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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Shifts in evolutionary lability underlie independent gains and losses of root-nodule symbiosis in a single clade of plants

Abstract Root nodule symbiosis (RNS) is a complex trait that enables plants to access atmospheric nitrogen converted into usable forms through a mutualistic relationship with soil bacteria. Pinpointing the evolutionary origins of RNS is critical for understanding its genetic basis, but building this evolutionary context is complicated by data limitations and the intermittent presence of RNS in a single clade of ca. 30,000 species of flowering plants, i.e., the nitrogen-fixing clade (NFC). We developed the most extensive de novo phylogeny for the NFC and an RNS trait database to reconstruct the evolution of RNS. Our analysis identifies evolutionary rate heterogeneity associated with a two-step process: An ancestral precursor state transitioned to a more labile state from which RNS was rapidly gained at multiple points in the NFC. We illustrate how a two-step process could explain multiple independent gains and losses of RNS, contrary to recent hypotheses suggesting one gain and numerous losses, and suggest a broader phylogenetic and genetic scope may be required for genome-phenome mapping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

Abstract The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å −1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Science & Technology - Other Topics↗
DOE OSTI2024

Non-averaged single-molecule tertiary structures reveal RNA self-folding through individual-particle cryo-electron tomography

Large-scale and continuous conformational changes in the RNA self-folding process present significant challenges for structural studies, often requiring trade-offs between resolution and observational scope. Here, we utilize individual-particle cryo-electron tomography (IPET) to examine the post-transcriptional self-folding process of designed RNA origami 6-helix bundle with a clasp helix (6HBC). By avoiding selection, classification, averaging, or chemical fixation and optimizing cryo-ET data acquisition parameters, we reconstruct 120 three-dimensional (3D) density maps from 120 individual particles at an electron dose of no more than 168 e - Å -2 , achieving averaged resolutions ranging from 23 to 35 Å, as estimated by Fourier shell correlation (FSC) at 0.5. Each map allows us to identify distinct RNA helices and determine a unique tertiary structure. Statistical analysis of these 120 structures confirms two reported conformations and reveals a range of kinetically trapped, intermediate, and highly compacted states, demonstrating a maturation folding landscape likely driven by helix-helix compaction interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A substrate-multiplexed platform for profiling enzymatic potential of plant family 1 glycosyltransferases

Plants have expanded various biosynthetic enzyme families to produce a wide diversity of natural products; however, most enzymes encoded in plant genomes remain uncharacterized, highlighting the need for new functional genomic approaches. Here, we report a platform enabling the rapid functional characterization of plant family 1 glycosyltransferases, which serve important roles in plant development, defense, and communication. Using substrate-multiplexed reactions, mass spectrometry, and automated analysis, we screen 85 enzymes against a diverse library of 453 natural products, for a total of nearly 40,000 possible reactions. The resulting dataset reveals a widespread promiscuity and a strong preference for planar, hydroxylated aromatic substrates among family 1 glycosyltransferases. We also characterize glycosyltransferases with an unusually wide substrate scope and with a non-canonical Cys-Asp catalytic dyad. This work establishes a widely-applicable enzymatic screening pipeline, reflects the immense glycosylation capability of plants, and has implications in biocatalysis, metabolic engineering, and gene discovery.

Sirirungruang, Sasilada↗
DOE OSTI2026

Solventless, Ambient-Pressure Production of Bio-Based Lactones Over Earth-Abundant, Mixed Metal Oxide Catalysts for Circular Polyesters

Transitioning to a circular plastics economy will require use of renewable feedstocks, energy-efficient processes, and closed-loop recyclable polymers, such as polyesters. A key challenge lies in sustainably sourcing monomers used to make recyclable polyesters. This work presents a catalytic platform utilizing earth-abundant Cu(x)Ca(1-x)O mixed metal oxides for the oxidative dehydrocyclization of bio-based diols to lactones, which are advantaged for energy-efficient ring-opening polymerization. Operating below 200 degrees C, at ambient pressure, and without solvent, the process uses air as the sole oxidant, achieving high yields of lactones across a broad substrate scope of C4-8 diols in the liquid phase. The oxidative dehydrocyclization reaction is thermodynamically downhill due to water formation and energy-efficient compared to incumbent, non-redox pathways utilized in fossil carbon-based industrial processes for lactone production. Mechanistic studies reveal facile redox cycling of Cu2+-O(Ca2+)-Cu2+ interfacial sites unique to the developed catalyst. Techno-economic analysis and life cycle assessment estimate 40% lower energy demand and 15% lower GHG intensity per mass of butyrolactone produced compared to the fossil carbon-based route. Liquid-phase oxidative dehydrocyclization offers a promising approach for scalable lactone production from renewable, bio-based diols to enable circular polyesters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Magneto-optical Kerr effect in an A-type antiferromagnet

