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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 73 records · Page 4
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DOE OSTI2023

Biosynthesis of Haloterpenoids in Red Algae via Microbial-like Type I Terpene Synthases

Red algae or seaweeds produce highly distinctive halogenated terpenoid compounds, including the pentabromochlorinated monoterpene halomon that was once heralded as a promising anticancer agent. The first dedicated step in the biosynthesis of these natural product molecules is expected to be catalyzed by terpene synthase (TS) enzymes. Recent work has demonstrated an emerging class of type I TSs in red algal terpene biosynthesis. However, only one such enzyme from a notoriously haloterpenoid-producing red alga (Laurencia pacifica) has been functionally characterized and the product structure is not related to halogenated terpenoids. Herein, we report 10 new type I TSs from the red algae Portieria hornemannii, Plocamium pacificum, L. pacifica, and Laurencia subopposita that produce a diversity of halogenated mono- and sesquiterpenes. Here we used a combination of genome sequencing, terpenoid metabolomics, in vitro biochemistry, and bioinformatics to establish red algal TSs in all four species, including those associated with the selective production of key halogenated terpene precursors myrcene, trans-β-ocimene, and germacrene D-4-ol. These results expand on a small but growing number of characterized red algal TSs and offer insight into the biosynthesis of iconic halogenated algal compounds that are not without precedence elsewhere in biology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Modularity-at-scale for advanced reactors presentation

Modularity options have been limited for traditional nuclear energy deployment due to the conventional light water reactor (LWR) safety requirements, such as high pressure retaining heavy and robust containment structures. However, a relatively new regulatory approach called ‘Functional Containment’ has potential to allow less expensive and more flexible designs for non-LWRs. Functional containment provides flexibility in design and deployment based on risk-informed and performance-based criteria, so that reactors are not over-designed. Non-nuclear industry has successfully used modular design approaches in automotive, aerospace, chemical processing, building construction, and ship building. These industries have shown that modular construction reduces construction time by around 30% - 50% compared to the conventional stick-built approach. The nuclear industry can use similar approaches to reduce construction time and costs, balanced with safety requirements, using the functional containment approach. This paper discussing the background of modularity in nuclear energy, examples of less learned, modularity approaches in non-nuclear industries and the potential of cost and schedule savings through the emerging regulatory design flexibilities potentially enabling combination of modular deployment at different scales.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

MODULARITY-AT-SCALE FOR COST COMPETITIVE DEPLOYMENT OF NUCLEAR ENERGY

Modularity options have been limited for traditional nuclear energy deployment due to the conventional light water reactor (LWR) safety requirements, such as high pressure retaining heavy and robust containment structures. However, a relatively new regulatory approach called ‘Functional Containment’ has potential to allow less expensive and more flexible designs for non LWRs. Functional containment provides flexibility in design and deployment based on risk informed and performance-based criteria, so that reactors are not over-designed. Non-nuclear industry has successfully used modular design approaches in automotive, aerospace, chemical processing, building construction, and ship building. These industries have shown that modular construction reduces construction time by around 30% - 50% compared to the conventional stick built approach. The nuclear industry can use similar approaches to reduce construction time and costs, balanced with safety requirements, using the functional containment approach. This paper discussing the background of modularity in nuclear energy, examples of less learned, modularity approaches in non-nuclear industries and the potential of cost and schedule savings through the emerging regulatory design flexibilities potentially enabling combination of modular deployment at different scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Response of Subsurface Nitrogen-Cycling Microbial Communities to Environmental Fluctuations (Final Technical Report)

