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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
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 OSTI2020

An investigation of the long-range and local structure of sub-stoichiometric zirconium carbide sintered at different temperatures

ZrC 1-x (sub-stoichiometric zirconium carbide), a group IV transition metal carbide, is being considered for various high temperature applications. Departure from stoichiometry changes the thermo-physical response of the material. Reported thermo-physical properties exhibit, in some cases, a degree of scatter with one likely contributor to this being the uncertainty in the C/Zr ratio of the samples produced. Conventional, methods for assigning C/Zr to samples are determined either by nominal stochiometric ratios or combustion carbon analysis. In this study, a range of stoichiometries of hot-pressed ZrC 1-x were examined by SEM, XRD, Raman spectroscopy and static 13 C NMR spectroscopy and used as a basis to correct the C/Zr. Graphite, amorphous, and ZrC 1-x carbon signatures are observed in the 13 C NMR spectra of samples and are determined to vary in intensity with sintering temperature and stoichiometry. In this study a method is outlined to quantify the stoichiometry of ZrC 1-x and free carbon phases, providing an improvement over the sole use and reliance of widely adopted bulk carbon combustion analysis. We report significantly lower C/Zr values determined by 13 C NMR analysis compared with carbon analyser and nominal methods. Furthermore, the location of carbon disassociated from the ZrC 1-x structure is analysed using SEM and Raman spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A data-driven approach to modeling cancer cell mechanics during microcirculatory transport

In order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer’s cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

CFD-based design optimization of ducted hydrokinetic turbines

Abstract Hydrokinetic turbines extract kinetic energy from moving water to generate renewable electricity, thus contributing to sustainable energy production and reducing reliance on fossil fuels. It has been hypothesized that a duct can accelerate and condition the fluid flow passing the turbine blades, improving the overall energy extraction efficiency. However, no substantial evidence has been provided so far for hydrokinetic turbines. To investigate this problem, we perform a CFD-based optimization study with a blade-resolved Reynolds-averaged Navier–Stokes (RANS) solver to explore the design of a ducted hydrokinetic turbine that maximizes the efficiency of energy extraction. A gradient-based optimization approach is utilized to effectively deal with the high-dimensional design space of the blade and duct geometry, with gradients being calculated through the adjoint method. The final design is re-evaluated through higher-fidelity unsteady RANS (URANS) simulations. Our optimized ducted turbine achieves an efficiency of about 54% over a range of operating conditions, higher than the typical 46% efficiency of unducted turbines.

13 HYDRO ENERGY↗
DOE OSTI2024

Long-term, sustainable solutions to radioactive waste management

Nuclear power plays a pivotal role in ensuring a scalable, affordable, and reliable low-carbon electricity supply. Along with other low-carbon energy technologies, nuclear energy is essential for reducing our reliance on fossil fuels, addressing climate change and air pollution, and achieving a sustainable economy. Whilst significant progress has been made in reducing the volume of final radioactive waste, its management remains one of the most important challenges when considering the continued use and expansion of nuclear energy. This recently published collection highlights the latest technological and scientific advances aimed to improve the safe, long-term, and sustainable management of wastes produced from nuclear power generation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Label-free functional analysis of root-associated microbes with dynamic quantitative oblique back-illumination microscopy

The increasing global demand for food, coupled with concerns about the environmental impact of synthetic fertilizers, underscores the urgency of developing sustainable agricultural practices. Nitrogen-fixing bacteria, known as diazotrophs, offer a potential solution by converting atmospheric nitrogen into bioavailable forms, reducing the reliance on synthetic fertilizers. However, a deeper understanding of their interactions with plants and other microbes is needed. In this study, we introduce a recently developed label-free 3D quantitative phase imaging technology called dynamic quantitative oblique back-illumination microscopy (DqOBM) to assess the functional dynamic activity of diazotrophs in vitro and in situ. Our experiments involved three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media with amendments of carbon and nitrogen sources. Over 5 days, we observed increased dynamics in nutrient-amended media. These results suggest that the observed bacterial dynamics correlate with their metabolic activity. Furthermore, we applied qOBM to visualize microbial dynamics within the root cap and elongation zone of Arabidopsis thaliana primary roots. This allowed us to identify distinct areas of microbial infiltration in plant roots without the need for fluorescent markers. Our findings demonstrate that DqOBM can effectively characterize microbial dynamics and provide insights into plant-microbe interactions in situ, offering a valuable tool for advancing our understanding of sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Impact of electric vehicle battery recycling on reducing raw material demand and battery life-cycle carbon emissions in China

