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Results for “predictive synthesis”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Liquid crystals of neat boron nitride nanotubes and their assembly into ordered macroscopic materials

Abstract Boron nitride nanotubes (BNNTs) have attracted attention for their predicted extraordinary properties; yet, challenges in synthesis and processing have stifled progress on macroscopic materials. Recent advances have led to the production of highly pure BNNTs. Here we report that neat BNNTs dissolve in chlorosulfonic acid (CSA) and form birefringent liquid crystal domains at concentrations above 170 ppmw. These tactoidal domains merge into millimeter-sized regions upon light sonication in capillaries. Cryogenic electron microscopy directly shows nematic alignment of BNNTs in solution. BNNT liquid crystals can be processed into aligned films and extruded into neat BNNT fibers. This study of nematic liquid crystals of BNNTs demonstrates their ability to form macroscopic materials to be used in high-performance applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High throughput screening of high entropy spinel electrolytes for multivalent batteries

High-entropy (HE) design emerged as a promising path for discovering multivalent superionic conductors. This work provides a computational exploration of the synthesizability of HE spinel-based electrolytes among the typical chemical space. Design principles have been established, while experimental synthesis has supported the stability rules predicted by computational data.

25 ENERGY STORAGE↗
DOE OSTI2021

Surrogate modelling the Baryonic Universe II: On forward modelling the colours of individual and populations of galaxies

ABSTRACT Among the properties shaping the light of a galaxy, the star formation history (SFH) is one of the most challenging to model due to the variety of correlated physical processes regulating star formation. In this work, we leverage the stellar population synthesis model fsps, together with SFHs predicted by the hydrodynamical simulation IllustrisTNG and the empirical model universemachine, to study the impact of star formation variability on galaxy colours. We start by introducing a model-independent metric to quantify the burstiness of a galaxy formation model, and we use this metric to demonstrate that universemachine predicts SFHs with more burstiness relative to IllustrisTNG. Using this metric and principal component analysis, we construct families of SFH models with adjustable variability, and we show that the precision of broad-band optical and near-infrared colours degrades as the level of unresolved short-term variability increases. We use the same technique to demonstrate that variability in metallicity and dust attenuation presents a practically negligible impact on colours relative to star formation variability. We additionally provide a model-independent fitting function capturing how the level of unresolved star formation variability translates into imprecision in predictions for galaxy colours; our fitting function can be used to determine the minimal SFH model that reproduces colours with some target precision. Finally, we show that modelling the colours of individual galaxies with per cent-level precision demands resorting to complex SFH models, while producing precise colours for galaxy populations can be achieved using models with just a few degrees of freedom.

Chaves-Montero, Jonás (ORCID:0000000295534261)↗
DOE OSTI2025

Computational epidemiological tools for pandemic analysis, understanding, and response

This suite of software tools is being developed to enhance and analyze computational epidemiological models that incorporate realistic disease dynamics and human behavior, with the goal of supporting epidemic and pandemic response. Specifically, the tools enable data analysis, feature extraction, data synthesis, machine learning model development, and prediction of key public health outcomes, such as cases, hospitalizations, deaths, and behavioral responses, for airborne infectious diseases like COVID-19 and influenza.

Butts, David↗
DOE OSTI2021

Genome, transcriptome and secretome analyses of the antagonistic, yeast-like fungus Aureobasidium pullulans to identify potential biocontrol genes

Aureobasidium pullulans is an extremotolerant, cosmopolitan yeast-like fungus that successfully colonises vastly different ecological niches. The species is widely used in biotechnology and successfully applied as a commercial biocontrol agent against postharvest diseases and fireblight. However, the exact mechanisms that are responsible for its antagonistic activity against diverse plant pathogens are not known at the molecular level. Thus, it is difficult to optimise and improve the biocontrol applications of this species. As a foundation for elucidating biocontrol mechanisms, we have de novo assembled a high-quality reference genome of a strongly antagonistic A. pullulans strain, performed dual RNA-seq experiments, and analysed proteins secreted during the interaction with the plant pathogen Fusarium oxysporum. Based on the genome annotation, potential biocontrol genes were predicted to encode secreted hydrolases or to be part of secondary metabolite clusters (e.g., NRPS-like, NRPS, T1PKS, terpene, and β-lactone clusters). Transcriptome and secretome analyses defined a subset of 79 A. pullulans genes (among the 10,925 annotated genes) that were transcriptionally upregulated or exclusively detected at the protein level during the competition with F. oxysporum. These potential biocontrol genes comprised predicted secreted hydrolases such as glycosylases, esterases, and proteases, as well as genes encoding enzymes, which are predicted to be involved in the synthesis of secondary metabolites. This study highlights the value of a sequential approach starting with genome mining and consecutive transcriptome and secretome analyses in order to identify a limited number of potential target genes for detailed, functional analyses.

