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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Plasma electron hole kinematics. Hole tracking Particle-In-Cell simulation

The kinematics of a 1-D electron hole is studied using a novel Particle-In-Cell simulation code. A hole tracking technique enables us to follow the trajectory of a fast-moving solitary hole and study quantitatively hole acceleration and coupling to ions. We observe a transient at the initial stage of hole formation when the hole accelerates to several times the cold-ion sound speed. Artificially imposing slow ion speed changes on a fully formed hole causes its velocity to change even when the ion stream speed in the hole frame greatly exceeds the ion thermal speed, so there are no reflected ions. The behavior that we observe in numerical simulations agrees very well with our analytic theory of hole momentum conservation and the effects of “jetting.”

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Observation and Control of Unidirectional Ballistic Dynamics of Nanoparticles at a Liquid–Gas Interface by 4D Electron Microscopy

Understanding and controlling the dynamics of active Brownian objects far from equilibrium are fundamentally important for emerging technologies such as artificial micro/nanomotors for drug deliveries and noninvasive microsurgery. However, direct observation and control of unidirectional propulsion of individual nanoscale objects are technically challenging due to the required spatiotemporal resolution. Here, we report in situ visualization and manipulation of unidirectional superfast ballistic dynamics of a single-photon-activated gold nanoparticle (NP) along the liquid–gas interface by four-dimensional electron microscopy (4D EM) at nanometer and nanosecond scales. We observed that, upon repetitive femtosecond laser excitation, the NP at the liquid–gas interface exhibits a continuously superfast unidirectional translation with a linear dependence of its root mean squared velocity (νrms) on either the laser fluence or repetition rate. Under a single femtosecond pulse excitation, the NP exhibits a superfast ballistic translation at the nanosecond time scale. Combined experiment and physical modeling reveals that the superfast unidirectional, ballistic translation is driven by unidirectional random impulsive forces arising from the nanobubbles (NBs) induced by enhanced laser heating as a result of plasmonic excitation, which is controllable by tuning the laser characteristics. Furthermore, this directional plasmonic NB-propulsion mechanism sheds light on the design of light-controllable artificially intelligent micro/nanomotor systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A protein scaffold enables hydrogen evolution for a Ni-bisdiphosphine complex

We find that an artificial metalloenzyme acting as a functional biomimic of hydrogenase enzymes was activated by assembly via covalent attachment of the molecular complex, [Ni(PN glycine P) 2 ] 2- , within a structured protein scaffold. Electrocatalytic H2 production was observed from pH 3.0 to 10.0 for the artificial enzyme, while no electrocatalytic activity was observed for similar [Ni(PNP) 2 ] 2+ systems.

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

Long term predictions of coal fired power plant data using evolved recurrent neural networks

Abstract This work presents an investigation into the ability of recurrent neural networks (RNNs) to provide long term predictions of time series data generated by coal fired power plants. While there are numerous studies which have used artificial neural networks (ANNs) to predict coal plant parameters, to the authors’ knowledge these have almost entirely been restricted to predicting values at the next time step, and not farther into the future. Using a novel neuro-evolution strategy called Evolutionary eXploration of Augmenting Memory Models (EXAMM), we evolved RNNs with advanced memory cells to predict per-minute plant parameters and per-hour boiler parameters up to 8 hours into the future. These data sets were challenging prediction tasks as they involve spiking behavior in the parameters being predicted. While the evolved RNNs were able to successfully predict the spikes in the hourly data they did not perform very well in accurately predicting their severity. The per-minute data proved even more challenging as medium range predictions miscalculated the beginning and ending of spikes, and longer range predictions reverted to long term trends and ignored the spikes entirely. We hope this initial study will motivate further study into this highly challenging prediction problem. The use of fuel properties data generated by a new Coal Tracker Optimization (CTO) program was also investigated and this work shows that their use improved predictive ability of the evolved RNNs.

