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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Theoretical Insights into Reaction-Induced Transformation and Tuning of Catalytic Behavior in Heterogenous Catalysis

Reaction-induced transformations in heterogenous catalysis represent diverse phenomena that challenge traditional views of static catalyst surfaces. From surface adsorbate dynamics, atomic rearrangements, to composition and phase transitions, these processes reveal the profound differences between idealized model systems under ultrahigh vacuum and the complex, evolving interfaces that govern real catalytic behaviors under reaction conditions. Here, this perspective reviews recent theoretical efforts to provide atomic-level mechanistic insights into significant reaction-induced transformations and their impact on catalytic activity and selectivity. It underscores the need for an integrated framework that combines predictive simulations with operando characterization to uncover active sites and mechanisms under realistic operating conditions. Achieving this requires accelerating existing simulations to fully capture diverse reaction-induced surface dynamics, enabling scalable and accurate modeling of catalysts as condition-dependent, dynamically evolving systems. Such approaches are critical to bridge the gap between theory and practice, offering a pathway to more impactful and predictive catalyst design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Northern peatland microbial communities exhibit resistance to warming and acquire electron acceptors from soil organic matter

The response of microbial communities that regulate belowground carbon turnover to climate change drivers in peatlands is poorly understood. Here, we leverage a whole ecosystem warming experiment to elucidate the key processes of terminal carbon decomposition and community responses to temperature rise. Our dataset of 697 metagenome-assembled genomes (MAGs) represents the microbial community from the surface (10 cm) to 2 m deep into the peat column, with only 3.7% of genomes overlapping with other well-studied peatlands. Community composition has yet to show a significant response to warming after 3 years, suggesting that metabolically diverse soil microbial communities are resistant to climate change. Surprisingly, abundant and active methanogens in the genus Candidatus Methanoflorens, Methanobacterium , and Methanoregula show the potential for both acetoclastic and hydrogenotrophic methanogenesis. Nonetheless, the predominant pathways for anaerobic carbon decomposition include sulfate/sulfite reduction, denitrification, and acetogenesis, rather than methanogenesis based on gene abundances. Multi-omics data suggest that organic matter cleavage provides terminal electron acceptors, which together with methanogen metabolic flexibility, may explain peat microbiome composition resistance to warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Leveraging peptide–cellulose interactions to tailor the hierarchy and mechanics of peptide–polymer hybrids

Inspired by spider silk's hierarchical diversity, we leveraged peptide motifs with the capability to tune structural arrangement for controlling the mechanical properties of a conventional polymer framework. The addition of nanofiller with hydrogen bonding sites was used as another pathway towards hierarchical tuning via matrix–filler interactions. Specifically, peptide–polyurea hybrids (PPUs) were combined with cellulose nanocrystals (CNCs) to develop mechanically-tunable nanocomposites via tailored matrix–filler interactions (or peptide–cellulose interactions). In this material platform, we explored the effect of these matrix–filler interactions on the secondary structure, hierarchical ordering, and mechanical properties of the peptide hybrid nanocomposites. Interactions between the peptide matrix and CNCs occur in all of the PPU/CNC nanocomposites, preventing α-helical ordering, but promoting inter-molecular hydrogen bonded β-sheet formation. Depending on peptide and CNC content, the Young's modulus varies from 10 to 150 MPa. Unlike conventional cellulose-reinforced polymer nanocomposites, the mechanical properties of these composite materials are dictated by a balance of CNC reinforcement, peptidic ordering, and microphase-separated morphology. This research highlights that leveraging peptide–cellulose interactions is a strategy to create materials with targeted mechanical properties for a specific application using a limited selection of building blocks.