transcriptome↗
DOE OSTI2023

Crystal growth and nucleation at metal-organic framework / solution interfaces

Metal-organic frameworks (MOFs) represent an incredibly diverse class of materials with equally diverse applications in catalysis, separations, and gas storage. Despite the prevalence of MOF structures in the literature, in many cases the synthesis of pure, crystalline MOF materials remains a significant challenge, with failed syntheses often yielding mixed crystalline or non-porous amorphous products. Our recent work aims to enable targeted MOF synthesis by developing a comprehensive and predictive model of the fundamental processes occurring at the MOF-solution interface under synthetically-relevant conditions. Our recent accomplishments include: (i) development and implementation of a graphics processor-accelerated approach to simulation the nucleation of weak electrolytes, and the extension of these methods to large/complex solutes such as MOFs; and (ii) studies of the structure of ZIF interfaces under conditions relevant to crystal growth; and (iii) addressing important open questions in the mechanism of MOF solvent-assisted linker exchange, and important synthetic pathway for MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

A comparison of flight and simulation data for three automatic landing system control laws for the Augmentor wing jet STOL research airplane

Automatic flare and decrab control laws for conventional takeoff and landing aircraft were adapted to the unique requirements of the powered lift short takeoff and landing airplane. Three longitudinal autoland control laws were developed. Direct lift and direct drag control were used in the longitudinal axis. A fast time simulation was used for the control law synthesis, with emphasis on stochastic performance prediction and evaluation. Good correlation with flight test results was obtained.

Feinreich, B.↗
NASA NTRS2012

Compound-Specific Carbon, Nitrogen, and Hydrogen Isotopic Ratios for Amino Acids in CM and CR Chondrites and their use in Evaluating Potential Formation Pathways

Stable hydrogen, carbon, and nitrogen isotopic ratios (oD, 013C, and olSN) of organic compounds can revcal information about their origin and formation pathways. Several formation mechanisms and environments have been postulated for the amino acids detected in carbonaceous chondrites. As each proposed mechanism utilizes different precursor molecules, the isotopic signatures of the resulting amino acids may indicate the most likely of these pathways. We have applied gas chromatography with mass spectrometry and combustion isotope ratio mass spectrometry to measure the compound-specific C, N, and H stable isotopic ratios of amino acids from seven CM and CR carbonaceous chondrites: CM1I2 Allan Hills (ALH) 83100, CM2 Murchison, CM2 Lewis Cliff (LEW) 90500, CM2 Lonewolf Nunataks (LON) 94101, CRZ Graves Nunataks (GRA) 95229, CRZ Elephant Moraine (EET) 92042, and CR3 Queen Alexandra Range (QUE) 99177. We compare the isotopic compositions of amino acids in these meteorites with predictions of expected isotopic enrichments from potential formation pathways. We observe trends of decreasing ODC and increasing oD with increasing carbon number in the aH, (l-NH2 amino acids that correspond to predictions made for formation via Streckercyanohydrin synthesis. We also observe light ODC signatures for ~-alanine, which may indicate either formation via Michael addition or via a pathway that forms primarily small, straight-chain, amine-terminal amino acids (n-ro-amino acids). Higher deuterium enrichments are observed in amethyl amino acids, indicating formation of these amino acids or their precursors in cold interstellar or nebular environments. Finally, individual amino acids are more enriched in deuterium in CR chondrites than CM chondrites, reflecting different parent-body chemistry.

Elsila, Jamie E.↗
NASA NTRS2015

A Synthesis of Hybrid RANS/LES CFD Results for F-16XL Aircraft Aerodynamics

A synthesis is presented of recent numerical predictions for the F-16XL aircraft flow fields and aerodynamics. The computational results were all performed with hybrid RANS/LES formulations, with an emphasis on unsteady flows and subsequent aerodynamics, and results from five computational methods are included. The work was focused on one particular low-speed, high angle-of-attack flight test condition, and comparisons against flight-test data are included. This work represents the third coordinated effort using the F-16XL aircraft, and a unique flight-test data set, to advance our knowledge of slender airframe aerodynamics as well as our capability for predicting these aerodynamics with advanced CFD formulations. The prior efforts were identified as Cranked Arrow Wing Aerodynamics Project International, with the acronyms CAWAPI and CAWAPI-2. All information in this paper is in the public domain.