Magneto-optic Kerr effect (MOKE) is a powerful probe of broken time-reversal symmetry (T$${{\mathcal{T}}}$$), typically used to study ferromagnets. While MOKE has been observed in some antiferromagnets (AFMs) with vanishing magnetization, it is often associated with structures whose symmetry is lower than basic collinear, bipartite order. In contrast, theory predicts a mechanism for MOKE intrinsic to all AFMs of A-type, i.e. layered AFMs in which ferromagnetic layers are antiferromagnetically aligned. Here we report the experimental confirmation of this mechanism in a bulk AFM. We achieve this by measuring the imaginary component of MOKE as a function of photon energy in MnBi2Te4, an A-type AFM where T$${{\mathcal{T}}}$$ is preserved in combination with a translation, and comparing the experimental results with model calculations. Our model suggests that observable MOKE should be expected in all collinear A-type AFMs with out-of-plane spin order, thus enabling optical detection of AFM domains and expanding the scope of MOKE to few-layer AFMs.

Sunko, Veronika↗
DOE OSTI2026

Mesoporous peptide frameworks engineered from crystallizable collagen-mimetic peptide amphiphiles

The rational design of porous frameworks with tunable pore dimensions and chemical functionalities is a critical step toward their implementation in diverse applications. While traditional porous materials are typically constructed from abiotic components, there is increasing interest in employing biologically derived building blocks (e.g., peptides and proteins) that offer unmatched structural and functional diversity. Here, we report the construction of crystalline mesoporous frameworks that are self-assembled from amphiphilic collagen-mimetic peptides. Comprehensive structural characterization via microscopy, spectroscopy, and computational techniques provides insights into the assembly packing model, in which hexagonally packed channels are interconnected by antiparallel-aligned collagen triple helices via hydrophobic and electrostatic interactions. Lastly, we demonstrate the functional potential of aCMP frameworks through the encapsulation of various molecular guests, including doxorubicin, an anti-cancer drug. Overall, this work establishes a class of mesoporous frameworks, derived from synthetically engineerable peptide conjugates, marking a significant step forward in broadening the architectural scope and application potential of peptide-based materials.

Perez, Anthony R↗
DOE OSTI2025

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Quantifying the phase diagram and Hamiltonian of S = 1/2 kagome antiferromagnets: bridging theory and experiment

Spin-1/2 kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings J 2 and Dzyaloshinskii-Moriya interactions D, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of J 2 and D. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.

Jiang, Shengtao [SLAC National Accelerator Laborat↗
DOE OSTI2026

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Lithium metal-mediated electrochemical reduction of per- and poly-fluoroalkyl substances

Per- and poly-fluoroalkyl substances (PFAS) have substantial environmental and health hazards. Unfortunately, current degradation routes require high temperatures or corrosive conditions and/or lead to incomplete defluorination and the generation of shorter alkyl chains. Inspired by the lithium-metal battery literature, here we develop an electrochemical degradation process that leverages reactive metals and highly reducing environments. Here, we show that electrodeposited lithium metal can enable 95% degradation and 94% defluorination of perfluorooctanoic acid to LiF without forming any shorter C 2 –C 6 PFAS as end products. Using computational simulations, we find that electron transfer from lithium to perfluorooctanoic acid leads to rapid C–F bond cleavage, fluoride formation and carbon chain fragmentation. We expand the scope to other PFAS compounds and demonstrate substantial degrees of degradation on over 22 different PFAS, plus complete mineralization to inorganic fluorides. Finally, we use the mineralized F − as a fluorine source for the synthesis of fluorinated non-PFAS compounds to complete a circular fluorine loop from waste to valuable product.

Sarkar, Bidushi [Univ. of Chicago, IL (United Stat↗
DOE OSTI2026

Nitrene generation and transfer for unnatural biosynthesis in living cells

Abstract Synthetic biology has enabled the production of natural and unnatural products from inexpensive sustainable feedstocks. Yet, the scope of available products has been limited largely to compounds accessible from nature’s chemical reactions. Evolved enzymes can catalyse reactions of unnatural substrates or reactions not found in nature but often require the addition of synthetic reagents to purified enzymes or to resting cells containing those enzymes. Here we show that chemical reactions of metabolic intermediates produced intracellularly in living cells can include intermolecular nitrene transfers. The biosynthesis of N -acetoxyanilines, in combination with the generation and transfer of N -aryl nitrene intermediates from them catalysed by a cytochrome P450, generates amino alcohols, diamines, diarylamines and aminoalkyl arenes from simple carbon feedstocks. These products—common substructures of pharmaceuticals and agrochemicals—are challenging to synthesize by standard organic chemistry and were produced from inexpensive, renewable feedstocks. Evolution of the enzymes in this pathway showed that titres can be increased by engineering and that the products can be synthesized with high enantioselectivity.

