrodite sulfide solid electrolyte Li 6 PS 5 Cl has attracted considerable interest for all-solid-state batteries owing to its high ionic conductivity, which can be further enhanced through ionic substitution. Although a variety of substitutions have been investigated, thioarsenate argyrodites remain comparatively underexplored. Here, we systematically investigate the phase stability and Li-ion conduction mechanisms in superionic Br-incorporated thioarsenate argyrodites using first-principles calculations and molecular dynamics simulations based on machine learning interatomic potentials (MLIPs). Systematic variation of S/Br site inversion reveals that an optimal degree of anion disorder significantly enhances inter-cage connectivity and facilitates long-range Li-ion diffusion. Configurational entropy serves as an effective quantitative descriptor of anion disorder, exhibiting a strong correlation with ionic conductivity. While greater anion disorder induced by site inversion and higher Br content enhances ionic conductivity up to 50 mS cm −1 , it simultaneously reduces structural stability. This trade-off results in an optimal window in which a moderate level of disorder yields conductivities exceeding 20 mS cm −1 while maintaining synthetic feasibility. In conclusion, this work highlights the reliability and efficiency of MLIPs for elucidating ion-transport mechanisms and accelerating the design of novel superionic argyrodites.

Jang, Myeongcho [Korea Institute of Science and Te↗
DOE OSTI2021

Optimal Protocols in Quantum Annealing and Quantum Approximate Optimization Algorithm Problems

Quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA) are two special cases of the following control problem: apply a combination of two Hamiltonians to minimize the energy of a quantum state. Which is more effective has remained unclear. Here we analytically apply the framework of optimal control theory to show that generically, given a fixed amount of time, the optimal procedure has the pulsed (or “bang-bang”) structure of QAOA at the beginning and end but can have a smooth annealing structure in between. This is in contrast to previous works which have suggested that bang-bang (i.e., QAOA) protocols are ideal. To support this theoretical work, we carry out simulations of various transverse field Ising models, demonstrating that bang-anneal-bang protocols are more common. Futher, the general features identified here provide guideposts for the nascent experimental implementations of quantum optimization algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Mechanistic Insights into Peptide Binding and Deactivation of an Adhesion G Protein-Coupled Receptor

Adhesion G protein-coupled receptors (ADGRGs) play critical roles in the reproductive, neurological, cardiovascular, and endocrine systems. In particular, ADGRG2 plays a significant role in Ewing sarcoma cell proliferation, parathyroid cell function, and male fertility. In 2022, a cryo-EM structure was reported for the active ADGRG2 bound by an optimized peptide agonist IP15 and the Gs protein. The IP15 peptide agonist was also modified to antagonists 4PH-E and 4PH-D with mutations of the 4PH residue to Glu and Asp, respectively. However, experimental structures of inactive antagonist-bound ADGRs remain to be resolved, and the activation mechanism of ADGRs such as ADGRG2 is poorly understood. Here, we applied Gaussian accelerated molecular dynamics (GaMD) simulations to probe conformational dynamics of the agonist- and antagonist-bound ADGRG2. By performing GaMD simulations, we were able to identify important low-energy conformations of ADGRG2 in the active, intermediate, and inactive states, as well as explore the binding conformations of each peptide. Moreover, our simulations revealed critical peptide-receptor residue interactions during the deactivation of ADGRG2. In conclusion, through GaMD simulations, we uncovered mechanistic insights into peptide (agonist and antagonist) binding and deactivation of the ADGRG2. These findings will potentially facilitate rational design of new peptide modulators of ADGRG2 and other ADGRs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Correlating Local Compositions and Structures with the Macroscopic Optical Properties of Ce 3+ -Doped CaSc 2 O 4 , an Efficient Green-Emitting Phosphor