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

C-C Coupling Mechanism on Cu(100) A Molecular Dynamics Study at 298K

The electrochemical reduction of carbon dioxide (CO2) into valuable fuels such as C1 (syngas, methane) and C2 (ethylene, ethanol) products is a key strategy for achieving a carbon-neutral economy. Computational studies of C-C coupling, a critical step in CO2 reduction, are essential for designing more efficient catalysts. However, simulating these processes under realistic electrochemical conditions, including temperature and solvent effects, is computationally demanding. In this work, we develop a machine learning-based atomistic potential to study CO2 reduction on Cu(100) surfaces, accounting for temperature and explicit water solvent effects. We compute thermodynamic free energies of the possible C-C coupling pathways, CO*+CO*→OCCO*, CO*+CHO*→OCCHO*, CO*+COH*→OCCOH*, CHO*+COH*→OHCCOH*, COH*-COH*→HOCCOH*, and CHO*-CHO*→OHCCHO*. Our results quantify the thermodynamic tendencies of these reactions and reveal that, in addition to the well-established CO* + CO* → OCCO* pathway, CHO* is a critical intermediate in the formation of C2 products on Cu(100). Furthermore, we demonstrate that the machine learning approach offers a cost-efficient framework for studying CO2 reduction on diverse catalysts under realistic electrochemical conditions.

machine learning↗
DOE OSTI2026

Conjugation-based genome engineering enables rapid prototyping and bioproduction in non-model bacteria

Abstract Non-model bacteria offer unique metabolic capabilities for sustainable bioproduction, yet their limited genetic accessibility hinders systematic strain development. Here we present conjugation-based serine recombinase-assisted genome engineering (cSAGE), a broad-host-range platform that enables predictable, iterative genomic integration in transformation-resistant bacteria. cSAGE combines conjugative DNA delivery, standardized low-copy vectors, orthogonal recombinases, and modular genetic parts to support rapid pathway assembly and cross-host benchmarking. Using purple nonsulfur bacteria as a testbed, we integrate promoter engineering, multi-payload genome modification, and genome-scale metabolic modeling to empirically evaluate host-dependent pathway performance. Applying this workflow, we identify strain-specific differences in photosynthetic conversion of lignin-derived p -coumarate to the thermoplastic precursor p -vinylphenol. By enabling genome engineering and functional comparison across diverse bacteria using a single plasmid system, cSAGE provides a general framework for non-model strain prototyping and biotransformation discovery.

Guzman, Michael S. [Department of Chemical Enginee↗
DOE OSTI2023

Quantitative elemental imaging in eukaryotic algae

Abstract All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved by how these elements are utilized, for what purpose, and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active centers of essential enzymes, can determine the state of metabolism, the nutritional status, or the developmental stage of an organism. Photosynthetic eukaryotes, especially algae, are excellent subjects for quantitative analysis of elemental distribution. These microbes utilize unique metabolic pathways that require various trace nutrients at their core to enable their operation. Photosynthetic microbes also have important environmental roles as primary producers in habitats with limited nutrient supplies or toxin contaminations. Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration, and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution. A number of diverse applications were developed for elemental imaging, allowing subcellular resolution, with X-ray fluorescence microscopy (XFM, XRF) being at the forefront, enabling quantitative descriptions of intact cells in a non-destructive method. This Tutorial Review summarizes the workflow of a quantitative, single-cell elemental distribution analysis of a eukaryotic alga using XFM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Linkage mapping evidence for a syntenic QTL associated with flowering time in perennial C 4 rhizomatous grasses Miscanthus and switchgrass

Flowering in perennial species is directed via complex signalling pathways that adjust to developmental regulations and environmental cues. Synchronized flowering in certain environments is a prerequisite to commercial seed production, and so the elucidation of the genetic architecture of flowering time in Miscanthus and switchgrass could aid breeding in these underdeveloped species. In this context, we assessed a mapping population in Miscanthus and two ecologically diverse switchgrass mapping populations over 3 years from planting. Multiple flowering time quantitative trait loci (QTL) were identified in both species. Remarkably, the most significant Miscanthus and switchgrass QTL proved to be syntenic, located on linkage groups 4 and 2, with logarithm of odds scores of 17.05 and 21.8 respectively. These QTL regions contained three flowering time transcription factors: Squamosa Promoter-binding protein-Like, MADS-box SEPELLATA2 and gibberellin-responsive bHLH137. The former is emerging as a key component of the age-related flowering time pathway.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulating Discharge Curves of an All-Aqueous TRAB to Identify Pathways for Improving System Performance

Thermally regenerative ammonia batteries (TRABs) are an emerging technology that use low temperature heat (T < 150 °C) to recharge a flow battery that produces electrical power on demand. The all-aqueous copper TRAB can provide high power densities and thermal energy efficiencies relative to other devices that harvest energy from waste heat, but its performance is adversely impacted by the crossover of undesired species through the membrane and lower cell voltages compared to conventional batteries. In this work, we developed a numerical model to simulate discharge curves while accounting for crossover inefficiencies without tracking all electrolyte species through the membrane. The model was able to successfully reproduce discharge curves across a diverse range of battery conditions using a single fitting parameter to account for decay of electrode standard potential due to species crossover with minimal error (< 5%). The model was then used to simulate different design scenarios to estimate changes in energy output from alterations to the aspects of the battery electrolyte chemistry. Results from this study are used to identify pathways for improving future TRAB designs with respect to energy capacity and cost-effectiveness of the technology.

