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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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The Energy 101: Wind presentation, developed for the Energy Technology Innovation Partnership Project (ETIPP), provides an overview of wind energy. It covers fundamental concepts, technologies, considerations, case studies, and additional resources.
Disordered rocksalt cathodes hold promise for achieving high-capacity lithium-ion batteries while using low-cost, earth-abundant elements. However, their electrochemical performance remains critically limited by their poor electronic conductivity. Conventional strategies such as high-energy ball milling with excess carbon additives can improve conductivity but remain challenging to scale and often produce defects and increase surface area, thereby accelerating capacity degradation. Herein, we report an alternative approach of electrostatic-attraction-driven self-assembly to fabricate Li 1.2 Mn 0.6 Ti 0.2 O 1.8 F 0.2 (LMTOF) particles uniformly wrapped with electronically conductive graphene sheets without associated materials degradation. The graphene-wrapped LMTOF demonstrates significantly improved cycling stability (89% capacity retention after 100 cycles) and superior rate capability compared with an LMTOF-carbon composite electrode fabricated using the conventional high-energy ball-milling process. Post-cycling analysis reveals reduced oxygen evolution, suppressed unwanted side reactions, and improved structural integrity for the graphene-LMTOF composite. This work highlights the advantages of solution-based carbon wrapping and offers a scalable strategy to prepare high-performance DRX cathodes for lithium-ion batteries.
Power Purchase Agreements (PPAs) are becoming increasingly preferred among large energy consumers, such as data centers, to secure cost-effective energy and meet accelerating demand growth. Traditionally, variable renewable energy (VRE)-based PPAs operate on a pay-as-produced basis, balancing supply and demand for a relatively longer duration (e.g., annually). However, the focus is shifting toward matching supply and demand on an hourly basis to fully meet energy needs. This shift requires the integration of flexible energy resources, such as hydropower, thermal generation, and energy storage, to complement VRE sources like wind and solar, forming the foundation for 24/7 PPA. This work contributes by: (i) reviewing emerging market trends and current practices in PPA procurement, supported by data on PPA prices and technology portfolios; (ii) synthesizing the existing literature on modeling approaches for contract pricing, quantities, hybrid resource procurement, and risk management in 24/7 PPA design, while identifying key research gaps; and (iii) proposing an integrated 24/7 PPA design framework along with two contracting mechanisms from the perspectives of both PPA providers and consumers. The proposed framework highlights critical modeling challenges, risk-allocation issues, and future research opportunities for 24/7 PPA design.
Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.
The development and application of new organoboron reagents as Lewis acids in synthesis and metal-free catalysis have dramatically expanded over the past 20 years. In this context, we will show the recent uses of the simple and relatively weak Lewis acid BPh 3 —discovered 100 years ago—as a metal-free catalyst for various organic transformations. The first part will highlight catalytic applications in polymer synthesis such as the copolymerization of epoxides with CO 2 , isocyanate, and organic anhydrides to various polycarbonate copolymers and controlled diblock copolymers as well as alternating polyurethanes. This is followed by a discussion of BPh 3 as a Lewis acid component in the frustrated Lewis pair (FLP) mediated cleavage of hydrogen and hydrogenation catalysis. In addition, BPh 3 -catalyzed reductive N-methylations and C-methylations with CO 2 and silane to value-added organic products will be covered as well along with BPh 3 -catalyzed cycloadditions and insertion reactions. Collectively, this mini-review showcases the underexplored potential of commercially available BPh3 in metal-free catalysis.
Polymer matrix composites (PMCs) offer many benefits for the aerospace industry due to their potential for weight reduction when compared to metal or ceramic based materials. Most PMCs currently in flight use thermoset matrices, however, thermoplastic resins are being explored as alternatives due to their ability to be remelted, which is of particular interest due to the potential for in-situ repair and faster production. Most thermoplastic resins are semicrystalline polymers. The properties of semicrystalline thermoplastics are directly influenced by their crystallinity, which can vary due to many factors including thermal treatments, environmental conditions, and mechanical deformation. Within thermoplastic PMC parts, crystallinity gradients can arise due to variations in part geometry, across part thicknesses, and along bonded joints. Monitoring the crystallinity of thermoplastic composites is key to ensuring these materials meet the high demands required for aerospace. Several different analytical techniques exist that can be used to characterize the bulk crystallinity of thermoplastic materials. However, many existing methods lack the specificity required to identify the subtle variations in crystallinity that may play a significant role in the performance and durability of PMC parts. Because of this, a significant amount of work is still required to fully characterize and understand the crystallinity profiles of thermoplastic PMCs and the resulting impact to material properties. This talk discusses the use of multiple techniques such as Differential Scanning Calorimetry, Polarized Light Optical Microscopy, and Fourier-Transform Infrared Spectroscopy to characterize the crystallinity in carbon fiber/thermoplastic composites. Samples of different crystallinity profiles were manufactured using various cooling procedures. This work aims to provide the fundamental data necessary to understand the effects of crystallinity on thermoplastic PMCs, which is key to advancing their use in aerospace applications.
