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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 scientific requirements and implications for the instruments and telescope design for the Next Generation Space Telescope (NGST) are described. A candidate concept is a deployable, 8 m diameter telescope, optimized for the near infrared region, but featuring instruments capable of observing up to 30 micrometers. The observatory is radiatively cooled to approximately 30 K.
Abstract Redox flow batteries (RFBs) are a promising option for long‐duration energy storage (LDES) due to their stability, scalability, and potential reversibility. However, solid‐state and non‐aqueous flow batteries have low safety and low conductivity, while aqueous systems using vanadium and zinc are expensive and have low power and energy densities, limiting their industrial application. An approach to lower capital cost and improve scalability is to utilize cheap Earth‐abundant metals such as iron (Fe). Nevertheless, all‐iron RFBs have many complications, involving voltage loss from ohmic resistance, side reactions such as hydrogen evolution, oxidation, and most significantly electrode plating, and dendrite growth. To address these issues, researchers have begun to examine the effects of various alterations to all‐iron RFBs, such as adding organic ligands to form Fe complexes and using a slurry electrode instead of common materials such as graphite or platinum rods. Overall, progress in improving aqueous all‐iron RFBs is at its infant stage, and new strategies must be introduced, such as the utilization of nanoparticles, which can limit dendrite growth while increasing storage capacity. This review provides an in‐depth overview of current research and offers perspectives on how to design the next generation of all‐iron aqueous RFBs.
Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.
Abstract Despite their wide applications, lithium‐ion batteries (LIBs) have been struggling with their safety risks arising from different lifetime stages. Here, the NCMA pouch cell is taken as an example, and the safety of both fresh and aged cells from three milestone stages, that is, internal short circuit (ISC) triggering risk, ISC mode, and the subsequent thermal runaway (TR) consequence is investigated. By combining mechanical abusive testing and physics‐based models on commercialized cells with various states‐of‐health (SOH) and states‐of‐charge, it is discovered that the ISC triggering delays with the decay of SOH and soft ISC mode will be triggered more frequently, which is mainly due to the mechanical behaviors of the current collectors. The temperature rises and peak temperature during the subsequent TR also become milder for aged cells due to the reduced capacity and deterministic soft ISC process. Results here provide a mechanistic explanation of the safety risk comparison between the fresh and aged cells, offering cornerstone guidance to the evaluation and design of next‐generation safer LIBs.
Abstract This communication presents a mechanism‐based approach to identify organic electrolytes for non‐aqueous redox flow batteries (RFBs). Symmetrical flow cell cycling of a pyridinium anolyte and a cyclopropenium catholyte resulted in extensive capacity fade due to competing decomposition of the pyridinium species. Characterization of this decomposition pathway enabled the rational design of next‐generation anolyte/catholyte pairs with dramatically enhanced cycling performance. Three factors were identified as critical for slowing capacity fade: (1) separating the anolyte–catholyte in an asymmetric flow cell using an anion exchange membrane (AEM); (2) moving from monomeric to oligomeric electrolytes to limit crossover through the AEM; and (3) removing the basic carbonyl moiety from the anolyte to slow the protonation‐induced decomposition pathway. Ultimately, these modifications led to a novel anolyte–catholyte pair that can be cycled in an AEM‐separated asymmetric RFB for 96 h with >95 % capacity retention at an open circuit voltage of 1.57 V.
We develop a GPU-accelerated machine learning generative adversarial model designed to facilitate causal inferences from observational data. Our model's theoretical framework is conceptualized in a manner that is amenable to being operable and scalable for high-performance computing platforms. We leverage GPU acceleration to develop a parallel evolutionary algorithm to achieve large-scale parallel computation of the model within a now widely accessible computing platform. This capability both enhances computational speedup and efficiency and also extends the use of the model to a broader range of substantive research domains while maintaining the underlying theoretical properties of the model.
Electrocatalysts that start a reaction as molecules do not always end the reaction as molecules, and even when they do, they might not be molecules during catalysis. In this Perspective, we discuss knowledge learned from the study of Cu-based molecularly structured electrocatalysts––including metal coordination complexes, metal-organic frameworks, single-atom catalysts, and polymeric materials––that restructure under electrochemical CO 2 reduction reactions. Recent reports are summarized with an emphasis on the nature and significance of post-mortem and in situ characterization for the proper identification of active sites. We demonstrate that molecular and material structures determine whether electrocatalysts restructure and how they restructure, that understanding of restructuring processes can help us identify active sites for catalysis, and that this knowledge can be leveraged to design precatalysts that generate highly active catalysts under reaction conditions. In addition, we provide recommended practices for studying the integrity of heterogeneous molecular catalysts during and after CO 2 reduction reactions.