25 ENERGY STORAGE↗
DOE OSTI2026

Biocatalyst discovery and design for plastics deconstruction: A multi‐scale perspective

Plastic waste accumulation poses significant environmental challenges due to a lack of economical solutions for the molecular deconstruction of diverse synthetic polymers. Biological‐based degradation offers promise but is hindered by the crystallinity, hydrophobicity, and additive complexity of plastics, which restrict biocatalyst access and activity. To address these problems, we propose a multi‐scale framework that combines detailed materials characterization, optimization of plastic‐biomolecular interfacial interactions, and enhancement of biocatalytic kinetics to develop effective plastic‐deconstructing enzymes. This approach leverages principles from reaction kinetics, transport and interfacial phenomena, and enzyme engineering to systematically address barriers across diverse plastic types. Our framework aims to accelerate the discovery and optimization of biocatalysts capable of scalable, selective, and efficient deconstruction of plastic waste. These advances hold potential to enable sustainable biological recycling and upcycling pathways, contributing to global efforts in mitigating plastic pollution and promoting circular material economies.

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

A radioisotope - enabled reactive transport model for deep vadose zone carbon

In mountainous regions, which constitute the principle source of recharge to major rivers and regional aquifers, infiltration occurs through fractured, partially saturated, weathered bedrock that acts as a boundary layer between saturated aquifers and surface soil. Commonly this deep vadose zone (DVZ) is many meters thick, and yet its role in regulating the generation, retention and mobility of reactive solutes, including nutrients, contaminants and weathering products, is largely unknown. In particular, many of the key reactions that drive the formation of the weathered DVZ and the quality of water moving through it are redox processes, regulated by the availability of organic carbon and oxygen below the soil layer. The role of the DVZ is thus also poorly constrained in the context of carbon stocks and mobility, particularly in lithologies that are naturally high in organic carbon, such as shales. The overarching hypothesis of this study is that upland regions developed in geologic settings with abundant petrogenic carbon store and actively cycle carbon in the weathered DVZ below the soil and above the water table at rates that are significant and currently unconstrained. In order to quantify this cycling, the current study combines novel instrumentation techniques allowing new direct sampling of DVZ systems with advanced numerical reactive transport simulations of carbon transport and transformation. Critically, these simulations will explicitly treat the three isotopes of carbon (the abundant 12C, the stable rare 13C and the radioactive 14C) in a unified framework, thus clearly parsing between the contributions of modern surface derived carbon and lithologic carbon sources in integrated measurements of fluid and gas phase fluxes. This novel model capability will be applied to test the role of DVZ carbon cycling as a regulator of water quality and geological weathering in two complementary field sites both located in organic carbon rich shale lithologies. The first is the Eel River Critical Zone Observatory (ERCZO) in Mendocino County, California, and the second is the Lawrence Berkeley National Laboratory Watershed Function Scientific Focus Area (SFA) in the East River watershed, near Crested Butte, Colorado. At the ERCZO, a novel vadose zone monitoring system has been installed in a 20 m thick, partially saturated, weathered shale hillslope, and preliminary data already indicate substantial CO2 flux generated many meters below the soil surface. At the SFA field site, an instrumented hillslope transect indicates a more complex multi-dimensional fluid and solute transport regime, which will serve as a key test of the calibrated models. Collectively, this project will advance understanding of the cycling of carbon belowground and in relation to transport pathways across the poorly constrained DVZ characteristic of primary water recharge areas. The key product of this work will be enhanced isotope simulation capabilities that are robust and publicly available for application across a broad diversity of systems.