The rapid growth of electric vehicles (EVs) in China challenges raw material demand. This study evaluates the impact of recycling and reusing EV batteries on reducing material demand and carbon emissions. Integrating a national-level vehicle stock turnover model with life-cycle carbon emission assessment, we found that replacing nickel-cobalt-manganese batteries with lithium iron phosphate batteries with battery recycling can reduce lithium, cobalt, and nickel demand between 2021 and 2060 by up to 7.8 million tons (Mt) (67%), 12.4 Mt (96%), and 37.2 Mt (93%), respectively, significantly decreasing reliance on import. Moreover, battery recycling coupled with reuse can reduce carbon emissions by up to 6,532-6,864 Mt (36.0-37.9%), depending on four recycling methods employed. However, this reuse strategy delays battery recycling and risks lithium supply shortage, necessitating trade-offs between carbon reduction and material supply. Future technologies, such as lithium-sulfur and all-solid-state batteries, despite their energy efficiency, might exacerbate lithium shortage, underscoring the crucial need for increased lithium supply.

25 ENERGY STORAGE↗
DOE OSTI2025

Assessing the deployment of solar-driven hydrogen from biomass at scale in the U.S.

Solar hydrogen from biomass gasification is a promising technology to sustainably produce hydrogen, responsibly dispose biomass waste, and reduce reliance on fossil fuels. However, its large-scale deployment faces challenges due to the geospatial misalignment between biomass resources and solar intensity, which introduces additional supply chain logistics costs. We analyze the logistics cost burden imposed by this misalignment and its impact on successful large-scale deployment of solar-driven hydrogen from biomass in the United States. We also consider associated carbon emissions and explore how the mix of deployed technologies evolves under externally imposed carbon penalties. Our findings show that while economies of scale are known to apply at the processing facility level, the reverse effect occurs at the broader systems-level, driven by logistics. Also, at current technology costs, high carbon penalties would be required to favor deployment of solar based technologies over conventional and hybrid alternatives. We further illustrate strategies and system-level changes to reduce logistics costs and enable sustainable, low-cost hydrogen for decarbonizing different industrial sectors.

Iloeje, Chukwunwike O. [Argonne National Laborator↗
DOE OSTI2024

Global peak water limit of future groundwater withdrawals

Over the past 50 years, humans have extracted the Earth’s groundwater stocks at a steep rate, largely to fuel global agro-economic development. Given society’s growing reliance on groundwater, we explore ‘peak water limits’ to investigate whether, when and where humanity might reach peak groundwater extraction. Using an integrated global model of the coupled human–Earth system, we simulate groundwater withdrawals across 235 water basins under 900 future scenarios of global change over the twenty-first century. Here we find that global non-renewable groundwater withdrawals exhibit a distinct peak-and-decline signature, comparable to historical observations of other depletable resources (for example, minerals), in nearly all (98%) scenarios, peaking on average at 625 km 3 yr -1 around mid-century, followed by a decline through 2100. The peak and decline occur in about one-third (82) of basins, including 21 that may have already peaked, exposing about half (44%) of the global population to groundwater stress. Most of these basins are in countries with the highest current extraction rates, including the United States, Mexico, Pakistan, India, China, Saudi Arabia and Iran. Furthermore, these groundwater-dependent basins will probably face increasing costs of groundwater and food production, suggesting important implications for global agricultural trade and a diminished role for groundwater in meeting global water demands during the twenty-first century.