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Nanofiltration (NF) membranes have garnered significant interest for Li + /Mg 2+ separation, a crucial step in lithium extraction from brines. The state-of-the-art commercial LiNE-XD membrane exhibits a Li + /Mg 2+ separation factor (SF Li/Mg ) of 42 at pH 3.3, due to the positive charges on the membrane surface and its strong size-sieving ability. Membranes with higher separation factors at a broad pH range are being pursued to improve separation efficiency. This work aims to provide a timely and comprehensive assessment of advanced NF membranes with superior Li + /Mg 2+ separation properties, as well as their structure and property relationships for designing next-generation membranes. We describe transport mechanisms for ions in NF membranes and discuss governing parameters and models employed to quantify the Li + /Mg 2+ separation. High-performance membrane materials are exhaustively introduced, including commercial and modified polyamides, two-dimensional materials, metal–organic frameworks, crown ethers, and their blends. Finally, we critically compare these membranes in an upper bound plot and highlight the opportunities and challenges of NF membranes for practical Li + /Mg 2+ separations.
Self-assembled colloidal crystals display structural colors due to light diffracted from their microscale, ordered structure. This color arises due to Bragg reflection (BR) or grating diffraction (GD); the latter mechanism is much less explored than the former. Here the design space for generating GD structural color is identified and its relative advantages are demonstrated. Electrophoretic deposition is used to self-assemble crystals with fine crystal grains from colloids of diameter 1.0 µm. The structural color in transmission is tunable across the full visible spectrum. The optimum optical response—represented by both color intensity and saturation—is observed at low layer number (≤5 layers). The spectral response is well predicted by Mie scattering of the crystals. Taken together, the experimental and theoretical results demonstrate that vivid grating colors with high color saturation can be produced from thin layers of micron-sized colloids. These colloidal crystals extend the potential of artificial structural color materials.
Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.
The constant fragment (Fc) of the immunoglobulin G1 (IgG1) subtype is increasingly recognized as a crucial scaffold in the development of advanced therapeutics due to its enhanced specificity, efficacy, and extended half‐life. A prime example is VEGF‐Trap (Aflibercept), a recombinant fusion protein that merges the Fc region of the IgG1 subtype with the binding domains of vascular endothelial growth factor receptors (VEGFR)‐1 and VEGFR‐2. The Fc region's role in N‐glycosylation is particularly important, as it significantly influences protein stability. Herein, the first near‐physiological temperature structures of the N‐glycan‐bound Fc fragment of IgG1 subtype from a biosimilar VEGF‐Trap are presented, determined using the SPring‐8 Angstrom Compact free electron LAser (SACLA) and the Turkish Light Source (Turkish DeLight). Comparative analysis with cryogenic structures, including existing data, reveals alternate conformations within the glycan‐binding pocket. Furthermore, molecular dynamics simulations indicate the presence of a high degree of structural plasticity, explaining how the protein adapts its structure through conformational changes. The observed structural fluctuations/conformational changes demonstrate the effect of N‐glycans on protein stability. These findings offer new insights into the molecular basis of Fc‐mediated functions and provide valuable information for the design of next‐generation therapeutics.
Abstract In this work we tailor the response of trusses loaded beyond their yield limit. The truss structures are modeled using finite strain theory and rate-independent elasto-plasticity. We design trusses with a tailored mechanical response that is between “elastic” and “elastic-ideal-plastic” subject to the volume constraint. The design updates are generated by the gradient-based Method of Moving Asymptotes (MMA) solver and the sensitivities of the response functions are computed using a path-dependent adjoint sensitivity analysis. The computations are performed in Matlab.
Abstract Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25 $$^{\circ }$$ ∘ ) and coarse-resolution (1 $$^{\circ }$$ ∘ ) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.
