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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding Proton Movement in [Fe-Fe] Hydrogenases

Nature uses specialized metalloenzymes to carry out small molecule activation reactions, including CO 2 fixation, O 2 activation, and proton reduction, with unparalleled efficiency, rates, and selectivity. The latter reactions are performed by hydrogenases, protein metallo-complexes that interconverts H 2 to protons and electrons (H 2 oxidation) and the reverse reaction (H 2 production) with incredibly low energy input and amazingly fast kinetics. Reproducing both the activity and efficiency of metalloenzymes in sustainable anthropogenic systems remains one of the “holy grails” of inorganic chemistry. However, identifying the precise molecular components responsible for these desirable properties has been challenging in the natural metalloenzymes, hindering efforts to develop analogous processes in synthetic compounds. Considering the inherent complexity of a metalloenzyme and the many interactions, both strong and weak, that contribute to the function of an enzyme, we have elected to model natural metalloenzymes on a biochemical platform. Towards this end, we have a developed structural, functional, and mechanistic mimic of the [Ni-Fe] hydrogenases within a robust protein scaffold, rubredoxin, to understand the influence of the secondary coordination environment on the metal center. This involved preparing a series of three rubredoxin constructs containing a single point mutation at Val positions and making the NMR chemical shift assignments for the paramagnetic (nickel-substituted) and non-paramagnetic (zinc-substituted) form. These physical studies were complemented with in silico molecular dynamic studies on proteins with [Fe-S] clusters to engineer new proton channels to test in vitro . To assist our search for new natural metalloprotein scaffolds within the vast number of sequenced genomes, we created a neural network-based program to identify proteins with specific metal-binding sites. These computational and physical studies with metalloenzymes provide direct insight into the fundamental chemical principles driving the natural systems and offer design principles for developing catalysts that utilize analogous principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Regulation of sarcomere formation and function in the healthy heart requires a titin intronic enhancer

Heterozygous truncating variants in the sarcomere protein titin (TTN) are the most common genetic cause of heart failure. To understand mechanisms that regulate abundant cardiomyocyte (CM) TTN expression, we characterized highly conserved intron 1 sequences that exhibited dynamic changes in chromatin accessibility during differentiation of human CMs from induced pluripotent stem cells (hiPSC-CMs). Homozygous deletion of these sequences in mice caused embryonic lethality, whereas heterozygous mice showed an allele-specific reduction in Ttn expression. A 296 bp fragment of this element, denoted E1, was sufficient to drive expression of a reporter gene in hiPSC-CMs. Deletion of E1 downregulated TTN expression, impaired sarcomerogenesis, and decreased contractility in hiPSC-CMs. Site-directed mutagenesis of predicted binding sites of NK2 homeobox 5 (NKX2-5) and myocyte enhancer factor 2 (MEF2) within E1 abolished its transcriptional activity. In embryonic mice expressing E1 reporter gene constructs, we validated in vivo cardiac-specific activity of E1 and the requirement for NKX2-5- and MEF2-binding sequences. Moreover, isogenic hiPSC-CMs containing a rare E1 variant in the predicted MEF2-binding motif that was identified in a patient with unexplained dilated cardiomyopathy (DCM) showed reduced TTN expression. Together, these discoveries define an essential, functional enhancer that regulates TTN expression. Manipulation of this element may advance therapeutic strategies to treat DCM caused by TTN haploinsufficiency.

Kim, Yuri↗
DOE OSTI2024

Specialized protist communities on mycorrhizal fungal hyphae

Abstract Arbuscular mycorrhizal (AM) fungi not only play a crucial role in acquiring nutrients for plants but also serve as a habitat for soil microbes. Recent studies observed that AM fungal hyphae are colonized by specific bacterial communities. However, so far it has not been explored whether fungal hyphae and mycorrhizal networks also harbor specific communities of protists, a key group of microbes in the soil microbiome. Here, we characterized protist communities in soil in a compartment with plant roots and on hyphae collected from hyphal compartments without plant roots. We detected specific protist communities on fungal hyphae. Fourteen protistan amplicon sequences variants (ASVs) were significantly associated with fungal hyphae, half of which belonged to the Cercozoa group. This research, for the first-time detected specific protist ASVs directly associated with abundant AM fungus hyphae, highlighting the complexity of the hyphal food web.

