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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

External cavity type-I quantum well cascade diode lasers with a tuning range of 440 nm near 3 μm

A three-stage cascade GaSb-based diode laser heterostructure with an enhanced optical gain spectral bandwidth was designed and fabricated. The gain broadening was achieved by varying the thickness of the type-I quantum wells in different stages of the cascade active region from 10 to 14 nm. The structures were processed into bent ridge gain chips with virtually eliminated feedback from the anti-reflection-coated angled facet. In conclusion, the external cavity devices based on a novel gain chip design demonstrated a record wide tuning range from 2.79 to 3.23 μm in a Littrow cavity configuration at 20°C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

On-chip optical tweezers based on freeform optics

Since its advent in the 1970s, optical tweezers have been widely deployed as a preferred non-contact technique for manipulating microscale objects. On-chip integrated optical tweezers, which afford significant size, weight, and cost benefits, have been implemented, relying upon near-field evanescent waves. As a result, these tweezers are only capable of manipulation in near-surface regions and often demand high power since the evanescent interactions are relatively weak. We introduce on-chip optical tweezers based on freeform micro-optics, which comprise optical reflectors or refractive lenses integrated on waveguide end facets via two-photon polymerization. The freeform optical design offers unprecedented degrees of freedom to design optical fields with strong three-dimensional intensity gradients, useful for trapping and manipulating suspended particles in an integrated chip-scale platform. We demonstrate the design, fabrication, and measurement of both reflective and refractive micro-optical tweezers. The reflective tweezers feature a remarkably low trapping threshold power, and the refractive tweezers are particularly useful for multiparticle trapping and interparticle interaction analysis. Our integrated micro-optical tweezers uniquely combine a compact footprint, broadband operation, high trapping efficiency, and scalable integration with planar photonic circuits. This class of tweezers is promising for on-chip sensing, cell assembly, particle dynamics analysis, and ion trapping.

Yu, Shaoliang (ORCID:000000026072425X)↗
DOE OSTI2020

X-ray verification of sol-gel resist shrinkage in substrate-conformal imprint lithography for a replicated blazed reflection grating

Surface-relief gratings fabricated through nanoimprint lithography (NIL) are prone to topographic distortion induced by resist shrinkage. Characterizing the impact of this effect on blazed diffraction efficiency is particularly important for applications in astrophysical spectroscopy at soft x-ray wavelengths ( λ ≈ 0.5 − 5 nm) that call for the mass-production of large-area grating replicas with sub-micron, sawtooth surface-relief profiles. A variant of NIL that lends itself well for this task is substrate-conformal imprint lithography (SCIL), which uses a flexible, composite stamp formed from a rigid master template to imprint nanoscale features in an inorganic resist that cures thermodynamically through a silica sol-gel process. While SCIL enables the production of several hundred imprints before stamp degradation and avoids many of the detriments associated with large-area imprinting in NIL, the sol-gel resist suffers shrinkage dependent on the post-imprint cure temperature. Through atomic force microscopy and diffraction-efficiency testing at beamline 6.3.2 of the Advanced Light Source, the impact of this effect on blaze response is constrained for a ∼160-nm-period grating replica cured at 90 ° C. Results demonstrate a ∼2 ° reduction in blaze angle relative to the master grating, which was fabricated by anisotropic wet etching in 〈311〉-oriented silicon to yield a facet angle close to 30 ° .

McCoy, Jake A. (ORCID:0000000216057517)↗
DOE OSTI2018

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae , the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Conclusively, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

A Simple Auxin Transcriptional Response System Regulates Multiple Morphogenetic Processes in the Liverwort Marchantia polymorpha

In land plants comparative genomics has revealed that members of basal lineages share a common set of transcription factors with the derived flowering plants, despite sharing few homologous structures. The plant hormone auxin has been implicated in many facets of development in both basal and derived lineages of land plants. We functionally characterized the auxin transcriptional response machinery in the liverwort Marchantia polymorpha, a member of the basal lineage of extant land plants. All components known from flowering plant systems are present in M. polymorpha, but they exist as single orthologs: a single MpTOPLESS (TPL) corepressor, a single MpTRANSPORT INHIBITOR RESPONSE 1 auxin receptor, single orthologs of each class of AUXIN RESPONSE FACTOR (ARF; MpARF1, MpARF2, MpARF3), and a single negative regulator AUXIN/INDOLE-3-ACETIC ACID (MpIAA). Phylogenetic analyses suggest this simple system is the ancestral condition for land plants. We experimentally demonstrate that these genes act in an auxin response pathway — chimeric fusions of the MpTPL corepressor with heterodimerization domains of MpARF1, MpARF2, or their negative regulator, MpIAA, generate auxin insensitive plants that lack the capacity to pattern and transition into mature stages of development. Our results indicate auxin mediated transcriptional regulation acts as a facilitator of branching, differentiation and growth, rather than acting to determine or specify tissues during the haploid stage of the M. polymorpha life cycle. We hypothesize that the ancestral role of auxin is to modulate a balance of differentiated and pluri- or totipotent cell states, whose fates are determined by interactions with combinations of unrelated transcription factors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Chickpea NCR13 disulfide cross-linking variants exhibit profound differences in antifungal activity and modes of action