Luckring, James M.↗
NASA NTRS2022

Liquid crystals of neat boron nitride nanotubes and their assembly into ordered macroscopic materials

Boron nitride nanotubes (BNNTs) have attracted attention for their predicted extraordinary properties; yet, challenges in synthesis and processing have stifled progress on macroscopic materials. Recent advances have led to the production of highly pure BNNTs. Here we report that neat BNNTs dissolve in chlorosulfonic acid (CSA) and form birefringent liquid crystal domains at concentrations above 170 ppmw. These tactoidal domains merge into millimeter- sized regions upon light sonication in capillaries. Cryogenic electron microscopy directly shows nematic alignment of BNNTs in solution. BNNT liquid crystals can be processed into aligned films and extruded into neat BNNT fibers. This study of nematic liquid crystals of BNNTs demonstrates their ability to form macroscopic materials to be used in high- performance applications.

Cedric J. Simonsen Ginestra↗
NASA NTRSDate not supplied

Flight Dynamics Conceptual Design Exploration of Multirotor eVTOL

The advent of electric propulsion is revolutionizing the paradigm of rotorcraft design, leading to new electric Vertical Take-Off and Landing (eVTOL) aircraft. Direct drive topologies are common within these new designs, and some designers have chosen to utilize this mechanism for Primary Flight Control (PFC), effectively utilizing the aircraft engines as PFC actuators to control the speed of the rotors. This decision integrates the propulsion and flight control systems, and intrinsically couples the aircraft sizing and control. Four separate tools were exercised throughout this study to conduct a conceptual design exploration of eVTOL aircraft handling qualities. The main tasks for these tools were: 1) aircraft sizing and performance analysis, including the calculation of trim; 2) flight dynamics modeling and analysis; 3) handling qualities-centric control law optimization; and 4) electric motor sizing. Sizing of an RPM- controlled Hexacopter concept explored the dependency of aircraft size to fundamental design parameters: 1) disk loading and 2) blade loading coefficient. Increasing the design disk loading resulted in designs with high agility, but at the cost of significant growth in the design gross weight. Finally, Categories II and III pilot-induced-oscillation (PIO) are known potentially-catastrophic handling qualities deficiencies of fly-by-wire flight control systems such as those expected of eVTOL aircraft. Consideration of PIO predictive metrics in conceptual design control synthesis led to increased PIO robustness.

Flight Dynamics↗
DOE OSTI2022

High Entropy Alloys: Advanced Synchrotron X-Ray and Neutron Scattering Studies

Synchrotron high energy X-rays and neutrons are powerful complementary tools for structure-property relationship studies by probing materials under complex external stimuli and measuring the bulk-average materials properties and response mechanism, thanks to the high brightness and advanced instrumentations. The research of using the high energy X-ray or neutron scattering instruments at large facilities in high entropy alloys has greatly increased in alloy design, synthesis, property evaluation, performance testing, and predictive model validation, and thus accelerates the next generation structural material discoveries.

An, Ke↗
DOE OSTI2020

Critical differences in 3D atomic structure of individual ligand-protected nanocrystals in solution

Precise three-dimensional (3D) atomic structure determination of individual nanocrystals is a prerequisite for understanding and predicting their physical properties. Nanocrystals from the same synthesis batch display what are often presumed to be small but possibly important differences in size, lattice distortions, and defects, which can only be understood by structural characterization with high spatial 3D resolution. We solved the structures of individual colloidal platinum nanocrystals by developing atomic-resolution 3D liquid-cell electron microscopy to reveal critical intrinsic heterogeneity of ligand-protected platinum nanocrystals in solution, including structural degeneracies, lattice parameter deviations, internal defects, and strain. These differences in structure lead to substantial contributions to free energies, consequential enough that they must be considered in any discussion of fundamental nanocrystal properties or applications.

Kim, Byung Hyo↗
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2022

Computationally Accelerated Discovery of High Entropy Pyrochlore Oxides

High entropy ceramics provide enhanced flexibility for tailoring a wide range of physical properties, emerging from the diverse chemical and configurational degrees of freedom. Expanding upon the endeavors of recently synthesized high entropy ceramics in rock salt, fluorite, spinel, and perovskite structures, we explore the relative feasibility of formation of high entropy pyrochlore oxides, A 2 B 2 O 7 , with multication occupancy of the B-site, estimated from first-principles-based thermodynamic descriptors. Subsequently, we used Monte Carlo simulations to estimate the phase composition, oxygen vacancy concentration, and local ionic segregation as a function of temperature and oxygen partial pressure. In synergy with the theoretical calculations, we have investigated the synthesis of several multicomponent oxides with a pyrochlore composition, related to our computational investigations, resulting in four phase pure and one 97.4% pure high entropy pyrochlore oxides. Ultimately, our approach allows us to evaluate potential impurity phases, ionic disorder, and oxygen vacancy concentration in response to the experimental variables, thereby making realistic predictions that can direct and accelerate experimental synthesis of novel multicomponent ceramics.

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

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