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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nonlinear Topological Photonics: Capturing Nonlinear Dynamics and Optical Thermodynamics

Combining multiple optical resonators or engineering dispersion of complex media has provided an effective method for demonstrating topological physics controlling photons in unprecedented ways such as unidirectional light propagation and spatially localized modes between an interface or on a corner. Further, adding nonlinear responses to those topological photonic systems has enabled achieving diverse phases of photons in both space and time, allowing for more functionalities in photonic devices that provide a new playground for studying dynamic features of nonlinear topological systems. However, most methods for describing nonlinear topological photonic systems rely on linear topological theories, making it challenging to accurately characterize the topology of nonlinear systems. Thus, substantial efforts have focused on rigorously describing nonlinear topological phases and developing effective tools to analyze nonlinear topological effects. Meanwhile, coupled multimode optical waveguides with nonlinear dynamic responses provide an excellent platform for the statistical description of photons, opening a new paradigm called “optical thermodynamics”. This review will introduce the basic concepts of nonlinear topological photonics and the recent development of theoretical approaches focusing on data-driven approaches for creating phase diagrams as well as the spectral localizer framework and the pseudospectrum method for understanding optical nonlinearities in topological systems. In addition, the new concept of optical thermodynamics will be introduced with some recent theoretical works.

Topological photonics↗
DOE OSTI2022

Monitoring Fracture Saturation With Internal Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply scattered or reflected waves. However, a challenge arises when multiply scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural or induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (TNN also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The TNN with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. Furthermore, these results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2023

Finite-difference time-domain methods

The finite-difference time-domain (FDTD) method is a widespread numerical tool for full-wave analysis of electromagnetic fields in complex media and for detailed geometries. Applications of the FDTD method cover a range of time and spatial scales, extending from subatomic to galactic lengths and from classical to quantum physics. Technology areas that benefit from the FDTD method include biomedicine — bioimaging, biophotonics, bioelectronics and biosensors; geophysics — remote sensing, communications, space weather hazards and geolocation; metamaterials — sub-wavelength focusing lenses, electromagnetic cloaks and continuously scanning leaky-wave antennas; optics — diffractive optical elements, photonic bandgap structures, photonic crystal waveguides and ring-resonator devices; plasmonics — plasmonic waveguides and antennas; and quantum applications — quantum devices and quantum radar. This Primer summarizes the main features of the FDTD method, along with key extensions that enable accurate solutions to be obtained for different research questions. Additionally, hardware considerations are discussed, plus examples of how to extract magnitude and phase data, Brillouin diagrams and scattering parameters from the output of an FDTD model. Furthermore, the Primer ends with a discussion of ongoing challenges and opportunities to further enhance the FDTD method for current and future applications.

42 ENGINEERING↗
DOE OSTI2021

Connection between the physicochemical characteristics of amorphous carbon thin films and their electrochemical properties

Connecting a material's surface chemistry with its electrocatalytic performance is one of the major questions in analytical electrochemistry. This is especially important in many sensor applications where analytes from complex media need to be measured. Unfortunately, today this connection is still largely missing except perhaps for the most simple ideal model systems. Here we present an approach that can be used to obtain insights about this missing connection and apply it to the case of carbon nanomaterials. In this paper we show that by combining advanced computational techniques augmented by machine learning methods with x-ray absorption spectroscopy (XAS) and electrochemical measurements, it is possible to obtain a deeper understanding of the correlation between local surface chemistry and electrochemical performance. As a test case we show how by computationally assessing the growth of amorphous carbon (a-C) thin films at the atomic level, we can create computational structural motifs that may in turn be used to deconvolute the XAS data from the real samples resulting in local chemical information. Then, by carrying out electrochemical measurements on the same samples from which x-ray spectra were measured and that were further characterized computationally, it is possible to gain insight into the interplay between the local surface chemistry and electrochemical performance. To demonstrate this methodology, we proceed as follows: after assessing the basic electrochemical properties of a-C films, we investigate the effect of short HNO 3 treatment on the sensitivity of these electrodes towards an inner sphere redox probe dopamine to gain knowledge about the influence of altered surface chemistry to observed electrochemical performance. Overall, these results pave the way towards a more general assessment of electrocatalysis in different systems and provide the first steps towards data driven tailoring of electrode surfaces to gain optimal performance in a given application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Low-Cost X-Ray CT System for Imaging of Roots