Small cysteine-rich antifungal peptides with multi-site modes of action (MoA) have potential for development as biofungicides. In particular, legumes of the inverted repeat-lacking clade express a large family of nodule-specific cysteine-rich (NCR) peptides that orchestrate differentiation of nitrogen-fixing bacteria into bacteroids. These NCRs can form two or three intramolecular disulfide bonds and a subset of these peptides with high cationicity exhibits antifungal activity. However, the importance of intramolecular disulfide pairing and MoA against fungal pathogens for most of these plant peptides remains to be elucidated. Our study focused on a highly cationic chickpea NCR13, which has a net charge of +8 and contains six cysteines capable of forming three disulfide bonds. NCR13 expression in Pichia pastoris resulted in formation of two peptide folding variants, NCR13_PFV1 and NCR13_PFV2, that differed in the pairing of two out of three disulfide bonds despite having an identical amino acid sequence. The NMR structure of each PFV revealed a unique three-dimensional fold with the PFV1 structure being more compact but less dynamic. Surprisingly, PFV1 and PFV2 differed profoundly in the potency of antifungal activity against several fungal plant pathogens and their multi-faceted MoA. PFV1 showed significantly faster fungal cell-permeabilizing and cell entry capabilities as well as greater stability once inside the fungal cells. Additionally, PFV1 was more effective in binding fungal ribosomal RNA and inhibiting protein translation in vitro. Furthermore, when sprayed on pepper and tomato plants, PFV1 was more effective in reducing disease symptoms caused by Botrytis cinerea, causal agent of gray mold disease in fruits, vegetables, and flowers. In conclusion, our work highlights the significant impact of disulfide pairing on the antifungal activity and MoA of NCR13 and provides a structural framework for design of novel, potent antifungal peptides for agricultural use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A theoretical overview of isospin and EOS effects in heavy-ion reactions at intermediate energies

The isospin dependence of in-medium nuclear effective interactions is a fundamental issue in nuclear physics and has broad ramifications in astrophysics. Its uncertainties, especially the difference of neutron-proton interactions in the isosinglet and isotriplet channels, affect significantly the density and momentum dependence of the isovector single-nucleon potential and nucleon-nucleon short-range correlation in neutron-rich matter. Consequently, the neutron-proton effective mass splitting and the density dependence of nuclear symmetry energy are still rather uncertain. Heavy-ion reactions, especially those involving rare isotopes, are a useful tool for probing the isospin dependence of nuclear effective interactions through 1) the neutron-skin in coordinate and proton-skin in momentum of the initial state of colliding nuclei, 2) the density and momentum dependence of especially the isovector nuclear mean-field as well as 3) the isospin dependence of in-medium nucleon-nucleon cross sections. Observations of neutron stars especially since GW1710817 have also helped us significantly in understanding the isospin dependence of nuclear effective interactions. We summarize here a review talk on these issues given at the 2021 International Workshop on multi-facets of EOS and Clustering. For details we refer the readers to the original publications and references therein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nano-Oxide-Dispersed Ferritic Steel for Fusion Energy Systems

The role of oxide nanoparticles in cavity formation of a nano-oxide-dispersed ferritic steel subjected to (Fe + He) dual-ion and (Fe + He + H) triple-ion irradiations has been studied using transmission electron microscopy to elucidate the synergistic effects of helium and hydrogen on radiation tolerance of nano-oxide-dispersed ferritic steel for fusion energy systems. The effect of oxide nanoparticles on suppressing radiation-induced void swelling is clearly revealed from the observation of preferred trapping of helium bubbles at oxide nanoparticles, which results in a unimodal distribution of cavities in the (Fe + He) dual-ion irradiated specimen . An adverse effect of hydrogen implantation, however, is revealed from the observation of a bimodal distribution of cavities with large and facetted voids in association with the formation of HFe 5 O 8 -based hydroxide in local regions of the (Fe + He + H) triple-ion irradiated specimen.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Rapid Synthesis of Monodispersed TATB Microparticles in Ionic Liquid Micelles

