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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Randomly Layered Superstructure of In 2 O 3 Truncated Nano-Octahedra and Its High-Pressure Behavior

This study outlines the synthesis and characterization of a unique superlattice composed of vertex-truncated indium oxide (In 2 O 3 ) nano-octahedra, along with an exploration of its response to high-pressure conditions. Here, using a bright-field transmission electron microscope (BF-TEM), we determined an average circumradius of 15.2 nm for these octahedral building blocks. The resilience and response of the superlattice to pressure variations, peaking at 18.01 GPa, were examined by employing synchrotron-based Wide-Angle X-ray Scattering (WAXS) and Small-Angle X-ray Scattering (SAXS) techniques. The WAXS data revealed no phase transitions, reinforcing the stability of the 2D superlattice comprised of random layers in alignment with a 2D p31m symmetry. Notably, the SAXS data unveiled a pressure-induced, irreversible octahedron translation and ligand interaction occurring within the random layer. Through our examination of these pressure-sensitive behaviors, we identified a distinctive translation model inherent to octahedra and observed modulation in the superlattice cell parameter induced by pressure. This research signifies a noteworthy progression in deciphering the intricate behaviors of 2D superlattices under high-pressure conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Comparisons Between Array Derived Dynamic Strain Rate (ADDS) and Fiber‐Optic Distributed Acoustic Sensing (DAS) Strain Rate

Abstract Distributed acoustic sensing (DAS) strain rate and particle velocity can be compared through approximate scaling with medium velocity. We instead performed a direct comparison between array derived dynamic strain (ADDS) rate and DAS strain rate for six frequency bands. The PoroTomo project at Brady's Hot Springs, Nevada, deployed a 240‐geophone 3C array co‐located with fiber‐optic DAS system and 8.7 km of buried cable. We selected subsets of the geophone array to create four smaller arrays and computed ADDS. The horizontal components of the ADDS were rotated into the direction of the fiber‐optic cable and then compared with the observed DAS strain rates. From three example regional earthquakes of local magnitudes 2.9, 4.1, and 4.3, the ADDS are found to be coherent with DAS for frequencies ≤1 Hz. For frequencies >1‐Hz, this correlation decays quickly. Small differences between linear and areal dynamic strains at 1‐Hz suggest poor signal‐to‐noise or localized strain that is perturbed by shallow heterogeneities compare to the average strain propagating across the geophone array. The implication is that around 1‐Hz, straight fiber DAS is measuring axial strain along the fiber and can provide good approximations to translational particle motions. However, above 1‐Hz, DAS becomes more sensitive to shallow velocity gradients that can be beneficial for geophysical imaging yet becomes a limitation for traditional seismic analysis methods depending on absolute amplitude and phase from translational particle motions.

58 GEOSCIENCES↗
DOE OSTI2021

Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane

Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocarbons. From the active fraction, high-quality metagenome-assembled genomes (MAGs) were recovered for the acetoclastic methanogen, Methanothrix paradoxum, and a novel member of the Chlorobi with the potential to generate acetate via the Pta-Ack pathway. Members of the Bacteroides and Geobacter also encoded Pta-Ack and together, all four populations had the putative ability to degrade ethylbenzene, phenylphosphate, phenylethanol, toluene, xylene, and phenol. Metabolic reconstructions, gene analyses, and environmental parameters also indicated that redox fluctuations likely promote facultative energy metabolisms in the coal seam. The active "Chlorobi PRB" MAG encoded enzymes for fermentation, nitrate reduction, and multiple oxygenases with varying binding affinities for oxygen. "M. paradoxum PRB" encoded an extradiol dioxygenase for aerobic phenylacetate degradation, which was also present in previously published Methanothrix genomes. Finaly, these observations outline underlying processes for bio-methane from subbituminous coal by translationally active populations and demonstrate activity-based metagenomics as a powerful strategy in next generation physiology to understand ecologically relevant microbial populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An integrated multi-omics approach identifies the landscape of interferon-α-mediated responses of human pancreatic beta cells

