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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Abstract Tropical forests face increasing climate risk 1,2 , yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, $$\varPsi $$ Ψ 50 ) and hydraulic safety margins (for example, HSM 50 ) are important predictors of drought-induced mortality risk 3–5 , little is known about how these vary across Earth’s largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters $$\varPsi $$ Ψ 50 and HSM 50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both $$\varPsi $$ Ψ 50 and HSM 50 influence the biogeographical distribution of Amazon tree species. However, HSM 50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM 50 are gaining more biomass than are low HSM 50 forests. We propose that this may be associated with a growth–mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM 50 in the Amazon 6,7 , with strong implications for the Amazon carbon sink.

Science & Technology - Other Topics↗
DOE OSTI2024

The complex polyploid genome architecture of sugarcane

Sugarcane, the world’s most harvested crop by tonnage, has shaped global history, trade and geopolitics, and is currently responsible for 80% of sugar production worldwide. While traditional sugarcane breeding methods have effectively generated cultivars adapted to new environments and pathogens, sugar yield improvements have recently plateaued. The cessation of yield gains may be due to limited genetic diversity within breeding populations, long breeding cycles and the complexity of its genome, the latter preventing breeders from taking advantage of the recent explosion of whole-genome sequencing that has benefited many other crops. Thus, modern sugarcane hybrids are the last remaining major crop without a reference-quality genome. Here we take a major step towards advancing sugarcane biotechnology by generating a polyploid reference genome for R570, a typical modern cultivar derived from interspecific hybridization between the domesticated species (Saccharum officinarum) and the wild species (Saccharum spontaneum). In contrast to the existing single haplotype (‘monoploid’) representation of R570, our 8.7 billion base assembly contains a complete representation of unique DNA sequences across the approximately 12 chromosome copies in this polyploid genome. Using this highly contiguous genome assembly, we filled a previously unsized gap within an R570 physical genetic map to describe the likely causal genes underlying the single-copy Bru1 brown rust resistance locus. This polyploid genome assembly with fine-grain descriptions of genome architecture and molecular targets for biotechnology will help accelerate molecular and transgenic breeding and adaptation of sugarcane to future environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Giant energy storage and power density negative capacitance superlattices

Dielectric electrostatic capacitors, because of their ultrafast charge-discharge, are desirable for high-power energy storage applications. Along with ultrafast operation, on-chip integration can enable miniaturized energy storage devices for emerging autonomous microelectronics and microsystems. Moreover, state-of-the-art miniaturized electrochemical energy storage systems-microsupercapacitors and microbatteries-currently face safety, packaging, materials and microfabrication challenges preventing on-chip technological readiness, leaving an opportunity for electrostatic microcapacitors. Here, in this study, we report record-high electrostatic energy storage density (ESD) and power density, to our knowledge, in HfO 2 -ZrO 2 -based thin film microcapacitors integrated into silicon, through a three-pronged approach. First, to increase intrinsic energy storage, atomic-layer-deposited antiferroelectric HfO 2 -ZrO 2 films are engineered near a field-driven ferroelectric phase transition to exhibit amplified charge storage by the negative capacitance effect, which enhances volumetric ESD beyond the best-known back-end-of-the-line-compatible dielectrics (115 J cm -3 ). Second, to increase total energy storage, antiferroelectric superlattice engineering scales the energy storage performance beyond the conventional thickness limitations of HfO 2 -ZrO 2 -based (anti)ferroelectricity (100-nm regime). Third, to increase the storage per footprint, the superlattices are conformally integrated into three-dimensional capacitors, which boosts the areal ESD nine times and the areal power density 170 times that of the best-known electrostatic capacitors: 80 mJ cm -2 and 300 kW cm -2 , respectively. This simultaneous demonstration of ultrahigh energy density and power density overcomes the traditional capacity-speed trade-off across the electrostatic-electrochemical energy storage hierarchy. Furthermore, the integration of ultrahigh-density and ultrafast-charging thin films within a back-end-of-the-line-compatible process enables monolithic integration of on-chip microcapacitors, which can unlock substantial energy storage and power delivery performance for electronic microsystems.

25 ENERGY STORAGE↗
DOE OSTI2024

A holistic platform for accelerating sorbent-based carbon capture

Abstract Reducing carbon dioxide (CO 2 ) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO 2 from various sources and deliver it to different sinks 1,2 . The quest for optimal solutions for specific source–sink pairs is a complex, multi-objective challenge involving multiple stakeholders and depends on social, economic and regional contexts. Currently, research follows a sequential approach: chemists focus on materials design 3 and engineers on optimizing processes 4,5 , which are then operated at a scale that impacts the economy and the environment. Assessing these impacts, such as the greenhouse gas emissions over the plant’s lifetime, is typically one of the final steps 6 . Here we introduce the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which integrates materials, process design, techno-economics and life-cycle assessment. We compare more than 60 case studies capturing CO 2 from various sources in 5 global regions using different technologies. The platform simultaneously informs various stakeholders about the cost-effectiveness of technologies, process configurations and locations, reveals the molecular characteristics of the top-performing sorbents, and provides insights on environmental impacts, co-benefits and trade-offs. By uniting stakeholders at an early research stage, PrISMa accelerates carbon-capture technology development during this critical period as we aim for a net-zero world.