Riparian floodplains are dynamic ecosystems linking terrestrial and riverine systems. These floodplains experience hydrological shifts such as changes in water table height, flooding, and drought and can be ‘hotspots’ of biogeochemical cycling due to shifting sediment moisture (and saturation) and subsurface exchanges of water, nutrients, and other compounds across different sediment layers. Subsurface microbial communities are the primary drivers of biogeochemical processes in floodplains, and thus their structure and function can directly influence both surface and groundwater quality. The microbial nitrogen (N) cycle is particularly important in floodplains as it affects nutrient availability and removal. Two functional guilds of chemoautotrophic (i.e. CO2-fixing) microorganisms are responsible for the first oxidative step of the N cycle, nitrification: ammonia-oxidizing archaea (AOA) and bacteria (AOB) catalyze the oxidation of ammonia to nitrite, while nitrite-oxidizing bacteria (NOB) oxidize nitrite to nitrate. Despite the critical role nitrification plays in N-cycling in both terrestrial and aquatic ecosystems, our understanding of the diversity, ecophysiology, and activity of nitrifying organisms in subsurface floodplain soils/sediments is extremely limited. To help address this critical knowledge gap, the overarching goal of this project was to determine how shifts in key environmental parameters and gradients impact microbial N-cycling communities/processes, with particular emphasis on nitrification, within hydrologically-variable floodplain sediments in the Wind River Basin near Riverton, Wyoming. The three specific objectives of this project were to: (1) to associate in situ environmental drivers of N cycling with distinct functional guilds; (2) determine the guild response to variation in key ecosystem drivers; and (3) develop a dynamic ecosystem model of the microbial N cycle with the Riverton subsurface using community genomic and biogeochemical data collected in the first two objectives. Over the course of this project, we employed both 16S rRNA gene amplicon sequencing and genome-resolved metagenomics to examine the phylogenetic diversity and metabolic potential of subsurface nitrifier communities within 68 samples collected across multiple sites, depths, and time points within the Riverton floodplain, allowing for both spatial and temporal investigations at different scales. This project benefitted tremendously from recent advances in high-throughput sequencing technologies coupled with dramatic improvements in the computational tools and algorithms available for analyzing such large, complex genomic datasets. By pairing these cutting-edge genomic approaches with depth-resolved sampling and detailed geochemical analyses of the Riverton floodplain, we have gained novel insights into the structure and function of subsurface nitrifier communities in relation to both hydrology and biogeochemistry. This project resulted in the most detailed and comprehensive characterization of N-cycling floodplain microbial communities to date and will hopefully inspire and pave the way for future studies using similar approaches in other floodplains. Indeed, such information is critical for understanding subsurface biogeochemical cycling and how elemental stores are altered from perturbations initiated by the water cycle within floodplains. Finally, because of the terrestrial-aquatic nature of the Riverton floodplain, results from this project are also of relevance to disciplines such as soil science, estuarine science, limnology & oceanography, biogeochemistry, geobiology, environmental engineering, as well as genomics and data science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Gaia: An AI-enabled genomic context–aware platform for protein sequence annotation

Protein sequence similarity search is fundamental to biology research, but current methods are typically not able to consider crucial genomic context information indicative of protein function, especially in microbial systems. Here, we present Gaia (Genomic AI Annotator), a sequence annotation platform that enables rapid, context-aware protein sequence search across genomic datasets. Gaia leverages gLM2, a mixed-modality genomic language model trained on both amino acid sequences and their genomic neighborhoods to generate embeddings that integrate sequence-structure-context information. This approach allows for the identification of functionally and/or evolutionarily related genes that are found in conserved genomic contexts, which may be missed by traditional sequence- or structure-based search alone. Gaia enables real-time search of a curated database comprising more than 85 million protein clusters from 131,744 microbial genomes. We compare the homolog retrieval performance of Gaia search against other embedding and alignment-based approaches. We provide Gaia as a web-based, freely available tool.