Proinflammatory cytokines are important mediators of pancreatic beta cell dysfunction and demise in the early stages of type 1 diabetes (T1D). Interferon-a (IFNa), a type I interferon member, is expressed in the islets of T1D individuals and it is expression and signaling is regulated by both genetic (T1D risk variants) and environmental factors (viral infections) associated to T1D. We presently characterized human beta cells responses to IFNa by combining ATAC-seq, RNA-seq and proteomics assays. The initial beta cell response to IFNa was characterized by major chromatin remodeling, followed by marked changes in transcriptional and translational regulation. IFNa-induced changes in alternative splicing (AS) and first exon usage increased the diversity of transcripts expressed by beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may significantly expand the peptide repertoire presented by beta cells to the immune system. On the other hand, beta cells up-regulated checkpoint proteins, such as PDL1 and HLA-E, that may protect them against the autoimmune assault. Data mining of the present multi-omics analysis led to the identification of two compound classes that revert IFNa effects on human beta cells and may be translated to clinical trials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation

The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning

Abstract mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.

Cetnar, Daniel P.↗
DOE OSTI2025

Human protein synthesis requires aminoacyl-tRNA pivoting during proofreading

Abstract Rigorous studies have characterized the aa-tRNA selection mechanism in bacteria, which is essential for maintaining translational fidelity. Recent investigations have identified critical distinctions in humans, such as the requirement of subunit rolling and a tenfold slower proofreading step. Although these studies captured key intermediates involved in tRNA selection, they did not elucidate the transitions of aa-tRNA between intermediates. Through diverse structure-based simulations, we simulated 1856 aa-tRNA accommodation events into the human ribosomal A site. Here we show the requirement for a distinct ~30° pivoting of aa-tRNA about the anticodon stem within the accommodation corridor. This pivoting is crucial for navigating the crowded accommodation corridor, which becomes more constrained due to subunit rolling. Subunit rolling-dependent crowding increases the steric contributions of the accommodation corridor during aa-tRNA accommodation, consistent with the tenfold reduction in the rate of proofreading. Furthermore, we show that eEF1A interacts with the accommodating aa-tRNA through conserved basic residues, limiting premature aa-tRNA dissociation from the A site. These findings provide a structural description of the human aa-tRNA selection process and demonstrate that the aa-tRNA alignment relative to the ribosomal catalytic sites is a critical determinant of translational fidelity.

Science & Technology - Other Topics↗
DOE OSTI2021

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Chemistry↗
DOE OSTI2025

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

Abstract The genome-scale model of metabolism and gene expression (ME-model) forPseudomonas putidaKT2440,iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model,iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization inP. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus,iPpu1676-ME offers valuable insights intoP. putida’s proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

Mathematical & Computational Biology↗
DOE OSTI2021

Direct observation of coherent femtosecond solvent reorganization coupled to intramolecular electron transfer

It is well known that the solvent plays a critical role in ultrafast electron-transfer reactions. However, solvent reorganization occurs on multiple length scales, and selectively measuring short-range solute–solvent interactions at the atomic level with femtosecond time resolution remains a challenge. Here we report femtosecond X-ray scattering and emission measurements following photoinduced charge-transfer excitation in a mixed-valence bimetallic (Fe ii Ru iii ) complex in water, and their interpretation using non-equilibrium molecular dynamics simulations. Combined experimental and computational analysis reveals that the charge-transfer excited state has a lifetime of 62 fs and that coherent translational motions of the first solvation shell are coupled to the back electron transfer. Overall, our molecular dynamics simulations identify that the observed coherent translational motions arise from hydrogen bonding changes between the solute and nearby water molecules upon photoexcitation, and have an amplitude of tenths of ångströms, 120–200 cm -1 frequency and ~100 fs relaxation time. This study provides an atomistic view of coherent solvent reorganization mediating ultrafast intramolecular electron transfer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Solution-phase sample-averaged single-particle spectroscopy of quantum emitters with femtosecond resolution