Science & Technology - Other Topics↗
DOE OSTI2026

Nanophotonic waveguide chip-to-world beam scanning

A seamless chip-to-world photonic interface enables broad advancements in optical ranging, display, communication, computation and quantum information science. The ideal solution enables two-dimensional scanning of a diffraction-limited beam from anywhere on a photonic integrated circuit to a large number of resolvable spots. Current beam-scanning technologies are limited by a fundamental trade-off: photonic-integrated-circuits with diffractive optics offer scalability but have poor mode quality, whereas inertially limited micromechanical scanners provide high-quality beams but lack scalable integration. Here we report a photonic ski-jump—a nanoscale waveguide monolithically integrated on a piezoelectric cantilever—to overcome these limitations. It passively curls ~90° out-of-plane within a less-than-0.1 mm 2 footprint, emits a submicrometre, broadband diffraction-limited beam, and exhibits kilohertz-rate mechanical resonances with quality factors of over 10,000. Fabricated in a volume complementary metal–oxide–semiconductor (CMOS) foundry, our device enables scalable two-dimensional beam scanning. Driven on-resonance at CMOS-level voltages, it achieves a footprint-adjusted spot rate of 68.6 mega spots s –1 mm–², exceeding state-of-the-art micro-electro-mechanical systems mirrors by more than 50-fold, which is sufficient for one million pixels at 100 Hz from an approximately 1.5 mm diameter footprint. We demonstrate full-colour image and video projection, and single-photon initialization and readout from silicon vacancy centres in diamond. Finally, by demonstrating uniformity across a 64 ski-jump array, we establish a pathway to achieving greater than one gigaspot resolution at kilohertz rates within a sub-5-cm-diameter footprint, creating a seamless optical pipeline between integrated photonic processors and the free-space world.

Displays↗
DOE OSTI2025

Traceable and Scalable Food Balance Sheets from Agricultural Commodity Supply and Utilization Accounts (2010–2022)

Abstract The Food Balance Sheets (FBS), compiled by the Food and Agriculture Organization (FAO), serve as a cornerstone dataset for studies on agricultural development, food security, and dietary health, providing a broad overview of global and regional food systems. However, its limited transparency and scalability hinder its application in empirical analysis and multisector dynamic modeling. Here, we present a traceable Food Balance Sheets (T-FBS) dataset, developed from detailed Supply Utilization Accounts (SUA) using a novel Primary Commodity equivalent (PCe) aggregation approach. This framework enables the aggregation of commodity flows along supply chains while ensuring consistency and balance across multiple dimensions. The T-FBS dataset includes 57 PCe commodities across 195 regions for the period 2010–2022, consolidated from over 500 SUA products. While T-FBS closely aligns with FAO-FBS at aggregate levels for dietary energy and macronutrients, it identifies key uncertainties in other elements (e.g., feed, trade, stocks). By enhancing methodological transparency, traceability, and scalability, T-FBS strengthens the robustness of food system studies and fosters future research and collaboration within the open-source community.

agriculture↗
DOE OSTI2019

Microbial Diversity in Bushmeat Samples Recovered from the Serengeti Ecosystem in Tanzania

Bushmeat, the meat and organs derived from wildlife species, is a common source of animal protein in the diets of those living in sub-Saharan Africa and is frequently associated with zoonotic spillover of dangerous pathogens. Given the frequent consumption of bushmeat in this region and the lack of knowledge about the microbial communities associated with this meat, the microbiome of 56 fresh and processed bushmeat samples ascertained from three districts in the Western Serengeti ecosystem in Tanzania was characterized using 16S rRNA metagenomic sequencing. Here, the results show that the most abundant phyla present in bushmeat samples include Firmicutes (67.8%), Proteobacteria (18.4%), Cyanobacteria (8.9%), and Bacteroidetes (3.1%). Regardless of wildlife species, sample condition, season, or region, the microbiome is diverse across all samples, with no significant difference in alpha or beta diversity. The findings also suggest the presence of DNA signatures of potentially dangerous zoonotic pathogens, including those from the genus Bacillus, Brucella, Coxiella, and others, in bushmeat. Together, this investigation provides a better understanding of the microbiome associated with this major food source in samples collected from the Western Serengeti in Tanzania and highlights a need for future investigations on the potential health risks associated with the harvesting, trade, and consumption of bushmeat in Sub-Saharan Africa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

2D-to-3D image translation of complex nanoporous volumes using generative networks

Image-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.