Controlling microscopic morphology of energetic materials is of significant interest for the improvement of their performance and production consistency. As an important insensitive high explosive material, triaminotrinitrobenzene (TATB) has attracted tremendous research effort for military grade explosives and propellants. Here, in this study, a new, rapid and inexpensive synthesis method for monodispersed TATB microparticles based on micelle-confined precipitation was developed. Surfactant with proper hydrophilic-lipophilic balance value was found to be critical to the success of this synthesis. Lasltyl, the morphology of the TATB microparticles can be tuned between quasi-spherical and faceted by controlling the speed of recrystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Designing protein–material interfaces

This article addresses recent advances in using de novo protein design to create coherent interfaces between proteins and inorganic materials, either through protein self-assembly on crystal lattices or through directed nucleation and growth of crystals by protein scaffolds. Inspired by natural protein-crystal interfaces, we focus on a class of designed helical repeat proteins that present a repeating pattern of charged amino acid residues. We describe the use of in situ imaging and spectroscopic methods to investigate both the assembly of these proteins and their ability to direct crystal nucleation and growth. Furthermore, the findings reveal the importance of surface charge, facet-specific binding, solvent organization, and, more generally, the balance of protein-substrate-solvent interactions in determining how organized protein-materials interfaces emerge. Moreover, the results demonstrate the vast potential of protein design in materials science and elucidate the mechanisms by which interactions between biomolecules and inorganic surfaces lead to unique materials and morphologies.

Biomaterials-Proteins↗
DOE OSTI2025

Advances in additive manufacturing, materials, and applications with AI/ML

There is high interest in making digital manufacturing a central facet of the new manufacturing landscape. However, in the materials science world, there is much work and opportunity to realize the full potential of artificial intelligence/machine learning (AI/ML) with regard to the structure–composition–processing–property (SCPP) relationship. For polymers (thermoplastics, thermosets, elastomers) and composites (nanocomposites), the origin of their high performance and even recyclability starts with design and formulation. Processing methods enable more property development based on curing, shape-factor forming, and anisotropic directionality. In subtractive manufacturing, high-performance and engineering polymers can be shaped and milled to very high tolerance and specifications and used as replacements for metals and alloys. In conclusion, this typically relies on digital manufacturing methods but tends to be wasteful in materials.

Lara-Ceniceros, Tania E. [Centro de Investigación ↗
DOE OSTI2016

Simulating Fragmentation and Fluid-Induced Fracture in Disordered Media Using Random Finite-Element Meshes

Fracture and fragmentation are extremely nonlinear multiscale processes in which microscale damage mechanisms emerge at the macroscale as new fracture surfaces. Numerous numerical methods have been developed for simulating fracture initiation, propagation, and coalescence. In this paper, we present a computational approach for modeling pervasive fracture in quasi-brittle materials based on random close-packed Voronoi tessellations. Each Voronoi cell is formulated as a polyhedral finite element containing an arbitrary number of vertices and faces. Fracture surfaces are allowed to nucleate only at the intercell faces. Cohesive softening tractions are applied to new fracture surfaces in order to model the energy dissipated during fracture growth. The randomly seeded Voronoi cells provide a regularized discrete random network for representing fracture surfaces. The potential crack paths within the random network are viewed as instances of realizable crack paths within the continuum material. Mesh convergence of fracture simulations is viewed in a weak, or distributional, sense. The explicit facet representation of fractures within this approach is advantageous for modeling contact on new fracture surfaces and fluid flow within the evolving fracture network. Finally, applications of interest include fracture and fragmentation in quasi-brittle materials and geomechanical applications such as hydraulic fracturing, engineered geothermal systems, compressed-air energy storage, and carbon sequestration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DEM simulation of the compression of crushable sand: does the initial particle shape matter?

Advances in DEM modeling, combined with high-resolution X-ray tomography, opened the way for computer models based on virtual replicas of the particles which preserve nearly all facets of their geometry. This leads to simulation advantages, but also high computational costs. Here we tackle a question stemming from this trend: how accurate should particle models be to ensure accuracy? We address this question for the case of the compression of crushable sand. LS-DEM was used to generate three models of Ottawa sand (exact replicas, ellipsoids, and spheres) from digital images of its grains. Compression-induced crushing was simulated for all sets by tracking evolving size and shape distribution. The results confirm that exact replicas provide the closest match of the measurements. However, intermediate degrees of rendering (e.g. ellipsoids preserving volume and aspect ratio of the real grains) led to satisfactory results only marginally different from those of exact replicas. In conclusion, these findings provide an example of the protocols that may be followed to identify the optimal degree of particle approximation which should be regarded as mandatory to achieve a conscious, sustainable use of computational resources.