Automation & Control Systems↗
DOE OSTI2025

Designing Cyclic Nitrogen‐Bridged Sulfonamides with Anti‐Cancer Activity

The N-bridgehead heterocyclic structure is an abundant motif in a multitude of natural products. This structural feature is of high interest because it is present in many different bioactive molecules, many of which are well-established pharmaceuticals. The introduction of a sulfone group into the N-bridgehead system yields a new core structure containing a N-bridgehead sulfonamide. While linear sulfonamides can be found in natural products, only artificial cyclic sulfonamides are known to date. Applications of related cyclic sulfonamide compounds include matrix metalloproteinase inhibitors, potential HIV and cancer therapeutics, and anti-inflammatory compounds. To explore the potential bioactivity of the N-bridgehead sulfonamide scaffold, a synthetic route toward these scaffolds is developed and their bioactivity is explored against different cancer cell lines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Genome organization and botanical diversity

Abstract The rich diversity of angiosperms, both the planet's dominant flora and the cornerstone of agriculture, is integrally intertwined with a distinctive evolutionary history. Here, we explore the interplay between angiosperm genome organization and botanical diversity, empowered by genomic approaches ranging from genetic linkage mapping to analysis of gene regulation. Commonality in the genetic hardware of plants has enabled robust comparative genomics that has provided a broad picture of angiosperm evolution and implicated both general processes and specific elements in contributing to botanical diversity. We argue that the hardware of plant genomes—both in content and in dynamics—has been shaped by selection for rather substantial differences in gene regulation between plants and animals such as maize and human, organisms of comparable genome size and gene number. Their distinctive genome content and dynamics may reflect in part the indeterminate development of plants that puts strikingly different demands on gene regulation than in animals. Repeated polyploidization of plant genomes and multiplication of individual genes together with extensive rearrangement and differential retention provide rich raw material for selection of morphological and/or physiological variations conferring fitness in specific niches, whether natural or artificial. These findings exemplify the burgeoning information available to employ in increasing knowledge of plant biology and in modifying selected plants to better meet human needs.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Engineered IL-18 variants with half-life extension and improved stability for cancer immunotherapy

Background The pro-inflammatory cytokine, interleukin-18 (IL-18), plays an instrumental role in bolstering anti-tumor immunity. However, the therapeutic application of IL-18 has been limited due to its susceptibility to neutralization by IL-18 binding protein (IL-18BP), short in vivo half-life, and unfavorable physicochemical properties. Methods In order to overcome the poor drug-like properties of IL-18, we installed an artificial disulfide bond, removed the native, unpaired cysteines, and fused the stabilized cytokine to an IgG Fc domain. The stability, potency, pharmacokinetic and pharmacodynamic properties as well as efficacy of disulfide-stabilized IL-18 Fc-fusion (dsIL-18-Fc) were assessed via in vitro and in vivo studies. Results The stability and mammalian host cell production yields of dsIL-18-Fc were improved, compared to the wild-type (WT) cytokine, while maintaining its biological potency and interactions with IL-18 receptor α (IL-18Rα) and IL-18BP. Recombinant fusion of the cytokine to an IgG Fc domain provided extended half-life. Notably, despite maintaining sensitivity to IL-18BP, dsIL-18-Fc was effective at activating both T and natural killer (NK) cells, and elicited a strong anti-tumor response, either as a single agent, or in conjunction with anti-programmed cell death-ligand 1 (anti-PD-L1) therapy. Conclusions We engineered IL-18 for reinforced stability, extended half-life, and improved manufacturability. The therapeutic benefit of dsIL-18-Fc, coupled with a more favorable manufacturability profile and enhanced drug-like properties, underscores the potential utility of this engineered cytokine in cancer immunotherapy.