Jha, Nishant↗
DOE OSTI2023

Lagrangian and Eulerian Supersaturation Statistics in Turbulent Cloudy Rayleigh–Bénard Convection: Applications for LES Subgrid Modeling

Abstract Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct numerical simulations (DNS) of turbulent Rayleigh–Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation time scale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the nonadiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

DMC-ICE13 : Ambient and high pressure polymorphs of ice from diffusion Monte Carlo and density functional theory

Ice is one of the most important and interesting molecular crystals, exhibiting a rich and evolving phase diagram. Recent discoveries mean that there are now 20 distinct polymorphs; a structural diversity that arises from a delicate interplay of hydrogen bonding and van der Waals dispersion forces. This wealth of structures provides a stern test of electronic structure theories, with Density Functional Theory (DFT) often not able to accurately characterize the relative energies of the various ice polymorphs. Thanks to recent advances that enable the accurate and efficient treatment of molecular crystals with Diffusion Monte Carlo (DMC), we present here the DMC-ICE13 dataset; a dataset of lattice energies of 13 ice polymorphs. This dataset encompasses the full structural complexity found in the ambient and high-pressure molecular ice polymorphs, and when experimental reference energies are available, our DMC results deliver sub-chemical accuracy. Using this dataset, we then perform an extensive benchmark of a broad range of DFT functionals. Of the functionals considered, revPBE-D3 and RSCAN reproduce reference absolute lattice energies with the smallest error, while optB86b-vdW and SCAN+rVV10 have the best performance on the relative lattice energies. Our results suggest that a single functional achieving reliable performance for all phases is still missing, and that care is needed in the selection of the most appropriate functional for the desired application. The insights obtained here may also be relevant to liquid water and other hydrogen-bonded and dispersion-bonded molecular crystals.

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

QuantumScents: Quantum-Mechanical Properties for 3.5k Olfactory Molecules

Quantitative structure–odor relationships are critically important for studies related to the function of olfaction. Current literature data sets contain expert-labeled molecules but lack feature data. This paper introduces QuantumScents, a quantum mechanics augmented derivative of the Leffingwell data set. QuantumScents contains 3.5k structurally and chemically diverse molecules ranging from 2 to 30 heavy atoms (CNOS) and their corresponding 3D coordinates, total PBE0 energy, molecular dipole moment, and per-atom Hirshfeld charges, dipoles, and ratios. The authors demonstrate that Hirshfeld charges and ratios contain sufficient information to perform molecular classification by training a Message Passing Neural Network with chemprop (Heid, E.; et al. ChemRxiv, 2023, DOI: 10.26434/chemrxiv-2023-3zcfl) to predict scent labels. Finally, the QuantumScents data set is freely available on Zenodo along with the authors’ code, example models, and data set generation workflow (https://zenodo.org/doi/10.5281/zenodo.8239853).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

SPARC: Structural properties associated with residue constraints

SPARC facilitates the generation of plausible hypotheses regarding underlying biochemical mechanisms by structurally characterizing protein sequence constraints. Such constraints appear as residues co-conserved in functionally related subgroups, as subtle pairwise correlations (i.e., direct couplings), and as correlations among these sequence features or with structural features. SPARC performs three types of analyses. First, based on pairwise sequence correlations, it estimates the biological relevance of alternative conformations and of homomeric contacts, as illustrated here for death domains. Second, it estimates the statistical significance of the correspondence between directly coupled residue pairs and interactions at heterodimeric interfaces. Third, given molecular dynamics simulated structures, it characterizes interactions among constrained residues or between such residues and ligands that: (a) are stably maintained during the simulation; (b) undergo correlated formation and/or disruption of interactions with other constrained residues; or (c) switch between alternative interactions. We illustrate this for two homohexameric complexes: the bacterial enhancer binding protein (bEBP) NtrC1, which activates transcription by remodeling RNA polymerase (RNAP) containing σ 54 , and for DnaB helicase, which opens DNA at the bacterial replication fork. Based on the NtrC1 analysis, we hypothesize possible mechanisms for inhibiting ATP hydrolysis until ADP is released from an adjacent subunit and for coupling ATP hydrolysis to restructuring of σ 54 binding loops. Based on the DnaB analysis, we hypothesize that DnaB ‘grabs’ ssDNA by flipping every fourth base and inserting it into cavities between subunits and that flipping of a DnaB-specific glutamine residue triggers ATP hydrolysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Tailored Silicone Network Architecture for Ultimate Mechanical Reinforcement