Mycology↗
DOE OSTI2025

Persistent short-range spin fluctuation in rare-earth substituted square lattice Sr 2 ⁢Ir 0.89 ⁢Tb 0.11⁢ O 4

Sr 2 ⁢IrO 4 , a prototypical 𝐽 eff = 1/2 square lattice system, is widely studied for novel physics. While transition metal substitution effects are well explored, rare-earth doping at the Ir 4+ site is less explored. Here we present a detailed magnetization and neutron diffraction study on Sr 2 ⁢Ir 1−𝑥 ⁢Tb 𝑥 ⁢O 4 (𝑥 = 0.11). The system tends to adopt an in-plane antiferromagnetic configuration with a “++++” stacking sequence. Strong spin fluctuations persist down to the lowest measured temperature, consistent with the concave shape order parameter. A pronounced magnetic diffuse scattering rod develops along the 𝐿 direction at low temperature, indicating the formation of short-range magnetic correlations with a characteristic length of ∼ 24⁢(2) ⁢Å. In conclusion, this work provides a new pathway to exotic quantum phases in the strongly spin-orbit-coupled iridates.

Iridates↗
DOE OSTI2024

Microbial DNA Database and Demonstration

2024 Annual Review meeting talk, "Microbial DNA Database and Demonstration." Description of the NETL's PW DNA Database that will be 1) curated to make it more useable, 20 include metadata linked to each sequencing set, such as qPCR and geochemistry, and 30 allow better filtering and visualization. The database is hosted on KBase so terabytes worth of data can be used directly on the program without having to download it to personal computers.

Schweitzer, Hannah↗
DOE OSTI2023

Comparative feedstock analysis in Setaria viridis L. as a model for C 4 bioenergy grasses and panicoid crop species

Second generation feedstocks for bioethanol will likely include a sizable proportion of perennial C4 grasses, principally in the Panicoideae clade. The Panicoideae contain agronomically important annual grasses including Zea mays L. (maize), Sorghum bicolor (L.) Moench (sorghum), and Saccharum officinarum L. (sugar cane) as well as promising second generation perennial feedstocks including Miscanthus × giganteus and Panicum virgatum L. (switchgrass). The underlying complexity of these polyploid grass genomes is a major limitation for their direct manipulation and thus driving a need for rapidly cycling comparative model. Setaria viridis (green millet) is a rapid cycling C4 panicoid grass with a relatively small and sequenced diploid genome and abundant seed production. Stable, transient, and protoplast transformation technologies have also been developed for Setaria viridis making it a potentially excellent model for other C4 bioenergy grasses. Here, the lignocellulosic feedstock composition, cellulose biosynthesis inhibitor response and saccharification dynamics of Setaria viridis are compared with the annual sorghum and maize and the perennial switchgrass bioenergy crops as a baseline study into the applicability for translational research. A genome-wide systematic investigation of the cellulose synthase-A genes was performed identifying eight candidate sequences. Two developmental stages; (a) metabolically active young tissue and (b) metabolically plateaued (mature) material are examined to compare biomass performance metrics.

09 BIOMASS FUELS↗
DOE OSTI2020

Elucidating the Chemical Pathways Responsible for the Sooting Tendency of 1 and 2-phenylethanol

Yield Sooting Index (YSI) measurements have shown that oxygenated aromatic compounds (OACs) tend to have lower YSI than aromatic hydrocarbon (AHC) compounds. For example, typical AHCs such as toluene and ethyl benzene have a YSI of 170 and 216, respectively, in contrast, OACs such as phenol and anisole have a YSI of 81 and 111, respectively. However, this trend is not always true as was observed for the structural isomers 1-phenylethanol (1PE, YSI=142) and 2-phenylethanol (2PE, YSI=207), where 2PE contains a YSI more representative of AHCs than OACs. We applied flow reactor experiments and density functional theory (DFT) calculations to examine how oxygen functionality present in 1PE and 2PE alters the reaction pathways leading to the observed difference in soot formation. It was determined that the proximity of the oxygen functional group to the aromatic ring determines whether the oxygen remains attached to the primary reacting species (for 1PE) or is eliminated early in the combustion sequence (for 2PE). For these alcohols, preservation of the oxygen in the molecule leads to further OACs, while loss of the oxygen leads to AHCs and benzyl radical. The direct pathways to AHCs and benzyl radical result in the higher YSI observed for 2PE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automated Signal Timing Plan Reconstruction Using High-Resolution Event-Based Controller Data for Digital Twins