Immunology↗
DOE OSTI2024

Insight into molecular basis and dynamics of full-length CRaf kinase in cellular signaling mechanisms

Raf kinases play key roles in signal transduction in cells for regulating proliferation, differentiation, and survival. Despite decades of research into functions and dynamics of Raf kinases with respect to other cytosolic proteins, understanding Raf kinases is limited by the lack of their full-length structures at the atomic resolution. Here, we present the first model of the full-length CRaf kinase obtained from artificial intelligence/machine learning algorithms with a converging ensemble of structures simulated by large-scale temperature replica exchange simulations. Our model is validated by comparing simulated structures with the latest cryo-EM structure detailing close contacts among three key domains and regions of the CRaf. Our simulations identify potentially new epitopes of intramolecule interactions within the CRaf and reveal a dynamical nature of CRaf kinases, in which the three domains can move back and forth relative to each other for regulatory dynamics. The dynamic conformations are then used in a docking algorithm to shed insight into the paradoxical effect caused by vemurafenib in comparison with a paradox breaker PLX7904. In this study, we propose a model of Raf-heterodimer/KRas-dimer as a signalosome based on the dynamics of the full-length CRaf.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Bi-Layer Dense/Porous Solid Electrolyte Interphase for Enhanced Lithium-Metal Stability

Due to its high theoretical capacity and low electrochemical potential, lithium metal is a highly investigated anode for next-generation high-energy batteries. However, the unstable chemical and topographical heterogeneous surface of lithium gives rise to safety and efficiency concerns that prevent it from being utilized in practical applications. Exposure to electrolyte leads to non-uniform, mixed organic-inorganic solid-electrolyte interphase (SEI) formation, starting a cascading to non-uniform flux distribution, consumption of active materials, and dendrite growth. The SEI is the key feature that will lead to harnessing the capabilities possible with lithium metal. In this work, the formation of a closed-host bi-layer solid electrolyte interphase (SEI) improves the stability of lithium anode. This is successfully realized by forming an interconnected porous LiF-rich artificial SEI in contact with Li metal, and a dense, stable in-situ formed upper layer SEI. The porous layer increases the number of Li/LiF interfaces, which reduces local volume fluctuations and improves Li+ diffusion along these interfaces. Additionally, the tortuous porous structure guides uniform Li+ flux distribution and mechanically suppresses dendrite propagation. The dense upper layer of the SEI accomplishes a closed-host design through reaction with LiNO3 additive and prevents continuous consumption of active materials seen without the additive included in the electrolyte. The duality of a dense top layer with porous bottom layer led to extended cycle life and improved rate performance, evidenced with symmetric cell testing, as well as full cell testing paired with sulfur and LiFePO4 (LFP) cathodes. This work is a good example of a rational design of the SEI, based on comprehensive consideration of various critical factors to improve Li-metal anode stability, and highlights a new pathway to improve cycling and rate performances of Li metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

τ-SIMPLE Algorithm for the closure problem in homogenization of stokes flows

Estimation of the permeability tensor is crucial for many natural and industrial porous media applications. Homogenization provides a rigorous framework to calculate effective parameters from pore-scale images of a representative unit cell of the porous medium by solving a boundary value problem, also known as closure problem, subject to global constraints. However, the latter are hard to satisfy for arbitrarily complex geometries. We have developed a novel computational algorithm to calculate rigorously the permeability tensor. The approach, here referred to as τ-SIMPLE, is based on introducing an artificial time scale τ to satisfy the global constraint within the SIMPLE iteration. Here, we show that the proposed algorithm has high accuracy for both two-dimensional and three-dimensional periodically arranged geometries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Light–matter coupling in large-area van der Waals superlattices

Two-dimensional (2D) crystals have renewed opportunities in design and assembly of artificial lattices without the constraints of epitaxy. However, the lack of thickness control in exfoliated van der Waals (vdW) layers prevents realization of repeat units with high fidelity. Recent availability of uniform, wafer-scale samples permits engineering of both electronic and optical dispersions in stacks of disparate 2D layers with multiple repeating units. In this study, we present optical dispersion engineering in a superlattice structure comprising alternating layers of 2D excitonic chalcogenides and dielectric insulators. By carefully designing the unit cell parameters, we demonstrate greater than 90% narrow band absorption in less than 4 nm of active layer excitonic absorber medium at room temperature, concurrently with enhanced photoluminescence in square-centimetre samples. These superlattices show evidence of strong light–matter coupling and exciton–polariton formation with geometry-tuneable coupling constants. Our results demonstrate proof of concept structures with engineered optical properties and pave the way for a broad class of scalable, designer optical metamaterials from atomically thin layers.

