Triboelectric Nanogenerator Tree for Harvesting Wind Energy and Illuminating in Subway Tunnel
Not provided.
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not provided.
Lithium sulfur (Li-S) redox flow battery (RFB) is a promising candidate for high energy large-scale energy storage application due to good solubility of long-chain polysulfide species and low cost of sulfur. In this report, recent progress and new concepts for Li-S redox flow batteries are discussed with an emphasis on the fundamental understanding and control of lithium polysulfide chemistry to enable the development of liquid phase Li-S redox flow prototype cells. These differ significantly from conventional static Li-S batteries targeting for vehicle electrification. A high solubility of the different lithium polysulfides generated at different depths of discharge and states of charge is required for a flow battery in order to take full advantage of the multiple electron transitions between elemental sulfur and Li2S. A new DMSO-based electrolyte is proposed for Li-S redox flow batteries, which not only enables the high solubility of lithium polysulfide species, especially for the short-chain species, but also results in excellent cycling with a high Coulombic efficiency. The challenges and opportunities for the Li-S redox flow concept have also been discussed in depth.
Among various commercially available energy storage devices, lithium-ion batteries (LIBs) stand out as the most compact and rapidly growing technology. This multicomponent system operates on coupled dynamics to reversibly store and release electricity. With the hierarchical electrode architectures inside LIBs, versatile functionality can be realized by design, while considerable difficulties remain to be solved to fully exploit the capability of each constituent. With the rapid electrification of the transportation sector and an urgent need to overhaul electric grids in the context of renewable energy penetration, demands for concomitant high energy and high power batteries are continuously increasing. Although building an ideal battery requires effort from multiple scientific and engineering aspects, it is imperative to gain insight into multiscale transport behaviors arising in both spatial and temporal dimensions, and enable their harmonic integration inside the whole battery system. In this progress report, recent research efforts on characterizing and understanding transport kinetics in LIBs are reviewed covering a broad range of electrode materials and length scales. Further, to demonstrate the crucial role of such information in revolutionary electrode design, examples of innovative high energy/power electrodes are provided with their unique hierarchical porous architectures highlighted. To conclude, perspectives on further approaches toward advanced thick electrode designs with fast kinetics and tailored properties are discussed.
Serious safety issues are impeding the widespread adoption of high-energy lithium-ion batteries for transportation electrification and large-scale grid storage. Herein, a triple-salt ethylene carbonate (EC) free electrolyte for high-safety and high-energy pouch-type LiNi 0.8 Mn 0.1 Co 0.1 O 2 |graphite (NMC811|Gr) cells is reported. This EC-free electrolyte can effectively stabilize the NMC811 surface under high potential (up to 4.5 V), as well as generate a stable interphase to achieve a superior compatibility with the Gr anode. The electrolyte strategy enables significantly enhanced intrinsic safety (trigger temperature of thermal runaway (TR) increased by 67.0 °C), excellent electrochemical properties (4.2V, ≈100% after 200 cycles), and superior high voltage stability (4.5 V, 82.1% after 200 cycles). Furthermore, the work opens up a new avenue for developing novel electrolyte systems to build safer high-energy batteries for practical applications.
Abstract The lithium–sulfur (Li–S) battery is a promising technology for large‐scale energy storage and vehicle electrification due to its high theoretical energy density and low cost. Reducing the sulfur cathode porosity has been identified recently as a viable strategy for improving the cell practical energy density and minimizing pore‐filling electrolytes to extend cell life at lean electrolyte conditions. Direct use of a low‐porosity cathode for Li–S battery results in poor electrode wetting, nonuniform electrode reactions, and thus early cell failure. To understand and mitigate the barriers associated with the use of low‐porosity electrodes, multiscale modeling is performed to predict electrode wetting, electrolyte diffusion, and their impacts on sulfur reactions in Li–S cells by explicitly considering the electrode wettability impacts and electrode morphologies. The study elucidates the critical impact of low tortuosity and large channel pore design for promoting electrode wetting and species diffusion. It is suggested that the secondary particle size should be comparable with the electrode thickness to effectively promote electrolyte wettability and sulfur reactivity. This study provides new insights into the low‐porosity electrode material and designs and is expected to accelerate the development of practical high‐energy Li–S batteries.
