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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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This fact sheet provides an overview of the National Renewable Energy Laboratory (NREL) State, Local, and Tribal Program. The State, Local, and Tribal Program partners with communities of all sizes to support the development of efficient, affordable, equitable, and resilient energy systems.
The Communities Local Energy Action Program (LEAP) pilot overview fact sheet provides background on the opportunities and benefits of the program, the various pathways that communities are pursuing, and the technical assistance provided.
The U.S. Department of Energy's (DOE) Communities LEAP (Local Energy Action Program) partners with low income, energy-burdened communities that are disadvantaged and/or experience direct economic impacts from reducing their historical reliance on fossil fuels to build community-driven action plans for clean energy-related economic development.
In our previous papers we described the mathematical formalism and the computed results for energy-balance hydrostatic models of the solar transition region. In this paper we discuss in some detail the limitations of the hydrostatic and one-dimensional assumptions used. Then we analyze the determination of helium emission when diffusion is included. We use transport coefficients estimated from kinetic theory to determine the helium departures from local ionization balance. We calculate the helium spectra for each of our models and evaluate the role of helium in the energy transport. Also, we investigate the effects of coronal illumination on the structure of the transition region and upper chromosphere, and show how coronal illumination affects various EUV lines and the He I 10830 A line. Comparing with both absolute intensities and detailed line profiles, we show that our models are consistent not only with the observed hydrogen spectra but also with the available helium spectra.
High-temperature thermochemical energy storage materials using doped magnesium-transition metal spinel oxides are provided. —transition metal spinel oxides, such as magnesium manganese oxide (MgMn) 3 O 4 , are promising candidates for high-temperature thermochemical energy storage applications. However, the use of these materials has been constrained by the limited extent of their endothermic reaction. Embodiments described herein provide for doping magnesium-transition metal spinel oxides to produce a material of low material costs and with high energy densities, creating an avenue for plausibly sized modules with high energy storing capacities.
Abstract The present work presents results from an investigation of the glass transition and crystallization behaviors of HNAB tested over more than five orders of magnitude of cooling rate from 0.005 °C/s to 600 °C/s (0.3 to 36000 °C/min) by a combination of conventional and Flash differential scanning calorimetry (DSC). The work quantifies the influence of the thermal amorphization route on the properties of this high explosive. Cooling rates faster than 100 °C/s (6000 °C/min) result in amorphous HNAB as expected from prior work, but we also find that amorphization of the HNAB occurs at cooling rates slower than 0.008 °C/s (0.5 °C/min). The behavior of the amorphous HNAB made by slow cooling is compared with that of amorphous HNAB made by fast cooling, as well as with that made by solvent casting in terms of glass transition temperature, apparent activation energy of glass transition, and dynamic fragility parameter m . Besides, the non‐isothermal crystallization response as a function of cooling rate is also reported. The thermal stability and decomposition energy of amorphous HNAB are compared with those of the crystalline counterpart, being similar heats of decomposition of 3295 and 3392 J/g, respectively; suggesting that the amorphous HNAB will have similar thermal stability and chemical energy to the crystalline form.
We have calculated energy levels and oscillator strengths for dipole-allowed transitions between the terms belonging to the 3s(sup 2)3p(sup 2), 3s3p(sup 3), 3S(sup 2)3p3d, 3S(sup 2)3p4s, 3S(sup 2)3p4p and 3s(sup 2)3p4d configurations of S iii in the LS-coupling scheme. We used flexible radial functions and included a large number of configurations in the configuration-interaction expansions to ensure convergence. The calculated energy levels are in close agreement with the recent laboratory measurement. The present oscillator strengths are compared with other calculations and experiments and most of the existing discrepancies between the available calculations are resolved.
Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.
We report achieving sustainable decarbonization of the energy sector requires implementing and improving energy technologies while simultaneously managing sources of social inequity in the energy system. Centering energy justice, which has "the goal of achieving equity in both the social and economic participation in the energy system, while also remediating social, economic, and health burdens on those historically harmed by the energy system," in the transition to clean energy has become an increasingly urgent priority for social scientists, policymakers, and community activists alike. However, late-stage consideration of social impacts of energy technologies may result in identifying inequities only after substantial time, money, and effort have been expended on research and development (R&D). This issue is exemplified by concerns over environmental and human health impacts related to cobalt in lithium-ion batteries, which has spurred research into alternatives only after decades of R&D and the establishment of supply chains, infrastructure, and markets for cobalt-containing chemistries. Other examples include issues with land use and resource consumption related to first-generation biofuel feedstocks as well as occupational hazards and pollution associated with photovoltaics manufacturing. In all these cases, subsequent R&D to improve technologies or processes cannot undo the effects already experienced. Incorporating energy justice from the earliest stage of R&D will enable more just technology implementation, but integrating justice considerations into early-stage research is a challenge due to a lack of tools to assess and manage them. To fill this gap, we center early-stage research to develop the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - energy justice metrics specifically targeted at early-stage researchers to assess their work on an immediate timescale. By applying these metrics to a case study focused on materials for next-generation photovoltaics, we highlight potential benefits and barriers to implementing this framework in early-stage research and discuss necessary institutional and individual actions needed for researchers to effectively leverage the tool to incorporate justice-focused criteria into R&D decision making.