Transition metal oxides not only exhibits novel magnetic properties but also provides outstanding ionic transports. Ionic conductors have great potential for interesting tunable physical properties via ionic liquid gating and novel energy storage applications such as all-solid-state lithium batteries. In particular, low migration barriers and high hopping attempt frequency are the keys to achieve fast ion diffusion in solids. Taking advantage of the oxygen-vacancy channel in Li x Sr 2 Co 2 O 5 , we show that migration barriers of lithium ion are as small as 0.28–0.17 eV depending on the lithium concentration rates. Our first-principles calculation also investigated hopping attempt frequency and concluded the room temperature ionic diffusivity and ion conductivity are high as 10 −7 –10 −6 cm 2 s −1 and 10 −3 –10 −2 Scm −1 , respectively, which outperform most of perovskite-type, garnet-type, and sulfide Li-ion solid-state electrolytes. This work proves Li x Sr 2 Co 2 O 5 as a promising super-ionic conductor.
Various embodiments include cementitious compositions with low levels of embodied greenhouse gas emissions, in particular carbon dioxide, as a result of its production and/or use compared to conventional cementitious materials, such as portland cement. Various embodiments include any cementitious material or materials with low embodied carbon, as well as any material produced using this cement.
Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.
This study evaluates the long-term hygrothermal durability of four advanced retrofit wall systems using a field test facility located in Hollywood, South Carolina, representative of a mixed-humid coastal climate (Zone 3). The research focuses on assessing the thermal and moisture performance of a baseline wall assembly retrofitted with (1) Tremco/Dryvit prefabricated panel systems (Revitalite and Fedderlite), (2) Reinforced Fiberglass Plastic (RFP) panels developed by Oak Ridge National Laboratory (ORNL), and (3) Vacuum Insulated Panels (VIP) integrated with EPS by Home Innovation Research Labs (HIRL) and ORNL. A baseline wall representing a typical uninsulated wood-frame construction was used for comparison.
Achieving high product selectivity in electrocatalytic carbon dioxide reduction (CO 2 RR) remains a critical challenge due to competition between multiple proton-coupled electron-transfer pathways on catalyst surfaces. Meanwhile, chirality-induced spin selectivity (CISS), which enables spin-polarized electron transport through chiral interfaces, has recently emerged as a promising strategy to modulate interfacial electrochemical reactions. Although the CISS effect has been shown to enhance selectivity and efficiency in the spin-sensitive oxygen evolution reaction (OER), its role in regulating CO 2 RR pathways and in stabilizing intermediates remains largely unexplored. Here, chiral molecules (R- and S-1,1′-bi-2-naphthyl-2,2′-diyl hydrogen phosphate, BNP) were integrated with SnO 2 to construct chiral-modified catalysts (R-BNP/SnO 2 and S-BNP/SnO 2 ). Compared with bare SnO 2 and racemic BNP-modified SnO 2 (Rac-BNP/SnO 2 ), the chiral catalysts exhibited a pronounced shift in product selectivity from CO toward formate production. Importantly, in-situ attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) reveals that the chiral interface selectively stabilizes the O-bound *OCHO intermediate associated with the formate pathway and modulates interfacial water structure and hydrogen-bonding dynamics. These findings demonstrate that spin-polarized interfacial electron transfer can regulate CO 2 RR pathway selectivity by modulating the stabilization of key intermediates. More broadly, this work establishes chiral spin-selective interfaces as a new strategy for regulating competitive electrocatalytic reaction pathways.