Here, the development of many optical quantum technologies depends on the availability of solid-state single quantum emitters with near-perfect optical coherence. However, a standing issue that limits systematic improvement is the significant sample heterogeneity and lack of mechanistic understanding of microscopic energy flow at the single emitter level and ultrafast timescales. Here we develop solution-phase single-particle pump-probe spectroscopy with photon correlation detection that captures sample-averaged dynamics in single molecules and/or defect states with unprecedented clarity at femtosecond resolution. We apply this technique to single quantum emitters in two-dimensional hexagonal boron nitride, which suffers from significant heterogeneity and low quantum efficiency. From millisecond to nanosecond timescales, the translation diffusion, metastable-state-related bunching shoulders, rotational dynamics, and antibunching features are disentangled by their distinct photon-correlation timescales, which collectively quantify the normalized two-photon emission quantum yield. Leveraging its femtosecond resolution, spectral selectivity and ultralow noise (two orders of magnitude improvement over solid-state methods), we visualize electron-phonon coupling in the time domain at the single defect level, and discover the acceleration of polaronic formation driven by multi-electron excitation. Corroborated with results from a theoretical polaron model, we show how this translates to sample-averaged photon fidelity characterization of cascaded emission efficiency and optical decoherence time. Our work provides a framework for ultrafast spectroscopy in single emitters, molecules, or defects prone to photoluminescence intermittency and heterogeneity, opening new avenues of extreme-scale characterization and synthetic improvements for quantum information applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ferroaxial density wave from intertwined charge and orbital order in rare-earth tritellurides

The discovery of the axial amplitude mode—commonly referred to as the Higgs mode—in charge density wave systems, such as rare-earth tritellurides, indicates the presence of a hidden order. A theoretical study proposed that this axial Higgs mode arises from a hidden orbital texture of the charge density wave, which produces a ferroaxial charge order. However, experimental evidence for the specific hidden order has been lacking. Here, we demonstrate a ferroaxial order of electronic origin throughout the rare-earth tritellurides. In ErTe 3 and HoTe 3 , which exhibit two distinct charge density waves with different ordering temperatures, a detailed investigation shows that the high-temperature charge order phase breaks translational, rotational and all vertical as well as diagonal mirror symmetries. Furthermore, this phase produces an axial Higgs mode and an axial electronic gap. By contrast, the low-temperature phase breaks only translational symmetry and gives rise to a scalar Higgs mode. Notably, both phases preserve the space inversion and time-reversal symmetries. These findings are consistent with a ferroaxial phase driven by coupled orbital and charge orders, highlighting the role of Higgs modes in revealing hidden orders in systems with intertwined charge density waves.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Population-based heteropolymer design to mimic protein mixtures

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A swapped genetic code prevents viral infections and gene transfer

Engineering the genetic code of an organism has been proposed to provide a firewall from natural ecosystems by preventing viral infections and gene transfer. However, numerous viruses and mobile genetic elements encode parts of the translational apparatus, potentially rendering a genetic-code-based firewall ineffective. Here we show that such mobile transfer RNAs (tRNAs) enable gene transfer and allow viral replication in Escherichia coli despite the genome-wide removal of 3 of the 64 codons and the previously essential cognate tRNA and release factor genes. We then establish a genetic firewall by discovering viral tRNAs that provide exceptionally efficient codon reassignment allowing us to develop cells bearing an amino acid-swapped genetic code that reassigns two of the six serine codons to leucine during translation. This amino acid-swapped genetic code renders cells resistant to viral infections by mistranslating viral proteomes and prevents the escape of synthetic genetic information by engineered reliance on serine codons to produce leucine-requiring proteins. As these cells may have a selective advantage over wild organisms due to virus resistance, we also repurpose a third codon to biocontain this virus-resistant host through dependence on an amino acid not found in nature. Furthermore, our results may provide the basis for a general strategy to make any organism safely resistant to all natural viruses and prevent genetic information flow into and out of genetically modified organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The persistence of memory in ionic conduction probed by nonlinear optics