Calcium scandate (CaSc 2 O 4 ) substituted with small amounts (<1%) of Ce 3+ is a recently discovered bright-green-emitting phosphor with favorable light absorption and emission properties and robust temperature stability that make it well-suited for solid-state white-lighting applications. Combined analyses of scattering, solid-state nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR), and photoluminescence measurements establish the compositional and structural origins of the macroscopic optical properties of this phosphor material. Simultaneous refinements of synchrotron X-ray and neutron diffraction data of Ce 3+ -doped CaSc 2 O 4 enable the average crystal structure to be determined, which is shown to correspond to an exceedingly rigid host structure, as corroborated by density functional theory (DFT) calculations. Such structural rigidity leads to high quantum efficiency, which is optimized by the substitution of as little as 0.5 mol % of Ce 3+ for Ca 2+ ions, with higher extents of Ce 3+ substitution leading to decreased photoluminescent quantum yields. Solid-state 43 Ca and 45 Sc magic-angle spinning (MAS) NMR spectra are sensitive to the effects of the paramagnetic Ce 3+ dopant ions on nearby atoms in the host structure and yield evidence for local structural distortions. EPR measurements provide direct insights on structures of the Ce 3+ ions, as a function of Ce 3+ substitution. The combined scattering and spectroscopic analyses yield detailed new understanding of the local and long-range structures of Ce 3+ -doped CaSc 2 O 4 , which account for the sensitive composition-dependent optical properties of this important phosphor material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural evolution and magnetic hardness of (Sm,Zr)(Fe,Co,Ti) 12 alloy particles via reduction-diffusion

The quest for achieving high coercivity in Sm(Fe,Co,Ti) 12 alloys, despite their inherent strong magnetocrystalline anisotropy, has posed significant challenges. Recently, (Sm,Zr)(Fe,Co,Ti) 12 monocrystalline particles have exhibited coercivity μ 0 H c > 1.2 T, showcasing promising prospects and significant potential for both manufacturing and research endeavors. This study delves into the structural evolution of (Sm,Zr)(Fe,Co,Ti) 12 (1:12) alloy particles made via the calciothermic reduction-diffusion synthesis process as influenced by the molar ratios of Ca atoms to O 2- ions (Ca/O), annealing time and annealing temperature. Critical insight that informs conditions to optimize the magnetic response is gained via systematic experimentation and advanced electron microscopy. Complex structural features, including core-shell morphologies and intricate multiphase compositions within individual particles, are unveiled. An optimal Ca/O ratio of 1.30 produces particles with a coercivity up to μ 0 H c = 1.63 T, while higher Ca/O ratios induce the formation of a Sm-rich TbCu 7 -type (1: 7 ) phase, which only partially transforms into the desired 1:12 phase during annealing. Persistent remnants of the 1:7 phase locally impact atomic structure, particle morphology, and coercivity. Furthermore, these findings underscore the complex interplay between synthesis parameters, resulting structures, and magnetic properties, informing the design and optimization of high-performance permanent magnets comprised of the (1:12) compound.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bench-Scale Testing of Monolithic PPI Structured Contactors for Direct Air Capture of CO 2

The overall project objective was to develop, optimize and bench-scale test the integrated embodiment of a leading direct air capture (DAC) sorbent composition, linear-poly(propylenimine) (l-PPI), in a structured material system, specifically a monolithic contactor for achieving low pressure drop, to justify its further scale-up in a subsequent program. The project team was led by CORMETECH, a leading monolithic gas/solid contactor manufacturer for environmental applications, and included Global Thermostat, a well-known DAC start-up, and Georgia Tech, a prominent US academic institution in carbon capture and DAC. The monolithic contactor for l-PPI in this project employed an advanced, cost-effective fabrication technique from CORMETECH, namely a porous monolith substrate directly impregnated with the amine, an approach contrasting to the traditional wash-coating methods used for activating monoliths. The l-PPI monolithic contractor is targeted for use in DAC systems such as the Global Thermostat DAC process, which involves the cyclic operation of ambient air flow over a monolithic amine contactor followed by steam-mediated thermal desorption and CO 2 collection, maximizing volumetric productivity while reducing the auxiliary power required to capture CO 2 from air. The Global Thermostat DAC process currently utilizes poly(ethyleneimine) (PEI) materials as baseline sorbents, which are subject to oxidative degradation and slow capacity fade at the elevated temperature required for efficient CO 2 removal. Georgia Tech, with Global Thermostat, had discovered in lab-scale efforts that l-PPI had superior resistance to oxidative degradation compared to PEI, with similar CO 2 adsorption capacity. While the novel l-PPI sorbent offers significant advantages over PEI for DAC, including potential design simplification and increased process efficiency, it is not commercially available, and prior to this project, had not yet been evaluated on the bench-scale in a structured contactor.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redoxactive materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the- art machine learning ( ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. Furthermore, we anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