2D Crystals↗
DOE OSTI2023

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. Further, the present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

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

Deep learning at the edge enables real-time streaming ptychographic imaging

Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revealing and Elucidating ALD-Derived Control of Lithium Plating Microstructure

The practical implementation of Li metal batteries is hindered by difficulties in controlling the Li metal plating microstructure. While previous atomic layer deposition (ALD) studies have focused on directly coating Li metal with thin films for the passivation of the electrode–electrolyte interface, a different approach is adopted, situating the ALD film beneath Li metal and directly on the copper current collector. Here, a mechanistic explanation for this simple strategy of controlling the Li metal plating microstructure using TiO 2 grown on copper foil by ALD is presented. In contrast to previous studies where ALD-grown layers act as artificial interphases, this TiO 2 layer resides at the copper–Li metal interface, acting as a nucleation layer to improve the Li metal plating morphology. Upon lithiation of TiO 2 , a Li x TiO 2 complex forms; this alloy provides a lithiophilic surface layer that enables uniform and reversible Li plating. The reversibility of lithium deposition is evident from the champion cell (5 nm TiO 2 ), which displays an average Coulombic efficiency (CE) of 96% after 150 cycles at a moderate current density of 1 mA cm –2 . This simple approach provides the first account of the mechanism of ALD-derived Li nucleation control and suggests new possibilities for future ALD-synthesized nucleation layers.

25 ENERGY STORAGE↗
DOE OSTI2023

Engineered Escherichia coli platforms for tyrosine-derivative production from phenylalanine using phenylalanine hydroxylase and tetrahydrobiopterin-regeneration system

Aromatic compounds derived from tyrosine are important and diverse chemicals that have industrial and commercial applications. Although these aromatic compounds can be obtained by extraction from natural producers, their growth is slow, and their content is low. To overcome these problems, many of them have been chemically synthesized from petroleum-based feedstocks. However, because of the environmental burden and depleting availability of feedstock, microbial cell factories are attracting much attention as sustainable and environmentally friendly processes. To facilitate development of microbial cell factories for producing tyrosine derivatives, we developed simple and convenient tyrosine-producing Escherichia coli platforms with a bacterial phenylalanine hydroxylase, which converted phenylalanine to tyrosine with tetrahydromonapterin as a cofactor, using a synthetic biology approach. By introducing a tetrahydrobiopterin-regeneration system, the tyrosine titer of the plasmid-based engineered strain was 4.63 g/L in a medium supplemented with 5.00 g/L phenylalanine with a test tube. The strains were successfully used to produce industrially attractive compounds, such as tyrosol with a yield of 1.58 g/L by installing a tyrosol-producing module consisting of genes encoding tyrosine decarboxylase and tyramine oxidase on a plasmid. Gene integration into E. coli chromosomes has an advantage over the use of plasmids because it increases genetic stability without antibiotic feeding to the culture media and enables more flexible pathway engineering by accepting more plasmids with artificial pathway genes. Therefore, we constructed a plasmid-free tyrosine-producing platform by integrating five modules, comprising genes encoding the phenylalanine hydroxylase and tetrahydrobiopterin-regeneration system, into the chromosome. The platform strain could produce 1.04 g/L of 3,4-dihydroxyphenylalanine, a drug medicine, by installing a gene encoding tyrosine hydroxylase and the tetrahydrobiopterin-regeneration system on a plasmid. Moreover, by installing the tyrosol-producing module, tyrosol was produced with a yield of 1.28 g/L. We developed novel E. coli platforms for producing tyrosine from phenylalanine at multi-gram-per-liter levels in test-tube cultivation. The platforms allowed development and evaluation of microbial cell factories installing various designed tyrosine-derivative biosynthetic pathways at multi-grams-per-liter levels in test tubes.

09 BIOMASS FUELS↗
DOE OSTI2025

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. Further, the complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