58 GEOSCIENCES↗
DOE OSTI2025

Low-nickel cathode chemistry for sustainable and high-energy lithium-ion batteries

The transition to sustainable energy storage demands lithium-ion batteries with high energy density and reduced reliance on critical metals such as nickel (Ni), yet current strategies to increase capacity have largely depended on raising Ni content, leading to escalating supply risks, rising costs and sustainability concerns. More critically, Ni-rich cathodes suffer from rapid electrochemical degradation driven by structural instability, creating an insurmountable trade-off between capacity and cycle life. Here, in this study, we introduce a low-Ni chemistry cathode, Li(Li 0.05 Ni 0.57 Mn 0.31 Co 0.07 )O 2 , with a radial phase integration design that overcomes these limitations, enabling a remarkable Ni usage reduction (Ni < 0.6) while demonstrating high capacity (215 mAh g −1 ) and markedly improved cyclability (~97% retention over 400 cycles) compared to conventional high-Ni cathodes (Ni = 0.8). Advanced X-ray and electron microscopy analyses reveal that the designed cathode exhibits a highly reversible oxygen anionic redox, benefiting from a structurally stable surface and minimizing irreversible phase transitions. Moreover, the integrated structure substantially mitigates lattice strain and improves mechanical stability even under harsh conditions. In conclusion, this advance offers a general design principle for developing next-generation cathodes that combine resource efficiency with long-term electrochemical reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Absolute band-edge energies are over-emphasized in the design of photoelectrochemical materials

The absolute band-edge potentials of semiconductors, i.e., the conduction-band minimum, valence-band maximum, and their relative positions to solution redox potentials, are often invoked as design principles for photoelectrochemical (PEC) devices, especially for particulate photocatalysts. Here we show that reliance on these criteria is not necessary and limits the exploration of materials that will advance the fields of photoelectrochemistry, photochemistry, and photocatalysis. We discuss how i) band-edge energies are not singular parameters and instead shift with pH, electrolyte type, and surface chemistry; ii) the free energy of electrons and holes in comparison to that of solution redox couples dictates overall reaction spontaneity and thus reactivity; and iii) favorable charge-transfer kinetics can occur even when the relevant electrolyte redox potential(s) appear ‘outside’ the bandgap, enabled by the inversion or accumulation of electronic charge at the semiconductor surface. As a result, this discussion informs design principles for photocatalyst systems engineering for both one-electron redox reactions as well as for more complex multi-electron transfer reactions (e.g, H 2 evolution, H 2 O oxidation, CO 2 reduction).

14 SOLAR ENERGY↗
DOE OSTI2026

Harnessing photoenzymatic reactions for unnatural biosynthesis in microorganisms

Photobiocatalysis provides a powerful strategy for integrating light and biological catalysts to drive abiological transformations. However, its scalability is hindered by high enzyme loading, reliance on costly cofactors and instability under radical-generating conditions. Here we report the integration of light-driven enzymatic reactions into the cellular metabolism of Escherichia coli, bridging flavin-based photobiocatalysis with biosynthesis. Using synthetic biology strategies, we engineered microbial cells to continuously produce olefin substrates and ene-reductase while regenerating cofactors directly from glucose. By externally supplying radical precursors or introducing synthetic pathways for their in situ production, we enabled fermentation-based microbial photobiosynthesis, achieving high titres and demonstrating feasibility for scale-up in a bioreactor. This approach extends photobiocatalysis from in vitro applications to in vivo semi- and complete biosynthesis, revealing its full potential for integrating light-driven reactions into cellular metabolism.