Transportation digital twins are essential tools for evaluating emerging technologies such as connected and automated vehicles, adaptive traffic signal control, and mobility optimization strategies. Realistic digital twins require accurate emulation of real-world signal controllers and detailed signal timing plans. However, signal timing plans are often unavailable or difficult to access, forcing researchers and modelers to rely on assumed fixed timings or halt their analysis. To overcome this challenge, we present a method that directly estimates signal timing plan parameters using high-resolution, event-based data from traffic signal controllers. The proposed method extracts key parameters, including cycle length, offset, phase sequence, coordinated phases, phase-specific minimum and maximum green durations, vehicle extensions, and splits under coordination. A rule-based deterministic signal timing reconstruction algorithm based on traffic signal operation rules, such as those outlined in the Signal Timing Manual, is developed and validated. We evaluate this method, which uses high-resolution controller event logs and verified signal timing plans, on 94 signalized intersections in Nashville, Tennessee, demonstrating their ability to generate accurate, simulation-ready signal timing plans for tools such as SUMO and Vissim.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2024

Updates to the Alliance of Genome Resources central infrastructure

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific “landing pages” and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Simulated structure and thermodynamics of decagonal Al-Co-Cu quasicrystals

Atomic structures of Al-Co-Cu decagonal quasicrystals (dQCs) are investigated using empirical oscillating pair potentials (EOPP) in molecular dynamic (MD) simulations that we enhance by Monte Carlo (MC) swapping of chemical species and replica exchange. Predicted structures exhibit planar decagonal tiling patterns and are periodic along the perpendicular direction. We then recalculate the energies of promising structures using first-principles density functional theory (DFT), along with energies of competing phases. We find that our τ -inflated sequence of QC approximants (QCAs) are energetically unstable at low temperature by at least 3 meV/atom. Extending our study to finite temperatures by calculating harmonic vibrational entropy, as well as anharmonic contributions that include chemical species swaps and tile flips, our results suggest that the quasicrystal phase is entropically stabilized at temperatures in the range 600-800 K and above. It decomposes into ordinary (though complex) crystal phases at low temperatures, including a partially disordered B2-type phase. We discuss the influence of density and composition on QC phase stability; we compare the structural differences between Co-rich and Cu-rich quasicrystals; and we analyze the role of entropy in stabilizing the quasicrystal, concluding with a discussion of the possible existence of “high entropy” quasicrystals. Published by the American Physical Society 2024

Huang, Yang (ORCID:0009000045917347)↗
DOE OSTI2021

Anomalous ultrafast all-optical Hall effect in gapped graphene

We propose an ultrafast all-optical anomalous Hall effect in two-dimensional (2D) semiconductors of hexagonal symmetry such as gapped graphene (GG), transition metal dichalcogenides (TMDCs), and hexagonal boron nitride (h-BN). To induce such an effect, the material is subjected to a sequence of two strong-field single-optical-cycle pulses: A chiral pump pulse followed within a few femtoseconds by a probe pulse linearly polarized in the armchair direction of the 2D lattice. Due to the effect of topological resonance, the first (pump) pulse induces a large chirality (valley polarization) in the system, while the second pulse generates a femtosecond pulse of the anomalous Hall current. The proposed effect is fundamentally the fastest all-optical anomalous Hall effect possible in nature. It can be applied to ultrafast all-optical storage and processing of information, both classical and quantum.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗
DOE OSTI2023

Structural Similarities and Overlapping Activities among Dihydroflavonol 4-Reductase, Flavanone 4-Reductase, and Anthocyanidin Reductase Offer Metabolic Flexibility in the Flavonoid Pathway

Flavonoids are potent antioxidants that play a role in defense against pathogens, UV-radiation, and the detoxification of reactive oxygen species. Dihydroflavonol 4-reductase (DFR) and flavanone 4-reductase (FNR) reduce dihydroflavonols and flavanones, respectively, using NAD(P)H to produce flavan-(3)-4-(di)ols in flavonoid biosynthesis. Anthocyanidin reductase (ANR) reduces anthocyanidins to flavan-3-ols. In addition to their sequences, the 3D structures of recombinant DFR, FNR and ANR from sorghum and switchgrass showed a high level of similarity. The catalytic mechanism, substrate-specificity and key residues of three reductases were deduced from crystal structures, site-directed mutagenesis, molecular docking, kinetics, and thermodynamic ana-lyses. Although DFR displayed its highest activity against dihydroflavonols, it also showed activity against flavanones and anthocyanidins. It was inhibited by the flavonol quercetin and high concentrations of dihydroflavonols/flavonones. SbFNR1 and SbFNR2 did not show any activity against dihydroflavonols. However, SbFNR1 displayed activity against flavanones and ANR activity against two anthocyanidins, cyanidin and pelargonidin. Therefore, SbFNR1 and SbFNR2 could be specific ANR isozymes without delphinidin activity. Sorghum has high concentrations of 3-deoxyanthocyanidins in vivo, supporting the observed high activity of SbDFR against flavonols. Mining of expression data indicated substantial induction of these three reductase genes in both switchgrass and sorghum in response to biotic stress. Key signature sequences for proper DFR/ANR classification are proposed and could form the basis for future metabolic engineering of flavonoid metabolism.