The rapid electrification of aerial transportation is driving the need for high‐performance Li‐ion batteries that can operate reliably under stringent thermal and safety constraints. The unique mission profile of electric Vertical Take‐off and Landing (eVTOL) aircraft necessitates a focused investigation into the thermal behavior and safety characteristics of these batteries. Here, in this study, operando isothermal microcalorimetry is employed to examine the thermal evolution of Li‐ion batteries under cycling conditions representative of eVTOL operations. These findings reveal that high‐power discharge events—such as those during take‐off and landing—shift the thermal response toward exothermic behavior, in contrast to the typically endothermic response expected under near‐equilibrium cycling conditions. Additionally, the results suggest that advanced electrolyte formulations may help suppress excess heat generation, thereby improving battery safety. Notably, the calorimetric results exhibit a distinct thermal signature associated with lithium plating, offering a potential diagnostic for detecting Li plating during eVTOL operation. Overall, this study demonstrates the utility of isothermal microcalorimetry as a valuable tool for assessing thermal risks in Li‐ion batteries for eVTOL applications, and highlights the importance of targeted design strategies to mitigate safety hazards during high‐power demand scenarios.
Electrification of chemical production using renewable energy and abundant feedstocks offers a promising pathway for decarbonizing the chemical industry. Current efforts on CO 2 valorization largely focus on making chemicals and fuels. Here, to help achieve net-negative emissions through long-term carbon storage, this study aims to develop efficient electrocatalysts for a tandem electrochemical-thermochemical process to convert CO 2 into carbon nanofibers (CNFs). CO 2 and water are first electrochemically reduced in a membrane electrode assembly (MEA) electrolyzer to produce syngas (CO + H 2 ), which is subsequently fed into a thermochemical packed bed reactor to facilitate CNF growth. This work systematically evaluated Pd x Cu 1-x bimetallic electrocatalysts to assess the effect of Pd–Cu alloying on enhancing syngas production while reducing Pd loading. Transmission electron microscopy and Raman spectroscopy confirmed the formation of high-purity, crystalline CNFs, regardless of the syngas composition from the MEA. In situ X-ray absorption spectroscopy and X-ray diffraction measurements revealed that increasing Cu content in the Pd x Cu 1-x alloy progressively inhibited palladium hydride formation, consistent with DFT calculations on the stability of Pd x Cu 1-x under reducing electrochemical potentials.
Chemical manufacturing is a large and diverse sector of the U.S. economy, with products, fuels, and a wide assortment of materials used daily by both the public and businesses. Currently, several of the largest volume chemicals produced in the United States rely on fossil fuels as a feedstock, energy source, or both. The list of chemicals includes steam cracking products such as ethylene, propylene, benzene, and xylenes as well as products such as ammonia and methanol. The focus for this work is on platform chemicals that are both produced in the largest volume and have a high potential for subsequent processing into more specialized products. In this study, we explore several new pathways that reduce the overall energy consumption and greenhouse gas (GHG) emissions for each product. These pathways include energy efficiency measures applied to existing production methods, the use of bio‐based fuels and/or feedstocks as new production methods, and electrification of high‐energy‐input stages within current production methods. Scenarios for energy demand and GHG reduction were conducted with the National Renewable Energy Laboratory's Materials Flows through Industry tool. Projections of the energy demand and GHG emissions in 2030 and 2050 are included, using grid composition projections from the NREL ReEDS model. The alternative scenarios selected showcase the effect of realistic changes the industry could make, focusing on technologies with a high level of technical readiness.
The outcome of contact electrification between dielectrics depends not only on the transfer of charge but also on the transfer of material (see picture). Although only minute quantities of materials are being exchanged during contact, they can reverse the polarity of dielectrics. The reported results corroborate the mosaic model and suggest that the observations are because of the mechanical softness/hardness of the materials.
Abstract C−H bond activation is the first step in manufacturing chemical products from readily available light alkane feedstock and typically proceeds via carbon‐intensive thermal processes. The ongoing emphasis on decarbonization via electrification motivates low‐temperature electrochemical alternatives that could lead to sustainable chemicals production. Platinum (Pt) electrocatalysts have shown activity towards reacting alkanes; however, little is known about propane electrocatalytic activation and conditions suitable for enabling selective oxidation to valuable products. Herein, we utilize a combination of electrochemical mass spectrometry (ECMS) and density functional theory (DFT) calculations to elucidate the potential dependence of propane activation on Pt electrocatalysts. Results show a strong dependence of adsorption on the applied potential in room‐temperature aqueous acidic electrolyte, with a maximum coverage of propane‐derived adsorbates at 0.30 V vs RHE. Using charge deconvolution and deuterated experiments, the mechanism of adsorption was elucidated, and C 3 H 2 * was determined as the average dehydrogenated propane‐derived adsorbate species. DFT calculations further corroborate these results, showing that the formation of deeply dehydrogenated species is energetically accessible at room temperature. The combined theoretical and experimental findings yield insights for selective activation of paraffinic C−H bonds at room temperature, aqueous conditions—a critical step towards decarbonized chemical manufacturing.