58 GEOSCIENCES↗
DOE OSTI2025

An MLCommons Scientific Benchmarks Ontology

Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benchmarking developed through a unified, community-driven effort that extends the MLCommons ecosystem to cover physics, chemistry, materials science, biology, climate science, and more. Building on prior initiatives such as XAI-BENCH, FastML Science Benchmarks, PDEBench, and the SciMLBench framework, our effort consolidates a large set of disparate benchmarks and frameworks into a single taxonomy of scientific, application, and system-level benchmarks. New benchmarks can be added through an open submission workflow coordinated by the MLCommons Science Working Group and evaluated against a six-category rating rubric that promotes and identifies high-quality benchmarks, enabling stakeholders to select benchmarks that meet their specific needs. The architecture is extensible, supporting future scientific and AI/ML motifs, and we discuss methods for identifying emerging computing patterns for unique scientific workloads. The MLCommons Science Benchmarks Ontology provides a standardized, scalable foundation for reproducible, cross-domain benchmarking in scientific machine learning. A companion webpage for this work has also been developed as the effort evolves: https://mlcommons-science.github.io/benchmark/

Hawks, Ben [Fermilab] (ORCID:0000000157000288)↗
DOE OSTI2024

A Pathway Analysis Framework for Evaluating the Economic and Environmental Viability of Biomass-Based Plastic Production

Plastic production from fossil feedstocks (e.g., naphtha, coal, and natural gas) is not sustainable and causes known environmental impacts such as global warming. A possible solution is to shift production pathways to use biomass, which is a sustainable feedstock that can sequester atmospheric carbon dioxide. This study presents an optimization-based pathway analysis framework for evaluating the carbon footprints of the production of mainstream plastics from biomass and fossil feedstocks. We use the modeling framework to quickly navigate complex interdependencies that exist between the production pathways of different plastics and to determine pathways of minimum production cost under a range of carbon pricing scenarios. The framework interprets carbon prices as an exogenous taxation scheme or an endogenous negative value perceived by producers. The proposed approach reveals the biomass feedstock quantities needed to displace fossil counterparts and the plastics and technologies that should be prioritized. The framework can also be used for evaluating system-wide trade-offs between production costs and carbon footprints that arise from pathway interdependencies. We also evaluate hidden environmental impacts associated with the large-scale use of biomass as a feedstock, such as land use and water eutrophication that results from a significant increase in fertilizer use. Therefore, it is important to highlight that there are trade-offs between decarbonization and other environmental issues. Here, the proposed framework provides an integrative platform for basic techno-economic and life-cycle data that can be used for analyzing diverse scenarios and determining necessary technology targets (e.g., yields, footprints, and costs) to achieve required levels of decarbonization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Catchment storage and residence time in a periodically irrigated watershed

Understanding anthropogenic impacts on water storage and water flow pathways in catchments is an ongoing challenge in hydrology. Here, we study the dynamics of subsurface storage and residence time of water in a catchment in Berkeley, California, that is within a regional park but contains diverse land use within its perimeter, including a periodically irrigated golf course. Our study combines several isotopic tracers with water budget data to examine sources of water in a stream draining the site. Irrigation water, applied to a small area of the watershed, is a minor component of the water budget. However, geochemical tracers reveal that irrigation water is a significant fraction of stream flow downstream of the golf course during baseflow and during precipitation events. Isotopic tracers indicate that the watershed has a preference to release young water for stream flow generation, resulting in contrasting tritium ages for stream water and groundwater of 1.3 ± 0.5 year and 8.2 ± 1.7 year, respectively. We determined that the older water is a very small component (0.7%) of the stream water in the tail of an assumed exponential distribution. We used the seasonal variation of stable water isotopes in precipitation and stream water over two water years to explain the damping of the isotopic signature of stream water, which yields information about the catchment's response to the input signal. In conclusion, the methods described here may be applicable to other urban or suburban headwater catchments in areas with a component of non-natural recharge from, for example, leaky infrastructure, storm water routing or dry season irrigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2022

Expanding the use of ethanol as a feedstock for cell-free synthetic biochemistry by implementing acetyl-CoA and ATP generating pathways