Donnell, Isaac↗
DOE OSTI2025

Aqueous-based recycling of perovskite photovoltaics

Abstract Cumulative silicon photovoltaic (PV) waste highlights the importance of considering waste recycling before the commercialization of emerging PV technologies 1,2 . Perovskite PVs are a promising next-generation technology 3 , in which recycling their end-of-life waste can reduce the toxic waste and retain resources 4,5 . Here we report a low-cost, green-solvent-based holistic recycling strategy to restore all valuable components from perovskite PV waste. We develop an efficient aqueous-based perovskite recycling approach that can also rejuvenate degraded perovskites. We further extend the scope of recycling to charge-transport layers, substrates, cover glasses and metal electrodes. After repeated degradation–recycling processes, the recycled devices show similar efficiency and stability compared with the fresh devices. Our holistic recycling strategy reduces by 96.6% resource depletion and by 68.8% human toxicity (cancer effects) impacts associated with perovskite PVs compared with the landfill treatment. With recycling, the levelized cost of electricity also decreases for both utility-scale and residential systems. This study highlights unique opportunities of perovskite PVs for holistic recycling and paves the way for a sustainable perovskite solar economy.

Science & Technology - Other Topics↗
DOE OSTI2025

A map of the rubisco biochemical landscape

Rubisco is the primary CO 2 -fixing enzyme of the biosphere, yet it has slow kinetics. The roles of evolution and chemical mechanism in constraining its biochemical function remain debated. Engineering efforts aimed at adjusting the biochemical parameters of rubisco have largely failed, although recent results indicate that the functional potential of rubisco has a wider scope than previously known. Here we developed a massively parallel assay, using an engineered Escherichia coli in which enzyme activity is coupled to growth, to systematically map the sequence–function landscape of rubisco. Composite assay of more than 99% of single-amino acid mutants versus CO 2 concentration enabled inference of enzyme velocity and apparent CO 2 affinity parameters for thousands of substitutions. This approach identified many highly conserved positions that tolerate mutation and rare mutations that improve CO 2 affinity. These data indicate that non-trivial biochemical changes are readily accessible and that the functional distance between rubiscos from diverse organisms can be traversed, laying the groundwork for further enzyme engineering efforts.

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

Random heteropolymers as enzyme mimics

Despite successes in replicating the primary–secondary–tertiary structure hierarchy of protein, it remains elusive to synthetically materialize protein functions that are deeply rooted in their chemical, structural and dynamic heterogeneities. We propose that for polymers with backbone chemistries different from that of proteins, programming spatial and temporal projections of sidechains at the segmental level can be effective in replicating protein behaviours; and leveraging the rotational freedom of polymer can mitigate deficiencies in monomeric sequence specificity and achieve behaviour uniformity at the ensemble level. Here, guided by the active site analysis of about 1,300 metalloproteins, we design random heteropolymers (RHPs) as enzyme mimics based on one-pot synthesis. We introduce key monomers as the equivalents of the functional residues of protein and statistically modulate the chemical characteristics of key monomer-containing segments, such as segmental hydrophobicity. The resultant RHPs form pseudo-active sites that provide key monomers with protein-like microenvironments, co-localize substrates with catalytic or cofactor-binding sidechains and catalyse reactions such as oxidation and cyclization of citronellal with isopulegol/menthoglycol selectivity. This RHP design led to enzyme-like materials that can retain catalytic activity under non-biological conditions, are compatible with scalable processing and have expanded substrate scope, including environmentally long-lasting antibiotic tetracycline.

36 MATERIALS SCIENCE↗
DOE OSTI2025

PAVC: The foundation for a Pan-Arctic Vegetation Cover database

Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and “other” vegetation, as well as absolute cover for litter and top cover for water and bare ground.

Steckler, Morgan R. [Oak Ridge National Laboratory↗
DOE OSTI2025

ORBITaL-Net: A labeled training library for large-scale building feature extraction

Over the course of several years, nearly 1.5 million building outlines have been created from approximately 128,000 training tiles covering roughly 7,000 km 2 of very high-resolution multispectral overhead imagery, primarily dated between 2010 and 2020. This dataset, dubbed the Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is designed for machine learning applications and is global in scope, with samples drawn from 72 countries across North America, South America, Africa, Europe, and Asia. ORBITaL-Net captures a great diversity in geographic setting, structural characteristics, land use (urban and rural), terrain, and imagery conditions. While the labeled building outlines are themselves valuable, the dataset’s true strength lies in the pairing of these labels with corresponding reference imagery, which is being released for open source use. Similar to SpaceNet and Replicable AI For Microplanning (ramp), this building outline dataset will allow the larger computer vision community from academia, government, and industry the opportunity to develop robust, scalable, and generalizable geospatial machine learning techniques. Unlike SpaceNet and ramp, which offer high resolution labels and imagery primarily for large urban cities, ORBITaL-Net is not focused on training samples from heavily populated areas but instead aims to capture the innate variability of conditions present in both the physical environment and imagery collections.

Geography↗
DOE OSTI2026

Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models

Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.

Lee, Heechan [ORNL]↗