Hydrosilylation cured silicone elastomers are subject to reaction inefficiency, leading to incomplete and non-uniform crosslink networks, restricting the potential of mechanical reinforcement. This work investigates pre-synthesized, functional PDMS architectures as additives to improve ultimate mechanical performance relative to conventional single-step curing. Three custom, functional structures were prepared: a partially crosslinked PDMS scaffold (Structure A), a bottle-brush PDMS (Structure B), and a star-shaped PDMS derived from an MQ resin (Structure C). Rheological characterization was used to identify the ultimate design space and proper stoichiometric ratio for Structure A, and confirm successful formation of all structures for suitable incorporation into a base silicone formulation at 30wt%. Mechanical tests indicated that all three structures increased in ultimate tensile strength relative to their single-step counterparts, with Structure A providing additional improvements to toughness (432 vs. 258 kJ/m3) and ultimate elongation (158 vs. 115%). Furthermore, Structure B remained very soft in the unfilled state, while Structure C provided hardness (23 vs. 18 Shore A) and stiffness (780 vs. 420 kPa Young’s modulus) increases. In silica filled systems, Structure A retained increased strength but reduced elongation, while Structure B indicated strong reinforcement in terms of strength, toughness, and stiffness. Thermal analysis on the cure profiles of these materials suggested that pre-formation of network architectures enable a more complete reaction than a single-step process (15.9 vs. 15.1 J/g). Ultimately, these results indicate that tailoring PDMS architecture before the final cure can improve ultimate mechanical properties via improved network development in silicone elastomers. Furthermore, this work offers a promising strategy for designing higher-performance, more tunable silicone formulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Hybrid Quantum–Classical Study of Ion Adsorption at the Copper Electrode

Here, we present a set of computational studies that surveys the molecular structure and thermodynamics of simple ions at the electrode–electrolyte interface. A hybrid quantum–classical approach is used to obtain free energy profiles and solvent structure as a function of the ion’s position relative to the electrochemical liquid/solid interface and system potential. Model systems consist of a copper electrode and single halide or alkali metal ion in contact with an implicit solvent and reveal new insights into the interplay of forces that drive or resist ion absorption at the electrode/ electrolyte interface. We also expand on subtleties of absorption site preference and describe the complex relationship between the energetic barrier to absorption and the ion–electrode binding energy as a function of electrode potential.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Variance reduction in lattice QCD observables via normalizing flows

Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters. This work implements the approach for observables involving gluonic operator insertions in the SU(3) Yang-Mills theory and two-flavor QCD in four space-time dimensions. Variance reduction by factors of 10–60 is achieved in glueball correlation functions and in gluonic matrix elements related to hadron structure, with demonstrated computational advantages. The observed variance reduction is found to be approximately independent of the lattice volume, so volume transfer can be utilized to minimize training costs.

Abbott, Ryan [Columbia U.; MIT, Cambridge, CTP; IA↗
DOE OSTI2022

Shear strain alters the structure and migration mechanism of self-interstitial atoms in copper

Here, we use atomistic modeling to show that externally applied shear strain causes the lowest energy self-interstitial atom (SIA) structure in copper (Cu) to change from a <100>-type dumbbell to a <110>-type dumbbell. Concurrently, SIA migration switches from the 3-D random walk characteristic of <100>-type dumbbells to a 1-D mechanism analogous to that of crowdion SIAs. Furthermore, the relative energies of these two dumbbell structures as a function of strain are well predicted using elastic dipole tensors computed at zero strain, indicating that examination of these tensors may be used to assess the likelihood of strain-induced SIA structure transitions in other materials. Changes in lowest energy SIA structures and associated migration mechanisms stand to impact predictions of SIA behavior in irradiated solids.

