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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mechanistic insights into CO 2 capture and electrochemical conversion in nonaqueous Na–CO 2 batteries

Developing efficient energy storage systems that capture and convert CO 2 is critical for mitigating carbon emissions. Here, we report a Na–CO 2 battery with ruthenium dioxide (RuO 2 ) cathode catalysts and propane-1,3-diamine (PDA) as an electrolyte additive to enhance CO 2 capture and conversion efficiency. The integration of CO 2 adsorption and electrochemical reduction facilitates activation of the inert CO 2 molecule and circumvents gas–solid–liquid ternary-phase reactions at the interface. We employed density functional theory (DFT) calculations to systematically unravel the reaction mechanisms and energetics governing CO 2 reduction, both with and without PDA. Our results reveal an energetically favorable pathway toward the formation of Na 2 CO 3 and C as final discharge products, rather than sodium oxalate (Na 2 C 2 O 4 ). The CO 2 –amine adduct facilitates charge transfer from PDA to CO 2 , which results in activation of CO 2 . The kinetics of CO 2 conversion and regeneration of PDA were found to be significantly enhanced on the RuO 2 surface compared to the bulk electrolyte. More importantly, pre-activation of CO 2 via the amine–CO 2 adduct lowers the total overpotential to 2.44 V, compared to 3.13 V without PDA. This study provides fundamental insights into CO 2 electroreduction in Na–CO 2 batteries and underscores the promise of electrolyte engineering for sustainable CO 2 utilization and high-performance energy storage.

25 ENERGY STORAGE↗
DOE OSTI2023

A multifunctional copper single-atom electrocatalyst aerogel for smart sensing and producing ammonia from nitrate

Despite modern chemistry’s success in providing affordable fertilizers for feeding the population and supporting the ammonia industry, ineffective nitrogen management has led to pollution of water resources and air, contributing to climate change. Here, we report a multifunctional copper single-atom electrocatalyst-based aerogel (Cu SAA) that integrates the multiscale structure of coordinated single-atomic sites and 3D channel frameworks. The Cu SAA demonstrates an impressive faradaic efficiency of 87% for NH 3 synthesis, as well as remarkable sensing performance with detection limits of 0.15 ppm for NO 3 − and 1.19 ppm for NH 4 + . These multifunctional features enable precise control and conversion of nitrate to ammonia in the catalytic process, facilitating accurate regulation of the ammonium and nitrate ratios in fertilizers. We thus designed the Cu SAA into a smart and sustainable fertilizing system (SSFS), a prototype device for on-site automatic recycling of nutrients with precisely controlled nitrate/ammonium concentrations. The SSFS represents a forward step toward sustainable nutrient/waste recycling, thus permitting efficient nitrogen utilization of crops and mitigating pollutant emissions. This contribution exemplifies how electrocatalysis and nanotechnology can be potentially leveraged to enable sustainable agriculture.

Science & Technology - Other Topics↗
DOE OSTI2024

Propelling sustainable energy: Multi-omics analysis of pennycress FATTY ACID ELONGATION1 knockout for biofuel production

Abstract The aviation industry’s growing interest in renewable jet fuel has encouraged the exploration of alternative oilseed crops. Replacing traditional fossil fuels with a sustainable, domestically sourced crop can substantially reduce carbon emissions, thus mitigating global climate instability. Pennycress (Thlaspi arvense L.) is an emerging oilseed intermediate crop that can be grown during the offseason between maize (Zea mays) and soybean (Glycine max) to produce renewable biofuel. Pennycress is being domesticated through breeding and mutagenesis, providing opportunities for trait enhancement. Here, we employed metabolic engineering strategies to improve seed oil composition and bolster the plant's economic competitiveness. FATTY ACID ELONGATION1 (FAE1) was targeted using CRISPR-Cas 9 gene editing to eliminate very long chain fatty acids (VLCFAs) from pennycress seed oil, thereby enhancing its cold flow properties. Through an integrated multiomics approach, we investigated the impact of eliminating VLCFAs in developing and mature plant embryos. Our findings revealed improved cold-germination efficiency in fae1, with seedling emergence occurring up to 3 d earlier at 10 °C. However, these alterations led to a tradeoff between storage oil content and composition. Additionally, these shifts in lipid biosynthesis were accompanied by broad metabolic changes, such as the accumulation of glucose and ADP-glucose quantities consistent with increased starch production. Furthermore, shifts to shorter FA chains triggered the upregulation of heat shock proteins, underscoring the importance of VLCFAs in stress signaling pathways. Overall, this research provides crucial insights for optimizing pennycress seed oil while preserving essential traits for biofuel applications.