3-deoxyanthocyanidin↗
DOE OSTI2021

Cross-reactive immunogenicity of group A streptococcal vaccines designed using a recurrent neural network to identify conserved M protein linear epitopes

The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal sequence of the protein defines the more than 200 M types of Strep A and also contains epitopes that elicit opsonic antibodies, some of which cross-react with heterologous M types. Current efforts to develop broadly protective M protein-based vaccines are directed at identifying potential cross-protective epitopes located in the N-terminal regions of cluster-related M proteins for use as vaccine antigens. In this study, we have used a comprehensive approach using the recurrent neural network ABCpred and IEDB epitope conservancy analysis tools to predict 16 residue linear B-cell epitopes from 117 clinically relevant M types of Strep A (~88% of global Strep A infections). Furthermore, to examine the immunogenicity of these epitope-based vaccines, nine peptides that together shared ≥60% sequence identity with 37 heterologous M proteins were incorporated into two recombinant hybrid protein vaccines, in which the epitopes were repeated 2 or 3 times, respectively. The combined immune responses of immunized rabbits showed that the vaccines elicited significant levels of antibodies against all nine vaccine epitopes present in homologous N-terminal 1–50 amino acid synthetic M peptides, as well as cross-reactive antibodies against 16 of 37 heterologous M peptides predicted to contain similar epitopes. The epitope-specificity of the cross-reactive antibodies was confirmed by ELISA inhibition assays and functional opsonic activity was assayed in HL-60-based bactericidal assays. The results provide important information for the future design of broadly protective M protein-based Strep A vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Microbial spies and bloggers: programming cells to convert environmental information into discernible signals

Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.

analyte↗
DOE OSTI2025

Utilizing Machine Learning to Improve Neutralization Potency of an HIV-1 Antibody Targeting the gp41 N-Heptad Repeat

The N-heptad repeat (NHR) of the HIV-1 gp41 prehairpin intermediate (PHI) is an attractive potential vaccine target with high sequence conservation across diverse strains. However, despite the potency of NHR-targeting peptides and clinical efficacy of the NHR-targeting entry inhibitor enfuvirtide, no potently neutralizing NHR-directed monoclonal antibodies (mAbs) nor antisera have been identified or elicited to date. The lack of potent NHR-binding mAbs both dampens enthusiasm for vaccine development efforts at this target and presents a barrier to performing passive immunization experiments with NHR-targeting antibodies. To address this challenge, we previously developed an improved variant of the NHR-directed mAb D5, called D5_AR, which is capable of neutralizing diverse tier-2 viruses. Building on that work, here we present the 2.7Å-crystal structure of D5_AR bound to NHR mimetic peptide IQN17. We then utilize protein language models and supervised machine learning to generate small (n < 100) libraries of D5_AR variants that are subsequently screened for improved neutralization potency. We identify a variant with 5-fold improved neutralization potency, D5_FI, which is the most potent NHR-directed monoclonal antibody characterized to date and exhibits broad neutralization of tier-2 and −3 pseudoviruses as well as replicating R5 and X4 challenge strains. Additionally, our work highlights the ability of protein language models to efficiently identify improved mAb variants from relatively small libraries.

Biopolymers↗
DOE OSTI2022

A 20 Gbps PAM4 data transmitter ASIC for particle physics experiments

We present the design and test results of a novel data transmitter ASIC operating up to 20.48 Gbps with 4-level Pulse-Amplitude-Modulation (PAM4) for particle physics experiments. This ASIC, named GBS20, is fabricated in a 65 nm CMOS technology. Two serializers share a 5.12 GHz Phase Locked Loop (PLL) clock. The outputs from the serializers are combined into a PAM4 signal that directly drives a Vertical-Cavity-Surface-Emitting-Laser (VCSEL). The input data channels, each at 1.28 Gbps, are scrambled with an internal 2$^{7}$-1 Pseudo-Random Binary Sequence (PRBS), which also serves as a frame aligner. GBS20 is tested to work at 10.24 and 20.48 Gbps with a VCSEL-based Transmitter-Optical-Subassembly (TOSA). The power consumption of GBS20 is below 238 mW and reduced to 164 mW in the low-power mode.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