Abstract Ethanol is a widely available carbon compound that can be increasingly produced with a net negative carbon balance. Carbon-negative ethanol might therefore provide a feedstock for building a wider range of sustainable chemicals. Here we show how ethanol can be converted with a cell free system into acetyl-CoA, a central precursor for myriad biochemicals, and how we can use the energy stored in ethanol to generate ATP, another key molecule important for powering biochemical pathways. The ATP generator produces acetone as a value-added side product. Our ATP generator reached titers of 27 ± 6 mM ATP and 59 ± 15 mM acetone with maximum ATP synthesis rate of 2.8 ± 0.6 mM/h and acetone of 7.8 ± 0.8 mM/h. We illustrated how the ATP generating module can power cell-free biochemical pathways by converting mevalonate into isoprenol at a titer of 12.5 ± 0.8 mM and a maximum productivity of 1.0 ± 0.05 mM/h. These proof-of-principle demonstrations may ultimately find their way to the manufacture of diverse chemicals from ethanol and other simple carbon compounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rapidly evolving aerosol emissions are a dangerous omission from near-term climate risk assessments

Abstract Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, driving strong, spatially complex trends in temperature, hydroclimate, and extreme events both near and far from emission sources. Under-resourced, highly populated regions often bear the brunt of aerosols’ climate and air quality effects, amplifying risk through heightened exposure and vulnerability. However, many policy-facing evaluations of near-term climate risk, including those in the latest Intergovernmental Panel on Climate Change assessment report, underrepresent aerosols’ complex and regionally diverse climate effects, reducing them to a globally averaged offset to greenhouse gas warming. We argue that this constitutes a major missing element in society’s ability to prepare for future climate change. We outline a pathway towards progress and call for greater interaction between the aerosol research, impact modeling, scenario development, and risk assessment communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

F‐Box Protein‐Mediated Proteolytic Regulation of Phenylpropanoid Metabolism in Response to Biotic and Abiotic Stresses

Protein ubiquitination is a central regulatory mechanism governing plant growth, development and environmental adaptation. Ubiquitylomic studies have revealed that many enzymes in phenylpropanoid biosynthetic pathways are subject to ubiquitination. Increasing evidence indicates that specific F-box proteins target key enzymes in these pathways, including PAL, CCR, CAD, COMT and peroxidases in the lignin biosynthetic branch, and CHS in the flavonoid biosynthetic branch, thereby promoting their ubiquitination and selective degradation. These F-box proteins act in response to diverse developmental and environmental cues, including cellular carbon status, light quality and intensity, and biotic stresses (e.g., pathogen and insect attack). By regulating the stability and activity of both enzymes and regulatory proteins involved in phenylpropanoid biosynthesis, F-box proteins modulate the accumulation of simple phenolics and lignin polymers, ultimately contributing to plant resilience. This review summarizes recent advances in the characterization of F-box proteins involved in phenylpropanoid metabolism and their regulatory roles in response to biotic and abiotic stresses and identifies key knowledge gaps that limit mechanistic understanding of F-box protein-mediated proteolytic regulation of phenylpropanoid metabolism. In conclusion, insights into ubiquitin-mediated proteolytic control of phenylpropanoid metabolism offer promising avenues for enhancing bioactive phenolic production, advancing biofuel feedstock engineering and improving crop stress tolerance.

(A)biotic stress↗
DOE OSTI2025

Work In Progress: Using Internships as Means for Indirect Assessment of ABET Criteria 3 “1-7” Student Outcomes

The Electrical & Computer Engineering (ECE) Department at Portland State University (PSU) has developed a Power Engineering Internship (PEI) program that provides engineering career development pathways within the electric utility industry. The PEI is supported by several U.S. Department of Energy grants that aim to develop quality career opportunities and develop a future electric utility workforce that represents the nation’s diverse populations. The PSU ECE Department intends to use surveys of internship participants as assessment tools for its ABET accreditation process, in particular, the ABET Criteria 3 Student Outcomes (SOs). SOs relate to the knowledge, skills, and behaviors that students acquire as they progress through an engineering program [1]. They describe what engineering students are expected to know and should be able to do by the time they graduate. Engineering education literature presents multiple methods for using internships as means for assessing SOs. Methods include using self-assessments that derive from internship experiences as well as mapping survey responses from employers and interns to SOs. Such methods gain value as they are assessed over multiple years. However, the majority of this literature discusses assessment tools that were developed for the ABET 2000 Criteria 3 “a through k” SOs. One of our motivations is to provide assessment tools that can be used to assess modern Criteria 3 SOs based on students’ internship experiences.

Bass, Robert [Portland State University]↗