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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comprehensive Battery Safety Risk Evaluation: Aged Cells versus Fresh Cells Upon Mechanical Abusive Loadings

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.

25 ENERGY STORAGE↗
DOE OSTI2020

Realization of an Asymmetric Non‐Aqueous Redox Flow Battery through Molecular Design to Minimize Active Species Crossover and Decomposition

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.

Shrestha, Anuska↗
DOE OSTI2025

A GPU‐Accelerated Generative Adversarial Model for Causal Inference

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.

GPU↗
DOE OSTI2022

Restructuring and integrity of molecular catalysts in electrochemical CO 2 reduction

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.

CO2 reduction↗
DOE OSTI2025

Nanofiltration Membranes for Li + /Mg 2+ Separation: Materials and Mechanisms

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Reconfigurable Grating Diffraction Structural Color in Self-Assembled Colloidal Crystals

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.

42 ENGINEERING↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

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.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Experimental and Computational Insights into the Structural Dynamics of the Fc Fragment of IgG1 Subtype from Biosimilar VEGF‐Trap

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.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Response tailoring of elasto-plastic trusses

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.

42 ENGINEERING↗
DOE OSTI2024

ESM data downscaling: a comparison of super-resolution deep learning models

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.

Pawar, Nikhil M. (ORCID:0000000211613289)↗
DOE OSTI2026

Future foundries: A convergent manufacturing platform

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.

Convergent manufacturing↗
DOE OSTI2026

Cellulose-MOFs hybrid materials: Chemistry and mechanism of applications in biomedical - A review

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.

Biomedical↗
DOE OSTI2020

Improved lithium storage capacity and high rate capability of nitrogen-doped graphite-like electrode materials prepared from thermal pyrolysis of graphene quantum dots

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.

25 ENERGY STORAGE↗
DOE OSTI2023

Genetic algorithm optimization of a chemical kinetic mechanism for propane at engine relevant conditions

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modeling of fission and activation products in molten salt reactors and their potential impact on the radionuclide monitoring stations of the International Monitoring System

Molten Salt Reactors (MSRs) are one of six Generation IV reactor designs currently under development around the world. Because of the unique operating conditions of MSRs, which include molten fuel and the continuous removal of gaseous fission products during operation, work was performed to analyze the potential impact of emissions on the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Simulations were performed to predict the production of IMS-relevant radionuclides in four MSR designs operating under two scenarios: (1) a sealed reactor with releases only during operational shutdown, and (2) continuous reprocessing or sparging of the fuel salt. From these production estimates the radioxenon and radioiodine signatures were extracted and compared to three current reactor designs (Pressurized Water Reactor (PWR), BWR, RBMK). In cases where continuous reprocessing of the fuel salt occurred, both the radioxenon and radioiodine signatures were nearly indistinguishable from a nuclear explosion. Estimates were also made of the potential emission rate of radioxenon for three reactor designs and it was found that MSRs have the potential to emit radioxenon isotopes at a rate of 10^15-8×10^16 Bq/d for 133Xe if no abatement is used. An assessment was made of activation products using a candidate fuel salt (FLiBe) mixed with corrosion products for the Thorium Molten Salt Reactor (TMSR-LF1).

Johnson, Christine M.↗
DOE OSTI2020

Plasticity of irradiated materials at the nano and micro-scales

Here, we review here our recent work on plastic deformation in irradiated materials at the nano- and micro-scales, as revealed by Discrete Dislocation Dynamics (DDD) simulations. Two methods of including irradiation effects in the DDD framework are presented. The first directly captures the atomistic interaction mechanisms, while the second can effectively study high-dose irradiation. Computer simulations lead to new understanding of the dynamics of collective dislocation-irradiation defect interactions, as well as the quantitative analysis of the temporal and spatial characteristics associated with plastic instabilities. Based on these insights, theoretical models are developed to predict the critical conditions for dislocation channel formation. A simple probability model is proposed and demonstrated to predict the width of dislocation channels in bulk irradiated materials with good agreement with experimental data. The fundamental understanding of the origins of plastic flow localization in irradiated materials sheds light on the design of future generations of radiation-resistant materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deformation and Fracture Behaviors of Cylindrical Battery Shell During Thermal Runaway

Thermal runaway is one of the catastrophic failure modes of lithium-ion cells. During thermal runaway in cylindrical cells, sidewall shell rupture has been identified as a contributing factor for thermal runaway propagation in battery packs. Herein, the deformation and fracture behaviors of the battery shell during thermal runaway are investigated based on in-situ and ex-situ characterization as well as physics-based modeling. The deformation and fracture modes of the battery shell with/without Carbon Fiber Reinforced Polymer (CFRP) sleeves are identified. In the simulation, the strain introduced by thermal expansion of the system is considered, as well as the thermal and strain rate effects on the plastic stage. The final cell shell modeling is validated by cell thermal runaway tests. Results reveal the quantitative relation between shell deformation behaviors and the pressure and temperature distribution. Both the experiment and model demonstrate the effectiveness of adding a tight-fitting CFRP sleeve around the cell in limiting the side-wall rupture of the shell. The use of CFRP tubes introduces a novel phenomenon in the context of uneven temperature distribution. Results shed light on the mechanistic analysis of a cell side-wall rupture during thermal runaway and provide essential guidance for the next-generation safe battery design.

battery casing↗