58 GEOSCIENCES↗
DOE OSTI2023

On fast simulation of dynamical system with neural vector enhanced numerical solver

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec’s simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multiscale in-situ characterization of static recrystallization using dark-field X-ray microscopy and high-resolution X-ray diffraction

Abstract Dark-field X-ray microscopy (DFXM) is a high-resolution, X-ray-based diffraction microstructure imaging technique that uses an objective lens aligned with the diffracted beam to magnify a single Bragg reflection. DFXM can be used to spatially resolve local variations in elastic strain and orientation inside embedded crystals with high spatial (~ 60 nm) and angular (~ 0.001°) resolution. However, as with many high-resolution imaging techniques, there is a trade-off between resolution and field of view, and it is often desirable to enrich DFXM observations by combining it with a larger field-of-view technique. Here, we combine DFXM with high-resolution X-ray diffraction (HR-XRD) applied to an in-situ investigation of static recrystallization in an 80% hot-compressed Mg–3.2Zn–0.1Ca wt.% (ZX30) alloy. Using HR-XRD, we track the relative grain volume of > 8000 sub-surface grains during annealing in situ. Then, at several points during the annealing process, we “zoom in” to individual grains using DFXM. This combination of HR-XRD and DFXM enables multiscale characterization, used here to study why particular grains grow to consume a large volume fraction of the annealed microstructure. This technique pairing is particularly useful for small and/or highly deformed grains that are often difficult to resolve using more standard diffraction microstructure imaging techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Innovative dead-time correction and background subtraction for neutron multiplicity measurements using neural networks

Abstract The number of neutrons emitted from a nuclear reaction plays a crucial role in various fields, including nuclear theory, nuclear nonproliferation, nuclear energy and nuclear criticality safety. Accurate determination of neutron multiplicities requires the application of several corrections, with dead-time correction and background subtraction being particularly significant. These corrections become more challenging for neutron detectors with time-dependent neutron capture. In this work, we perform a comprehensive study of three existing methods used for dead-time correction and background subtraction in neutron detectors with time-dependent neutron capture. The methods were tested for dead-times in the range from 0 to 1 μs using a Monte Carlo model simulating the dead-time and background effects in the standard neutron multiplicity probability distribution of $$^{252}$$ 252 Cf. The previous methods showed larger than desired uncertainty or systematic trade off. Those uncertainties prompted the development of a novel approach using neural networks trained with data from Monte Carlo simulations. The Neural Network method enabled the correction of neutron multiplicity probabilities more accurately than the other methods with fractional errors smaller than 3% for multiplicities around the peak of $$^{252}$$ 252 Cf. A similar approach using neural networks could be applied to problems where the system being studied can be accurately simulated without having an accurate analytical description available. The neural network method presented in this paper can be easily expanded if multiplicities greater than 10 are expected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Limits on the evolutionary rates of biological traits

Abstract This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait’s average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher’s fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species’ weights to the lengths of DNA strands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

54 ENVIRONMENTAL SCIENCES↗
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

Faster recovery of North Atlantic tropical cyclone-induced cold wakes in recent decades

Intense winds associated with tropical cyclones (TCs) generate surface ocean cooling in their wakes, which can persist for several weeks in their aftermath. While multi-decadal observations of the sea surface have shown a substantial warming of the ocean, long-term changes in cold wake recovery time remain largely unknown. Here we find a trend toward faster recovery of TC cold wakes in the Atlantic main development region (MDR) since 2001. This is due primarily to a decrease in the strength of the North Atlantic trade winds, which reduces evaporative cooling of the ocean. The faster damping of TC cold wakes has led to a significant increase in the intensification of subsequent TCs that encounter lingering wakes from prior TCs, with a magnitude that is about 9% of that from long-term warming of the ocean. Finally, earth system model simulations indicate that the observed decrease in the cold wake recovery time will likely continue into the future.

54 ENVIRONMENTAL SCIENCES↗
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

Contrasting effects of urbanization on vegetation between the Global South and Global North

Urban vegetation, the core component of green infrastructure and critical for sustainable cities, is profoundly affected by the process of urbanization. Urbanization not only leads to substantial vegetation loss (direct impact) but also fosters urban vegetation growth (indirect impact). However, the extent to which these direct and indirect impacts affect vegetation dynamics across cities worldwide and how urban greening will change in the future remain unclear. Using satellite-based greenness and impervious surface datasets, we show that positive indirect impacts mitigated 56.85% of the negative direct impacts across 4,718 cities worldwide from 2000 to 2019. Notably, the offsetting coefficient is much greater in Global North cities (79.13%) than in Global South cities (38.01%) partly due to their socioeconomic differences. This disparity in urban greening dynamics will continue in the future. Approximately 60% of Global North cities and 30% of Global South cities will become greener by 2040. Our results reveal the divergent trade-offs between vegetation loss and enhanced vegetation growth in cities of different socioeconomic levels and stages of urbanization. In conclusion, such insights are crucial for a comprehensive understanding of urban greening dynamics and for devising strategies to attain sustainable development goals.

54 ENVIRONMENTAL SCIENCES↗
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↗