Abstract The demand for electrochemical energy storage is increasing rapidly due to a combination of decreasing costs in renewable electricity, governmental policies promoting electrification, and a desire by the public to decrease CO 2 emissions. Lithium‐ion batteries are the leading form of electrochemical energy storage for electric vehicles and the electrical grid. Lithium‐ion cell anodes are mostly made of graphite, which is derived from geographically constrained, non‐renewable resources using energy‐intensive and highly polluting processes. Thus, there is a desire to innovate technologies that utilize abundant, affordable, and renewable carbonaceous materials for the sustainable production of graphite anodes under relatively mild process conditions. This review highlights novel attempts to realize the aforementioned benefits through innovative technologies that convert biocarbon resources, including lignocellulose, into high quality graphite for use in lithium‐ion anodes.
Lithium metal batteries (LMBs) enable much higher energy density than lithium–ion batteries (LIBs) and thus hold great promise for future transportation electrification. However, the adoption of lithium metal (Li) as an anode poses serious concerns about cell safety and performance, which has been hindering LMBs from commercialization. To this end, extensive effort has been invested in understanding the underlying mechanisms theoretically and experimentally and developing technical solutions. In this review, we devote to providing a comprehensive review of the challenges, characterizations, and interfacial engineering of Li anodes in both liquid and solid LMBs. Finally, we expect that this work will stimulate new efforts and help peer researchers find new solutions for the commercialization of LMBs.
Electric vehicles are pivotal in the global shift toward decarbonizing road transport, with lithium-ion batteries at the heart of this technological evolution. However, the pursuit of batteries capable of extremely fast charging that also satisfy high energy and safety criteria, poses a significant challenge to current lithium-ion batteries technologies. Additionally, the increasing demand for aluminum (Al) and copper (Cu) in electrification, solar energy technologies, and vehicle light-eighting is driving these metals toward near-critical status in the medium term. This study introduces metalized polythylene terephthalate (mPET) polymer films by depositing an Al or Cu thin layer onto two sides of a polyethylene terephthalate film—named mPET/Al and mPET/Cu, as lightweight, cost-effective alternatives to traditional metal current collectors in lithium-ion batteries. We have fabricated current collectors that significantly reduce weight (by 73%), thickness (by 33%), and cost (by 85%) compared with traditional metal foil counterparts. These advancements have the potential to enhance energy density to 280 Wh kg -1 at the electrode level under 10-min charging at 6 C. Through testing, including a novel extremely fast charging protocol across various C-rates and long-term cycling (up to 1000 cycles) in different cell configurations, the superior performance of these metalized polymer films has been demonstrated. Notably, mPET/Cu and mPET/Al films exhibited comparable capacities to conventional cells under extremely fast charging, with the mPET cells showing a 27% improvement in energy density at 6 C and maintaining significant energy density after 1000 cycles. This study underscores the potential of mPET films to revolutionize the roll-to-roll battery manufacturing process and significantly advance the performance metrics of lithium-ion batteries in electric vehicles applications.
The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves - potentially at the cost of suboptimal economic conditions - in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.
This article presents the synthesis of results from the Stanford Energy Modeling Forum Study 27, an inter-comparison of 19 energy-economy and integrated assessment models. The study investigated the value of individual mitigation technologies such as energy intensity improvements, carbon capture and sequestration (CCS), nuclear power, solar and wind power and bioenergy for climate mitigation. Achieving atmospheric greenhouse gas concentration targets at 450 and 550 ppm CO2 equivalent requires massive greenhouse gas emissions reductions. A fragmented policy approach at the level of current ambition is inconsistent with these targets. The availability of a negative emissions technology, in most models biofuels with CCS, proved to be a key element for achieving the climate targets. Robust characteristics of the transformation of the energy system are increased energy intensity improvements and the electrification of energy end use coupled with a fast decarbonization of the electricity sector. Non-electric energy end use is hardest to decarbonize, particularly in the transport sector. Technology is a key element of climate mitigation. Versatile technologies such as CCS and bioenergy have largest value, due in part to their combined ability to produce negative emissions. The individual value of low-carbon power technologies is more limited due to the many alternatives in the sector. The scale of the energy transformation is larger for the 450 ppm than for the 550 ppm CO2e target. As a result, the achievability and the costs of the 450 ppm target are more sensitive to variations in technology variability. Mitigation costs roughly double when moving from 550 ppm to 450 ppm CO2e, but remain below 3% of GDP for most models.
Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.
The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.
The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.