The goal of this project was to develop and demonstrate an innovative, low cost, field deployable, stationary 3D x-ray computed tomography (CT) system that will image total root phenotypes with a micron size resolution at a throughput of hundreds of plants per cycle. This system is based on UHV’s unique low cost linear x-ray tube technology and sophisticated reconstruction & image segmentation algorithms developed at University of Massachusetts, Lowel and University of Nottingham; and was tested for several types of soils at University of Wisconsin and Texas A&M University. Currently, no technologies exist that have been designed to image roots in complex media such as agricultural field conditions. Due to its small size, high resolution & fast imaging of fine roots, low power consumption, large penetration depth (i.e. ability to see through several feet of soil) and ease of field deployability, this CT system will increase the speed and efficacy of discovery, field translation, and deployment of improved crops and systems that improve soil carbon accumulation and storage, decrease N2O emissions, and improve water efficiency leading towards advancements that could mitigate 10% of the total US Greenhouse gases. This degree of imaging in the field has never been available and would be invaluable to scientists in understanding how environmental conditions and phenotypic variations contribute to carbon deposition through root development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Engineering Terpene Production Pathways in Methylobacterium extorquens AM1

Terpenes are diverse specialized metabolites naturally found within plants and have important roles in inter-species communication, adaptation and interaction with the environment. Their industrial applications span a broad range, including fragrances, flavors, cosmetics, natural colorants to agrochemicals and therapeutics, yet formal chemical synthesis is economically challenging due to structural complexities. Engineering terpene biosynthesis could represent an alternative in microbial biotechnological workhorses, such as Saccharomyces cerevisiae or Escherichi coli, utilizing sugars or complex media as feedstocks. Host species that metabolize renewable and affordable carbon sources may offer unique sustainable biotechnological alternatives. Methylotrophs are bacteria with the capacity to utilize one-carbon feedstocks, such as methanol or formate. They colonize the phyllosphere (above-ground area) of plants, and many accumulate abundant carotenoid pigments. Methylotrophs have the capacity to take up and use a subset of the rare earth elements known as lanthanides. These metals can enhance one-carbon (methylotrophic) metabolism. Here, we investigated whether manipulating the metabolism enables and enhances terpene production. A carotenoid-deficient mutant potentially liberates carbon, which may contribute to bioproduct accumulation. To test this hypothesis, terpene-producing bacterial strains regulated by two distinct promoters were generated. Wildtype Methylobacterium extorquens, ∆Meta1_3665, a methylotrophic mutant lacking the carotenoid pathway, and an E. coli strain were transformed with an exogenous terpene pathway and grown both in the presence and absence of lanthanides. The extraction, and the comparison of analytical profiles, provided evidence that engineered cultured M. extorquens under control of a native, inducible methylotrophic promoter can yield the sesquiterpene patchoulol when supplemented with lanthanide. In contrast, using a moderate-strength constitutive promoter failed to give production. We demonstrated colonization of the phyllosphere with the engineered strains, supporting the future engineering of selected species of the plant microbiome and with promising implications for the synthetic biology of small molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Magnetic-Core/Gold-Shell Nanoparticles for the Detection of Hydrophobic Chemical Contaminants