We applied X-ray photoelectron emission microscopy (XPEEM) to visualize the surface distribution of photocatalytic activity at the anatase/rutile (A/R) interface of titanium dioxide. Acetic acid was adsorbed on the surface, and its decomposition and desorption reactions under ultraviolet light irradiation were monitored by observing C 1s spectra, enabling direct observation of the spatial distribution of photocatalytic activity. Complementary crystal structure information was obtained using spatially resolved low-energy electron diffraction, low-energy electron microscopy, X-ray absorption spectroscopy, and micro-focused Raman spectroscopy. Comparison with these structural data allowed for the evaluation of the photocatalytic activity as a function of the distance from the A/R interface. The activity is enhanced in the vicinity of the interface and gradually decreases toward both the anatase and rutile regions. These results demonstrate that XPEEM is an effective technique for spatially resolved mapping of photocatalytic activity, which has\\r\\npreviously been inaccessible using conventional characterization techniques.
With the high cost and reliability issues of DCFC infrastructure the project specifically addressed the needs of innovating on the AC to DC power conversion. We proved the integration of a new power module into existing charger units, and that theoretical educational work on single stage energy conversion is commercially viable, and a reduced cost compared to existing methods. Our A sample shown 3 PH 480VAC 60hz to 350VDC 8.5kW power transfer, and our software and simulation proved efficiency greater than 97% from 20kW to > 60kW, over the DC output voltage range 200 – 1200V. The project was stopped before we could implement B sample and execute full power testing, validation and integration into a demonstration charger (budget period 3).
In this work, we introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system–bath interaction. These master equations are constructed based on the time-dependent Dirac–Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz. The dynamics converges to the HEOM when increasing the rank of the core tensors, a limit in which the TTN ansatz becomes exact. We introduce TENSO, tensor equations for non-Markovian structured open systems, as a general-purpose Python code to propagate the TTN-HEOM dynamics. We implement three general propagators for the coupled master equations: two fixed-rank methods that require a constant memory footprint during the dynamics and one adaptive-rank method with a variable memory footprint controlled by the target level of computational error. We exemplify the utility of these methods by simulating a two-level system coupled to a structured bath containing one Drude–Lorentz component and eight Brownian oscillators, which is beyond what can presently be computed using the standard HEOM. Our results show that the TTN-HEOM is capable of simulating both dephasing and relaxation dynamics of driven quantum systems interacting with structured baths, even those of chemical complexity, with an affordable computational cost.
While electricity planning practices vary by state and utility based on utility type and market structure, integrated resource planning (IRP) remains a prominent vehicle — even in states with centrally-organized wholesale electricity markets. IRP focuses on meeting forecasted long-term electricity needs. Typically, utilities have not considered impacts of design and operation of the low-voltage distribution network in IRP. With advanced capabilities of grid-edge technologies to generate and store electricity and provide load flexibility, and large utility investments in distribution systems, it's increasingly important to consider at least some distribution planning elements in IRP. This report considers the value proposition for doing so, such as reducing utility costs through resource co-optimization and strategic siting of grid-edge resources, and idenfities the most important touchpoints between planning for bulk power and distribution systems and provide a range of tactics for integrating these two processes.
Addressing sustainable energy storage remains crucial for transitioning to renewable sources. While Li‐ion batteries have made significant contributions, enhancing their capacity through alternative materials remains a key challenge. Micro‐sized silicon is a promising anode material due to its tenfold higher theoretical capacity compared to conventional graphite. However, its substantial volumetric expansion during cycling impedes practical application due to mechanical failure and rapid capacity fading. A novel approach is proposed to mitigate this issue by incorporating trace amounts of aluminum into the micro‐sized silicon electrode using ball milling. Density functional theory (DFT) is employed to establish a theoretical framework elucidating how grain boundary sliding, a key mechanism involved in preventing mechanical failure is facilitated by the presence of trace aluminum at grain boundaries. This, in turn, reduces stress accumulation within the material, reducing the likelihood of failure. To validate the theoretical predictions, capacity retention experiments are conducted on undoped and Al‐doped micro‐sized silicon samples. In conclusion, the results demonstrate significantly reduced capacity fading in the doped sample, corroborating the theoretical framework and showcasing the potential of aluminum doping for improved Li‐ion battery performance.
This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. Here, the even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.
This report is part of series describing a variety of different ResStock™ measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Residential Variable-Speed Geothermal Heat Pump (4.4 COP, 30.9 EER) with Light Envelope Improvements" measure upgrade methodology and briefly discusses key results. All results can be accessed on the ResStock Open Energy Data Initiative "End-Use Load Profiles for the U.S. Building Stock" data lake and on the data viewer at resstock.nlr.gov.