36 MATERIALS SCIENCE↗
DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2023

Structural and functional validation of a highly specific Smurf2 inhibitor

Smurf1 and Smurf2 are two closely related member of the HECT (homologous to E6AP carboxy terminus) E3 ubiquitin ligase family and play important roles in the regulation of various cellular processes. Both were initially identified to regulate transforming growth factor‐β and bone morphogenetic protein signaling pathways through regulating Smad protein stability and are now implicated in various pathological processes. Generally, E3 ligases, of which over 800 exist in humans, are ideal targets for inhibition as they determine substrate specificity; however, there are few inhibitors with the ability to precisely target a particular E3 ligase of interest. In this work, we explored a panel of ubiquitin variants (UbVs) that were previously identified to bind Smurf1 or Smurf2. In vitro binding and ubiquitination assays identified a highly specific Smurf2 inhibitor, UbV S2.4, which was able to inhibit ligase activity with high potency in the low nanomolar range. Orthologous cellular assays further demonstrated high specificity of UbV S2.4 toward Smurf2 and no cross‐reactivity toward Smurf1. Structural analysis of UbV S2.4 in complex with Smurf2 revealed its mechanism of inhibition was through targeting the E2 binding site. In summary, we investigated several protein‐based inhibitors of Smurf1 and Smurf2 and identified a highly specific Smurf2 inhibitor that disrupts the E2–E3 protein interaction interface.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Electronic and Vibrational Contributions to the Bulk Stabilities of Trivalent 3d Transition Metal Oxyhydroxides from Electronic Structure Calculations

Any understanding of the thermodynamic driving forces active in the global cycles of earth abundant metals requires attendant knowledge of the bulk mineral free energies. This can be particularly challenging for metal (oxyhydr)oxide phases which at low temperature aqueous conditions can be produced at a variety of hydration states and particle sizes. Here we present high quality density functional theory calculations of the relative bulk stabilities of a geologically important family of polymorphic trivalent metal oxyhydroxide crystal structures MOOH (M=Cr, Al, Fe). To improve understanding of their relative stabilities we permute the metal cations into the various possible canonical structure types including goethite (isostructural to diaspore and bracewillite), lepidocrocite (isosctructural to boehmite), guyaniite, and grimaldiite. Electronic, vibrational, and magnetic contributions are considered on a consistent theoretical footing using hybrid functionals and converged plane-wave bases. We find the considered bulk structures to be thermodynamically degenerate within the precision of the available theory, despite the fact that the vibrational and magnetic contributions vary substantially among the cations. A significant conclusion is that Cr3+ supports strong hydrogen bonds, low O-H stretching frequencies and facile H movement relative to the other cations.

Prange, Micah P.↗
DOE OSTI2022

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Berry Curvature and Spin-One Color Superconductivity

Here, we explore the interplay between Berry curvature and topological properties in single-flavor color superconductors, where quarks form spin-one Cooper pairs. By deriving a new relation, we connect the topological nodal structure of the gap function in momentum space to the (non-Abelian) Berry flux associated with paired quarks. This generalizes the early work by Li and Haldane [Phys. Rev. Lett. 120, 067003 (2018)] to systems with additional internal quantum numbers, such as color. In the ultrarelativistic limit, we uncover rich topological structures driven by the interplay of spin, chirality, and color. Specifically, we identify chirality-induced topological nodes in the transverse (opposite chirality pairing) polar and 𝐴 phases. In contrast, the color-spin-locking phase lacks these nodes due to a nontrivial color Berry flux, which in turn induces gapless excitations with total Berry monopole charges of ±3/2—differing from conventional Weyl fermions. Our findings can be potentially extended to other fermionic systems carrying additional internal degrees of freedom.

Berry curvature↗