Magnetic-core/gold-shell nanoparticles (MAuNPs) are of interest for enabling rapid and portable detection of trace adulterants in complex media. Gold coating provides biocompatibility and facile functionalization, and a magnetic core affords analyte concentration and controlled deposition onto substrates for surface-enhanced Raman spectroscopy. Iron oxide cores were synthesized and coated with gold by reduction of HAuCl4 by NH2OH. MAuNPs were grafted with polyethylene glycol (PEG) and/or functionalized with 4-mercaptobenzoic acid (4-MBA) and examined using a variety of microscopic, spectroscopic, magnetometric, and scattering techniques. For MAuNPs grafted with both PEG and 4-MBA, the order in which they were grafted impacted not only the graft density of the individual ligands, but also the overall graft density. Significant Raman signal enhancement of the model analyte, 4-MBA, was observed. This enhancement demonstrates the functionality of MAuNPs in direct detection of trace contaminants. The magnetic deposition rate of MAuNPs in chloroform and water was explored. The presence of 4-MBA slowed the mass deposition rate, and it was postulated that the rate disparity originated from differing NP-substrate surface interactions. These findings emphasize the importance of ligand choice in reference to the medium, target analyte, and substrate material, as well as functionalization procedure in the design of similar sensing platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data for Monitoring Fracture Saturation with Internal Transportable Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply-scattered or reflected waves. However, a challenge arises when multiply-scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The twin neural network with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. These results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
NASA NTRS1986

Carbon dioxide evolution rate as a method to monitor and control an aerobic biological waste treatment system

An experimental system was developed to study the microbial growth kinetic of an undefined mixed culture in an erobic biological waste treatment process. The experimental results were used to develop a mathematical model that can predict the performance of a bioreactor. The bioreactor will be used to regeneratively treat waste material which is expected to be generated during a long term manned space mission. Since the presence of insoluble particles in the chemically undefined complex media made estimating biomass very difficult in the real system, a clean system was devised to study the microbial growth from the soluble substrate.

Lee, S. S.↗
NASA NTRS1994

Advanced studies of electromagnetic scattering

In radar signature applications it is often desirable to generate the range profiles and inverse synthetic aperture radar (ISAR) images of a target. They can be used either as identification tools to distinguish and classify the target from a collection of possible targets, or as diagnostic/design tools to pinpoint the key scattering centers on the target. The simulation of synthetic range profiles and ISAR images is usually a time intensive task and computation time is of prime importance. Our research has been focused on the development of fast simulation algorithms for range profiles and ISAR images using the shooting and bouncing ray (SBR) method, a high frequency electromagnetic simulation technique for predicting the radar returns from realistic aerospace vehicles and the scattering by complex media.

Ling, Hao↗
NASA NTRS1996

Accumulation of Dissolved DMSP by Marine Bacteria and its Degradation Via Bacterivory

Several bacterial isolates enriched from seawater using complex media were able to accumulate dimethylsulfoniopropionate (DMSP) from media into cells over several hours without degrading it. Uptake only occurred in metabolically active cells, and was repressed in some strains by the presence of additional carbon sources. Accumulation was also more rapid in osmotically-stressed cells, suggesting DMSP is used as an osmotic solute. Uptake could be blocked by inhibitors of active transport systems (2,4-dinitrophenol, azide, arsenate) and of protein synthesis (chloramphenicol). Some structural analogs such as glycine betaine and S-methyl methionine also blocked DMSP uptake, suggesting that the availability of alternate organic osmolytes may influence DMSP uptake. Stresses such as freezing, heating, or osmotic down shock resulted in partial release of DMSP back to the medium. One strain which contained a DMSP-lyase was also able to accumulate DMSP, and DMS was only produced in the absence of alternate carbon sources. Bacteria containing DMSP were prepared as prey for bacterivorous ciliates and flagellates, to examine the fate of the DMSP during grazing. In all cases, predators metabolized the DMSP in bacteria. In some cases, DMS was produced, but it is not clear if this was due to the predators or to associated bacteria in the non-axenic grazer cultures. Bacterivores may influence DMSP cycling by either modulating populations of DMSP-metabolizing bacteria, or by metabolizing DMSP accumulated by bacterial prey.