Biocatalysis↗
DOE OSTI2023

Vitamin B12 conveys a protective advantage to phycosphere-associated bacteria at high temperatures

Abstract Many marine microbes require vitamin B12 (cobalamin) but are unable to synthesize it, necessitating reliance on other B12-producing microbes. Thus, phytoplankton and bacterioplankton community dynamics can partially depend on the production and release of a limiting resource by members of the same community. We tested the impact of temperature and B12 availability on the growth of two bacterial taxa commonly associated with phytoplankton: Ruegeria pomeroyi, which produces B12 and fulfills the B12 requirements of some phytoplankton, and Alteromonas macleodii, which does not produce B12 but also does not strictly require it for growth. For B12-producing R. pomeroyi, we further tested how temperature influences B12 production and release. Access to B12 significantly increased growth rates of both species at the highest temperatures tested (38 °C for R. pomeroyi, 40 °C for A. macleodii) and A. macleodii biomass was significantly reduced when grown at high temperatures without B12, indicating that B12 is protective at high temperatures. Moreover, R. pomeroyi produced more B12 at warmer temperatures but did not release detectable amounts of B12 at any temperature tested. Results imply that increasing temperatures and more frequent marine heatwaves with climate change will influence microbial B12 dynamics and could interrupt symbiotic resource sharing.

Mars Brisbin, Margaret (ORCID:0000000177560039)↗
DOE OSTI2023

Making coordination networks ionic: a unique strategy to achieve solution-processable hybrid semiconductors

The development of high-performance, solution-processable semiconducting materials is crucial for the advancement of emerging clean-energy technologies such as light-emitting diodes and photovoltaics. While hybrid perovskites have shown considerable promise for implementation in these technologies, their reliance on toxic metals and relatively low stability towards moisture and chemical environments remain to be addressed. In this Chemistry Frontiers article, we describe a unique strategy to build nontoxic, robust and solution-processable hybrid semiconductors based on copper halide by incorporating ionic bonds in coordination complexes (molecular or extended network structures). Specifically, these compounds are made of anionic copper(I) halide and cationic organic ligands that form both coordinate and ionic bonds at the inorganic/organic interfaces and are referred to as all-in-one (AIO)-type structures. Here, the unique bonding nature renders the AIO-type structures with greatly enhanced solubility, excellent optical tunability and remarkable framework stability, all highly desirable for thin-film based optoelectronic devices. We will highlight the most recent progress in the development of this material group, including their design strategies, important properties and potential for clean-energy related applications. We will also briefly discuss the existing challenges and future outlook of these materials.

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

ACES-GNN: can graph neural network learn to explain activity cliffs?

Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.

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

Molecular concentration field design using closed-form steady-state solutions

Control over spatial concentration fields represents a fundamental challenge in designing synthetic biological systems and programmable soft materials. While nature creates morphogen gradients that orchestrate complex developmental processes, synthetic approaches have largely relied on empirical optimization and computationally intensive simulations. Here, we present an analytical framework for steady-state concentration fields generated by finite-sized localized sources in diffusion–degradation systems and derive closed-form solutions for one-, two-, and three-dimensional geometries. By expressing these solutions in dimensionless form, we show that gradient steepness and spatial structure are organized by the Thiele modulus, which captures the competition between diffusion and degradation length scales. The analysis reveals distinct design regimes: in degradation-dominated systems, gradient shape is governed by exponential decay and becomes dimension-independent, whereas in diffusion-dominated systems, gradient magnitude and extent follow dimension-dependent power-law scaling. Building on these results, we introduce a quantitative design strategy that uses threshold-based criteria to program concentration ranges by tuning physically accessible parameters, most directly the production rate, while holding transport and degradation properties fixed. Comparisons with numerical solutions and reported experimental systems demonstrate consistency with the predicted scaling behavior. Together, this work provides a generalizable and physically transparent framework for designing steady-state concentration fields in synthetic biological and soft matter systems, enabling predictive control of gradient-mediated organization without reliance on extensive numerical optimization.

Kim, Dong Woo [Johns Hopkins University, Baltimore↗
DOE OSTI2020

Studying impacts of communication system performance on dynamic stability of networked microgrid

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time‐critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter‐dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB‐D simulation environment, along with a representative multi‐traffic, multi‐channel, multi‐protocol communication system model, developed in the network simulator (ns‐3). Second, a hierarchical engine for large‐scale infrastructure co‐simulation framework is developed to co‐simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use‐cases. While the example use‐cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