Rasoul, Amira (ORCID:0009000714290175)↗
DOE OSTI2025

Climate-induced shifts in sulfate dynamics regulate anaerobic methane oxidation in a coastal wetland

Anaerobic methane oxidation (AMO) is a key microbial pathway that mitigates methane emissions in coastal wetlands, but the response of AMO to changing global climate remains poorly understood. Here, we assessed the response of AMO to climate change in a brackish coastal wetland using a 5-year field manipulation of warming and elevated carbon dioxide ( e CO 2 ). Sulfate (SO 4 2− )–dependent AMO (S-DAMO) was the predominant AMO process at our study site due to tidal inputs of SO 4 2− . However, SO 4 2− dynamics responded differently to the treatments; warming reduced SO 4 2− concentration by enhancing SO 4 2− reduction, while eCO 2 increased SO 4 2− concentration by enhancing SO 4 2− regeneration. S-DAMO rates mirrored these trends, with warming decreasing S-DAMO rates and e CO 2 stimulating them. These findings underscore the potential of climate change to alter soil AMO activities through changing SO 4 2− dynamics, highlighting the need to incorporate these processes in predictive models for more accurate representations of coastal wetland methane dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Prospects for silvicultural enhancement of fire resistance in mesic westside forests of the Pacific Northwest

Increasing wildfire activity in mesic, temperate Pacific Northwest forests west of the Cascade Range crest has stimulated interest in understanding whether alternative forest management practices could reduce risk of stand-replacing fire. To explore how management can enhance fire resistance in these forests and assess tradeoffs among resistance enhancement, carbon sequestration and storage, and economic returns, we conducted 40-year simulations of stand development with BioSum, a framework for conducting landscape analysis with the Forest Vegetation Simulator (FVS), utilizing a statistically representative and spatially balanced sample of Forest Inventory and Analysis (FIA) plots. Simulation outcomes under business-as-usual silviculture were contrasted with fire-aware silviculture, and treatment optimization logic was developed and applied to represent landscape-scale outcomes under business-as-usual and fire-focused management scenarios. Simulation results indicate that fire-aware prescriptions and fire-focused management can meaningfully enhance stand- and landscape-scale fire resistance of westside forests under less than extreme fire weather, but at the cost of lower economic returns and reduced net carbon storage and sequestration over the 40-year analysis window. Shifting from business-as-usual regeneration harvests with short rotations to fire-aware, episodic selection harvest improved fire resistance the most, especially in young privately-owned forests, and with only modest tradeoffs in carbon and economic outcomes. While fire-aware treatments generally reduced net present value from forest operations over business-as-usual, most treatments still generated positive net present value and could be implemented without subsidy. Fire-aware prescriptions that removed and utilized non-merchantable harvest residues instead of burning them, via either pile or broadcast burning, partially mitigated carbon emissions associated with fire-aware treatments, with about the same improvement in fire resistance. Given the currently limited institutional and financial capacity to implement fire resistance enhancing treatments at scale, the insights from this analysis may aid managers seeking to elevate fire resistance to prioritize where and how to manage.

Science & Technology - Other Topics↗
DOE OSTI2022

Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during Geological Carbon Storage

Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.

Engineering↗
DOE OSTI2021

Using AI to build a hydrobiogeochemical soil model

Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Investigating the Impacts of Changes in Land Cover and Land Management on Climate Using ACME

The overall objective of our ACME project is to advance the treatment of land disturbance, particularly land use and land cover changes (LULCCs) and land management practices, and couple it with ALM to fully explore the potential contribution of LULCC and land management practices to future emissions and mitigation opportunities, and terrestrial carbon sources and sinks, and climate change. To achieve this objective, we have incorporated the advances made by DOE in IAM and ESM modeling efforts and our research at UIUC in global land disturbance, carbon management, and socioeconomic research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Accelerating Informed Local Climate Action by Improving Access to DOE Predictive Modeling and Science-Based Tools

The threat of climate change is becoming increasingly urgent, and local governments across the United States are working to develop and implement climate action plans to reduce greenhouse gas emissions and mitigate the impacts of climate change. At the same time, the U.S. Department of Energy's Office of Science, Earth and Environmental Systems Sciences Division (EESSD) has been developing a range of tools and technologies to help predict the potential multi-faceted impacts from climate change given varying environmental and socioeconomic scenarios. However, local governments have been slow to engage with these scientific assets due to a lack of understanding and awareness. The DOE researchers interviewed highlighted three primary types of resources that could benefit local governments and climate practitioners: climate models, modeled datasets, and scientific journal articles. Although many of these resources are open source and available to the public, there were several challenges identified in translating these resources and their research insights directly to decision-makers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Optimization of Membrane-based Carbon Capture using Dimensional Analysis, CFD and Process System Engineering

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibil-ity and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability.To upscale the technology into stacked flat sheet configurations, high fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High fidelity simulations have been shown to be effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity requires a multi-stage membrane process, where the optimal network configuration of the membrane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules. Numerical results indicate that the optimal design includes three membrane stages, and the capture cost is 45.4 $/t-CO2.