The state of Washington must double its clean electricity supply by 2050 to meet its clean energy goals and comply with the Clean Energy Transformation Act. With more than 6.6 GW of technical resource potential in federal waters where Bureau of Ocean Energy Management has leasing authority, offshore wind energy could play an important role in diversifying Washington State's clean energy mix, reducing dependence on out-of-state energy sources, and helping meet state decarbonization goals. Decision makers need technology-specific information to assist with long-term energy system planning, so the Bureau of Ocean Energy Management requested that the National Renewable Energy Laboratory provide an overview of several drivers of offshore wind energy feasibility and cost in Washington. This study summarizes some of the existing engagement efforts and perspectives on offshore wind energy in the region and quantifies the offshore wind resources in Washington as well as technology costs and performance of potential projects. Furthermore, this report reviews existing grid and port infrastructure and discusses infrastructure needs along with information gaps. This study also explores opportunities and barriers to Washington entities supporting the broader floating offshore wind energy supply chain along the U.S. West Coast. Note that this study is not part of a formal project planning process or official engagement effort, nor does it assess environmental or economic impacts from potential offshore wind energy development.
Because of the growing concerns regarding climate change and energy sustainability, a transition toward a modern energy sector that reduces environmental effects while promoting social and economic growth has gained traction in recent years. Sustainable energy solutions, which include renewable and low-carbon sources such as nuclear energy and natural gas, could minimize emissions of greenhouse gases, enhance air and water quality, and encourage energy independence. Yet, the shift to a sustainable energy industry is fraught with difficulties, including governmental and regulatory obstacles, technological and economic limits, and societal acceptability hurdles. Addressing these issues would necessitate the development of long-term, durable, and cost-effective energy systems containing nuclear energy and associated with the generation of both electricity and other by-products required by industry. Integrated energy systems (IES) are a novel way to maximize the use of various energy resources and technologies in order to deliver dependable, efficient, and sustainable energy services. IES entail the integration of various energy systems, such as electricity, heating, cooling, and transportation, in respect to energy sustainability and a system’s resilience and flexibility. Their development and implementation require the cooperation of several parties, including energy providers and policymakers. This study provides a state-of-the-art literature review of the most creative nuclear-driven hybrid energy system applications and methodologies, from which the research challenges and prospects for effective IES implementation emerge.
This document summarizes how grid operators can address gaps in their planning and operations to maintain reliability as they pursue clean energy goals. When transitioning to higher renewable energy levels, many system operators configure a dedicated renewable energy desk to manage variable renewable energy resource operation. Establishing such a desk in the control room can be a key step in the modernization effort. A renewable energy desk in a control room is a specialized hub focused solely on monitoring, predicting, and managing the influx of energy from renewable sources.
This study examines the spatial and temporal impacts of the U.S. clean energy grid transition on related greenhouse gas (GHG) emissions from the wastewater treatment industry. By analyzing data from 17,156 water resource recovery facilities (WRRFs) and state-specific grid decarbonization scenarios, the results project a 60% reduction in Scope 2 emissions by 2050, driven by the national shift to renewable energy. However, regional disparities are prominent, with northeastern and western states achieving the most significant reductions, while the Ohio Valley and Rockies are likely to experience higher emissions due to the reliance on fossil fuels. This study offers the first assessment of clean grid impacts on the WRRFs and highlights the need for targeted, region-specific strategies across different emission scopes. This research provides insights for policymakers and stakeholders in the wastewater sector, emphasizing the critical role of grid decarbonization in achieving GHG reduction goals.
Here, excited states of 89 Zr and 91 Mo have been investigated using the fusion reaction 6 Li+ 89 Y at beam energy of 34 MeV. Based on new experimental data, about 16 (13) new transitions have been added to the level scheme of 89 Zr ( 91 Mo). The level structures are investigated in the framework of the spherical-basis shell model using the effective interactions JUN45 and GWBXG. Reasonable agreement is obtained between the experimental results and the calculations. The calculated results indicate the newly proposed states are characterized by proton excitations from the 1f 5/2 , 2p 3/2 , 2p 1/2 orbits to the high-j 1g 9/2 orbit. The systematic features of low lying states in the N = 49 isotones are discussed.
Crystalline metal-organic frameworks (MOFs) can assemble chromophoric molecules into a wide range of spatial arrangements, which are controlled by the MOF topology. Like natural light-harvesting complexes (LHCs), the precise arrangement modulates interchromophoric interactions, in turn determining excitonic behavior and migration dynamics. To unveil the key factors that control efficient exciton displacements within MOFs, we first developed linkers with low electronic symmetry (as defined by large transition dipoles) and then assembled them into MOFs. These linkers possess extended conjugation along one molecular axis, engendering low optical bandgaps and improved oscillator strength for their lowest-energy transition (S 0 → S 1 ). This enhances absorption-emission spectral overlap and boosts the efficiency of Forster resonance energy transfer, which was observed experimentally by a sizable decrease in emission quantum yield (QY), accompanied by a faster population decay profile. We find that MOFs that orient these elongated linkers along their asymmetric pore channel, e.g., the hexagonal pores in an xly network, manifested >50% decrease in their emission QY with faster decay profiles relative to their corresponding solution dissolved linkers. This is due to an efficient migration of photogenerated excitons at the crystallite peripheral sites to internal sites, which was facilitated by polarized absorption-emission overlap among the parallelly aligned linkers. In contrast, symmetric MOFs, such as those with sqc-a topological net, orient elongated linkers along two perpendicular crystal axes, which hinders efficient exciton migration. This study underscores that MOFs are promising to develop artificial LHCs, but that to achieve an efficient exciton displacement, appropriate topology-guided assembly is required to fully realize the true potential of linkers with low electronic symmetry.
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.
We report electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system.