58 GEOSCIENCES↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2016

Medulloblastoma-associated DDX3 variant selectively alters the translational response to stress

DDX3X encodes a DEAD-box family RNA helicase (DDX3) commonly mutated in medulloblastoma, a highly aggressive cerebellar tumor affecting both children and adults. Despite being implicated in several facets of RNA metabolism, the nature and scope of DDX3's interactions with RNA remain unclear. Here, we show DDX3 collaborates extensively with the translation initiation machinery through direct binding to 5'UTRs of nearly all coding RNAs, specific sites on the 18S rRNA, and multiple components of the translation initiation complex. Impairment of translation initiation is also evident in primary medulloblastomas harboring mutations in DDX3X, further highlighting DDX3's role in this process. Arsenite-induced stress shifts DDX3 binding from the 5'UTR into the coding region of mRNAs concomitant with a general reduction of translation, and both the shift of DDX3 on mRNA and decreased translation are blunted by expression of a catalytically-impaired, medulloblastoma-associated DDX3R534H variant. Furthermore, despite the global repression of translation induced by arsenite, translation is preserved on select genes involved in chromatin organization in DDX3R534H-expressing cells. Thus, DDX3 interacts extensively with RNA and ribosomal machinery to help remodel the translation landscape in response to stress, while cancer-related DDX3 variants adapt this response to selectively preserve translation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

rmap: An R package to plot and compare tabular data on customizable maps across scenarios and time

`rmap` is an R package that allows users to easily plot tabular data (CSV or R data frames) on maps without any Geographic Information Systems (GIS) knowledge. Maps produced by `rmap` are `ggplot` objects and thus capitalize on the flexibility and advancements of the `ggplot2` package and all elements of each map are thus fully customizable. Additionally `rmap` automatically detects and produces comparison maps if the data has multiple scenarios or time periods as well as animations for time series data. Advanced users can load their own shapefiles if desired. `rmap` comes with a range of pre-built color palettes but users can also provide any `R` color palette or create their own as needed. Four different legend types are available to highlight different kinds of data distributions. The input spatial data can be both gridded or polygon data. `rmap` is desgined in particular for comparing spatial data across scenarios and time periods and comes preloaded with standard country, state, and basin maps as well as custom maps compatible with the Global Change Analysis Model (GCAM) spatial boundaries. `rmap` has a growing number of users and its products have been used in multiple multisector dynamics publications as well as a required dependency in other R packages such as `rfasst` and `metis`. `rmap's` automatic processing of tabular data using pre-built map selection, difference map calculations, faceting, and animations offers unique functionality which makes it a powerful and yet simple tool for users looking to explore multi-sector, multi-scenario data across space and time.

58 GEOSCIENCES↗
DOE OSTI2022

interflow: A Python package to organize, calculate, and visualize sectoral interdependency flow data

Many economic sectors rely on an uninterrupted “upstream” supply of a resource to conduct their primary functions, leaving them vulnerable to adverse effects should that resource flow be interrupted or compromised (OECD, 2017; U.S. EPA, 2010). Well-known examples of these relationships include water demand by the energy sector (e.g., thermoelectric cooling for nuclear generation) (Grubert & Sanders, 2018; Webber, 2017) and energy demand by the water sector (e.g., electricity required to treat or move water in the public water sector) (Congressional Research Service, 2017) though many others exist. Being able to calculate and document these interdependencies and evaluate where the greatest cross-sectoral intensities and flows exist can reveal opportunities to enhance the overall network. Despite the implications and potential impacts, however, these interconnections and flows have been historically complex to analyze and understand. The interflow package provides a flexible tool to organize, calculate, and visualize (using Sankey diagrams and other visualizations) sectoral interdependency flows for multiple subsectors and resources. This tool can help decision-makers, researchers, and other audiences more easily pull meaning from these interdependencies to reveal multi-faceted opportunities and risks. interflow can help investigate questions such as (1) which sectors have high cross-resource dependencies, (2) how does demand for a resource in various sectors compare across regions, and (3) where the sectoral and regional opportunities are for enhanced efficiency, security, and resiliency.

97 MATHEMATICS AND COMPUTING↗