Wolfe, Gordon V.↗
DOE OSTI2023

Application of Machine Learning to Improve Biobased Glucaric Acid Production

Kalion, Inc. is an industrial biotechnology manufacturer of high-purity chemicals from biomass including glucaric acid to be used in the industrial, material, and pharmaceutical markets. The project goal is application of machine learning in conjunction with high-throughput cultivations and metabolomics to understand and overcome two different challenges: (i) improve glucaric acid productivity (the rate of production slows considerably after 48 hours, which limits the overall productivity that can be achieved in the process), and (ii) decrease its production costs (components from complex media are necessary to achieve robust production). Through a series of experiments, a set of metabolites contained in rich media that improved performance were identified using metabolomics, high throughput cultivations in bioreactors, and machine learning-friendly experimental designs. It was found that the substrate feeding strategy was the potential major driver to enhance bacterial performance of glucaric acid production. The major industrial impacts by this project are the improvement of the production process technology, as well as advancing infrastructures and workflows (where machine learning approaches in conjunction with high-throughput cultivation and metabolomics are used) to improve the analyses by making them more rapid and comprehensive.

BIOMASS FUELS↗
DOE OSTI2024

Learning a general model of single phase flow in complex 3D porous media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Dimensionality effects on multicomponent ionic transport and surface complexation in porous media

Coulombic interactions between charged species in pore water and at surface/solution interfaces are of pivotal importance for multicomponent reactive transport in porous media. In this study, we investigate the impact of domain dimensionality on electrostatically coupled dispersion and surface-solution reactions during transport of acidic plumes and major ions in porous media. Column and quasi two-dimensional flow-through experiments were performed, with identical silica porous media and under the same advection-dominated conditions. Equal mass fluxes of different electrolyte solutions (i.e., HCl - pH ~ 2.8, NaBr - 100 mM, HCl - pH ~ 2.8 plus NaBr - 100 mM) were continuously injected in the 1-D and 2-D experiments and breakthrough curves of pH and major ions were measured at the outlet of the domains. The presence of pronounced ionic strength gradients in the transverse direction in the 2-D setup caused distinct retardation and transport behaviors of protons and major ions which were not observed in the one-dimensional column experiments. Furthermore, in the cases of salt electrolytes injection, considerably enhanced release of H + (>61%) from the quartz surface was observed in the multidimensional system compared to the one-dimensional setup. Reactive transport modeling was performed to reproduce the experimental outcomes and to analyse the coupling between transport processes, based on the Nernst-Planck formulation of diffusive/dispersive fluxes and on surface complexation reactions at the solid-solution interface. Electrostatic interactions between Na + , B – , and H + , and deprotonation of the quartz surface upon the formation of sodium outer-sphere complexes, are the primary controllers of the spatial and temporal features displayed by the pH and major ions measurements. The reactive transport simulations allowed us to interpret the experimental observations, to visualize the distribution and spatio-temporal evolution of dissolved and solid species, to identify a spatially heterogeneous zonation of Coulombic interactions with distinct behavior at the fringe and core of the injected plumes in the multidimensional setup, and to quantify the different components of the Nerst-Planck fluxes of the charged solutes. This study demonstrates that the domain dimensionality directly affects electrostatic interactions between charged aqueous species in the pore water and surface complexation reactions at the solid-solution interface. The non-trivial effects of dimensionality on multicomponent ionic transport result in a significantly different behavior in 1-D and 2-D systems.

58 GEOSCIENCES↗
DOE OSTI2022

Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics

The bacterial exometabolome consists of a vast array of specialized metabolites, many of which are only produced in response to specific environmental stimuli. For this reason, it is desirable to control the extracellular environment with a defined growth medium composed of pure ingredients. However, complex (undefined) media are expected to support the robust growth of a greater variety of microorganisms than defined media. Here, we investigate the trade-offs inherent to a range of complex and defined solid media for the growth of soil microorganisms, production of specialized metabolites, and detection of these compounds using direct infusion mass spectrometry. We find that complex media support growth of more soil microorganisms, as well as allowing for the detection of more previously discovered natural products as a fraction of total m/z features detected in each sample. However, the use of complex media often caused mass spectrometer injection failures and poor-quality mass spectra, which in some cases resulted in over a quarter of samples being removed from analysis. Defined media, while more limiting in growth, generated higher quality spectra and yielded more m/z features after background subtraction. These results inform future exometabolomic experiments requiring a medium that supports the robust growth of many soil microorganisms.

59 BASIC BIOLOGICAL SCIENCES↗