This article introduces the Future Foundries platform developed at Oak Ridge National Laboratory, a first-generation research system designed to demonstrate convergent manufacturing. Convergent manufacturing brings together additive, subtractive, and transformative processes in a digitally interconnected environment to enable end-to-end production workflows. By linking traditionally discrete steps, convergent platforms accelerate production, improve repeatability, and support high-mix, low-volume manufacturing. The Future Foundries platform exemplifies this vision in practice by combining four modular, vendor-agnostic process cells that include robotic WAAM, induction heating, optical metrology, and machining, coordinated through an automated pallet handler and a ROS 2-based digital thread. This architecture provides the flexibility and scalability needed for agile production in small and medium-sized manufacturing enterprises and for field deployable manufacturing. Two case studies illustrate the platform’s capabilities. The first presents an integrated workflow for fabricating, transforming, and repairing critical replacement components, showing how consolidated thermal, additive, inspection, and machining operations reduce manual part handling and streamline process flow. The second case study highlights coordinated multi-part production enabled by automated pallet logistics and multi-cell scheduling. Together, these examples showcase convergent manufacturing as a practical and scalable strategy for strengthening domestic casting and forging capacity, improving supply-chain resilience, and enabling rapid, adaptable production of mission-critical components.
Rising costs and performance limits of modern biomedical materials motivate the search for advanced, biocompatible alternatives. Cellulose-based metal-organic frameworks (cellulose-MOFs) emerge as distinctive hybrids combining renewable polymer chemistry with tunable porous architectures, enabling uncommon structure–function relationships. Their large surface area, controllable pore size, adaptable functional groups, and efficient host–guest interactions underpin diverse biomedical functions. Till now, no comprehensive, application-focused review has systematically summarized cellulose-MOFs synthesis for biomedical applications. This review critically analyzes cellulose-MOFs, emphasizing mechanistic links between chemistry, synthesis routes, interfacial interactions, and biomedical performance, rather than cataloging applications alone. Antibacterial action, targeted drug delivery, and sensing/biosensing are discussed through comparative insights. The article identifies unresolved challenges and proposes future research pathways to rationally design next-generation cellulose-MOFs systems, guiding researchers and clinicians alike.
Adopting a solid-phase microwave-assisted technique followed by thermal pyrolysis of N-functionalized graphene quantum dots, novel nitrogen-doped graphite-like (NGL) electrode materials were synthesized in this work and served as the anode for Li-ion batteries. The NGL anode demonstrated reversible capacity of 530 mAh g -1 at 0.1C, superior rate capability at high C rate operation (420 mAh g -1 at 5C), remarkable initial coulombic efficiency (>95.7%), and excellent cyclic stability along with high efficiency (>99.1%) during entire cycling. The NGL anode nanostructure enables improved lithium ion mobility and reversible Li + storage during cycling. The analysis of the Ragone plots revealed that the specific energy of NGL anode reaches to ca. 840 Wh kg -1 at the power density of 4200 W kg -1 . The diffusion coefficient of Li ions was measured as 1.69 × 10 -9 cm 2 s -1 for the NGL anode material, substantially improving over commonly used graphite electrodes (15–26 times higher Li + diffusivity). The high-rate cyclability as well as the cyclic stability of the NGL anodes were also confirmed via long-term cycling of full pouch cells assembled with ternary cathode and NGL anode. The robust design of the NGL anode materials introduced in this work, paves the way for designing next-generation lithium-ion batteries operating at ultra-high C rates.
Propane has demonstrated significant potential for reductions in greenhouse gas and pollutant emissions in medium- and heavy-duty engine applications, but further improvements require accurate, compact, and scalable chemical kinetic mechanisms to design the next generation of propane fueled engines, particularly at the boosted operating conditions necessary to meet the power density demand of medium- and heavy-duty applications. In this work, six key chemical reactions were identified in a reduced mechanism with 70 species and 352 reactions through a sensitivity analysis performed at conditions typical of thermodynamic trajectories observed in a high compression ratio, long stroke engine operated on propane from throttled to boosted operating conditions. While the original mechanism was validated against rapid compression machine (RCM) data, it was found to overpredict experimental autoignition tendencies in 2-zone, 0-D SI engine simulations performed in Chemkin Pro. Subsequently, a genetic algorithm approach was used to optimize the six reaction rate parameters within established uncertainty bounds by performing RCM simulations and comparing to two independent sets of literature ignition delay times for propane, thus generating two new kinetic mechanisms. The first optimization achieved a mean absolute percent error (MPE) reduction in 2nd stage ignition delay of 61.4% in seven generations, while the second optimization utilized a newer experimental RCM dataset, and achieved MPE reduction of 56.7% in seven generations, and further marginal improvement to 57.8% reduction in 34 generations. Finally, the two mechanisms were then evaluated again in the 2-zone 0-D SI engine model in Chemkin Pro comparing typical mean and knocking cycle trajectories, and it was found that the second optimized mechanism provided better prediction of knock onset at the representative conditions evaluated in this work, particularly for higher load operating conditions.