Predicting practical rates of transport in condensed phases enables the rational design of materials, devices and processes. This is especially critical to developing low-carbon energy technologies such as rechargeable batteries. For ionic conduction, the collective mechanisms, variation of conductivity with timescales and confinement, and ambiguity in the phononic origin of translation, call for a direct probe of the fundamental steps of ionic diffusion: ion hops. However, such hops are rare-event large-amplitude translations, and are challenging to excite and detect. Here we use single-cycle terahertz pumps to impulsively trigger ionic hopping in battery solid electrolytes. This is visualized by an induced transient birefringence, enabling direct probing of anisotropy in ionic hopping on the picosecond timescale. The relaxation of the transient signal measures the decay of orientational memory, and the production of entropy in diffusion. We extend experimental results using in silico transient birefringence to identify vibrational attempt frequencies for ion hopping. Using nonlinear optical methods, we probe ion transport at its fastest limit, distinguish correlated conduction mechanisms from a true random walk at the atomic scale, and demonstrate the connection between activated transport and the thermodynamics of information.

25 ENERGY STORAGE↗
DOE OSTI2025

Cooper-pair density modulation state in an iron-based superconductor

Superconducting (SC) states that break space-group symmetries of the underlying crystal can exhibit nontrivial spatial modulation of the order parameter. Previously, such states were intimately associated with the breaking of translational symmetry, resulting in the density-wave orders, with wavelengths spanning several unit cells. However, a related basic concept has long been overlooked: when only intra-unit-cell symmetries of the space group are broken, the SC states can show a distinct type of nontrivial modulation preserving long-range lattice translation. Here, in this study, we refer to this new concept as the pair density modulation (PDM) and report the first observation of a PDM state in exfoliated thin flakes of the iron-based superconductor FeTe 0.55 Se 0.45 . Using scanning tunnelling microscopy (STM), we discover robust SC gap modulation with the wavelength corresponding to the lattice periodicity and the amplitude exceeding 30% of the gap average. Notably, we find that the observed modulation originates from the large difference in SC gaps on the two nominally equivalent iron sublattices. The experimental findings, backed up by model calculations, suggest that, in contrast to the density-wave orders, the PDM state is driven by the interplay of sublattice symmetry breaking and a peculiar nematic distortion specific to the thin flakes. Our results establish new frontiers for exploring the intertwined orders in strong-correlated electronic systems and open a new chapter for iron-based superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Millisecond lifetimes and coherence times in 2D transmon qubits

Materials improvement is a powerful approach to reducing loss and decoherence in superconducting qubits, because such improvements can be readily translated to large-scale processors. Recent work improved transmon coherence by using tantalum as a base layer and sapphire as a substrate. The losses in these devices are dominated by two-level systems with comparable contributions from both the surface and bulk dielectrics, indicating that both must be tackled to achieve substantial improvements in the state of the art. Here we show that replacing the substrate with high-resistivity silicon markedly decreases the bulk substrate loss, enabling 2D transmons with time-averaged quality factors (Q avg ) of 9.7 × 10 6 across 45 qubits. For our best qubit, we achieve a Q avg of 1.5 × 10 7 , reaching a maximum Q of 2.5 × 10 7 , corresponding to a lifetime (T 1 ) up to 1.68 ms. This low loss also allows us to observe decoherence effects related to the Josephson junction, and we use an improved, low-contamination junction deposition to achieve Hahn echo coherence times (T 2E ) exceeding T 1 . We achieve these materials improvements without any modifications to the qubit architecture, allowing us to readily incorporate standard quantum control gates. Here, we demonstrate single-qubit gates with 99.994% fidelity. The tantalum-on-silicon platform comprises a simple material stack that can potentially be fabricated at the wafer scale and therefore can be readily translated to large-scale quantum processors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