Pedrozo, Hector A.↗
DOE OSTI2024

Membrane-based Carbon Capture Process Optimization using CFD Modeling

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibility and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability. To upscale the technology into stacked flat sheet configurations, high fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High fidelity simulations have been shown to be effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity re-quires a multi-stage membrane process, where the optimal network configuration of the mem-brane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules.

Pedrozo, Hector A.↗
DOE OSTI2024

Optimizing Carbon Capture, Transport, and Storage: Overcoming Challenges with Machine Learning and Cost-Benefit Analysis

Carbon Capture, Utilization, and Storage (CCUS) is a critical strategy for reducing CO₂ emissions and mitigating climate change. However, its widespread deployment faces numerous challenges across the capture, transport, and storage phases. These challenges include the technical complexity of predicting subsurface behaviors during CO₂ injection, ensuring long-term storage integrity, optimizing transportation networks, and balancing the economic and environmental trade-offs. Addressing these issues requires an integrated approach combining advanced subsurface modeling with system-level analyses to assess costs, risks, and benefits. This presentation provides an overview of studies conducted by the National Energy Technology Laboratory (NETL) to tackle these challenges. NETL’s efforts encompass cutting-edge research in subsurface fluid behavior machine learning predictions, alongside the development of innovative tools for system optimization and economic evaluation. By bridging technical expertise and strategic analysis, NETL aims to advance the deployment of CCUS technologies to support global decarbonization efforts. Presented at the Carnegie Mellon University CEE IESS Student Seminar October 4, 2024.

Shih, Chung Yan↗
DOE OSTI2024

Co-gasification of Plastic, Coal Waste and Biomass for Hydrogen Rich Syngas Production

Plastic waste has increasingly become one of the most pressing environmental issues. To mitigate plastic emissions, extraordinary efforts are needed for plastic waste recycling and management. This study investigates the co-gasification of plastic, coal refuse, and biomass into hydrogen-rich syngas through steam gasification. The study focuses on the correlations between process parameters and feedstock composition. Coal refuse and biomass are used as co-feedstocks for gasification, aiming to improve the handling of plastic waste and investigating their synergistic effects on product distribution.

Bashir, Muhammad↗
DOE OSTI2025

SF6 / Acetone Separation & Purification

Sulfur hexafluoride (SF₆) is recognized as the most potent greenhouse gas used in the power transmission and semiconductor sectors. In the past decade, its global emissions have increased sharply, largely due to the absence of effective disposal and recovery pathways. This concern was formally addressed under the Kyoto Protocol, which urged participating nations to adopt measures to limit SF₆ emissions and mitigate its role in climate change. In recent years, various abatement strategies have been explored, including non-thermal plasma (NTP) technologies such as radio frequency, microwave, dielectric barrier discharge, and electron beam systems. While these methods can decompose SF₆, they also produce hazardous by-products like sulfur oxyfluorides, sulfur dioxide, hydrofluoric acid, and fluorine gas. Because these compounds pose both environmental and health risks, replacing traditional disposal practices with modern, more effective treatment methods has become an urgent priority.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Abstract for CRADA between NETL and Hope Gas, Inc.

The National Energy Technology Laboratory (NETL) and Hope Gas (PARTICIPANT) will collaborate in field demonstrations of NETL-developed methane quantification and mitigation technologies, including pipeline sensors and pipeline protection materials to mitigate methane emissions. Hope Gas will also provide access to gathering pipelines and other facilities for an NETL team to perform methane emission field survey.

03 NATURAL GAS↗
DOE OSTI2026

Methane Mitigation Using Linear Motor Leak Recovery Compressor

GTI Energy (GTI), in collaboration with The University of Texas at Austin - Center for Electromechanics (UT‑CEM), designed, fabricated, and tested a linear motor–driven compressor intended to mitigate methane emissions at natural gas facilities across transmission, storage, gathering, and processing sectors of the gas value chain.

02 PETROLEUM↗
DOE OSTI2022

Counterfactuals to Assess Effects to Species and Systems from Renewable Energy Development

Renewable energy production, mostly via wind, solar, and biofuels, is central to goals worldwide to reduce carbon emissions and mitigate anthropogenic climate change (IPCC, 2014; Portner et al., 2021). Nevertheless, adverse impacts to natural systems, especially fatalities of wildlife and alteration of habitat, are key challenges for renewable energy production (Allison et al., 2019; Katzner et al., 2019).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1992

A Segmented Ion-Propulsion Engine

New design approach for high-power (100-kW class or greater) ion engines conceptually divides single engine into combination of smaller discharge chambers integrated to operate as single large engine. Analogous to multicylinder automobile engine, benefits include reduction in required accelerator system span-to-gap ratio for large-area engines, reduction in required hollow-cathode emission current, mitigation of plasma-uniformity problem, increased tolerance to accelerator system faults, and reduction in vacuum-system pumping speed.

Brophy, John R.↗