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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Photochemically-Driven CO 2 Release Using a Metastable-State Photoacid for Energy Efficient Direct Air Capture

One of the grand challenges underlying current direct air capture (DAC) technologies relates to the intensive energy cost for sorbent regeneration and CO 2 release, making the massive scale (GtCO 2 /year) deployment required to have a positive impact on climate change economically unfeasible. This challenge underscores the critical need to develop new DAC processes with substantially reduced regeneration energies. Here, in this work, we report a photochemically-driven approach for CO 2 release by exploiting the unique properties of an indazole metastable-state photoacid (mPAH). Our measurements on simulated and amino acid-based DAC systems revealed the potential of mPAH to be used for CO 2 release cycles by regulating pH changes and associated isomers driven by light. Upon irradiating with moderate intensity light, a ≈55 % and ≈68 % to ≈78 % conversion of total inorganic carbon to CO 2 was found for the simulated and amino acid-based DAC systems, respectively. Our results confirm the feasibility of on-demand CO 2 release under ambient conditions using light instead of heat, thereby providing an energy efficient pathway for the regeneration of DAC sorbents.

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

Electron Delocalization Enables Sulfone-based Single-solvent Electrolyte for Lithium Metal Batteries

Li-metal batteries (LMB), although providing high energy density, face the grand challenge of identifying good electrolyte solvents for cycling. Common solvents are either only stable against lithium metal anode or only stable against LiNi x Mn y Co 1-x-y O 2 (NMC) cathode. There is significant effort trying to increase the cathode stability for ether electrolytes, which are in general stable against lithium metal anode. In comparison, there is much less effort trying to increase the anode stability of electrolytes that are stable against NMC cathode. One example is the sulfone-based electrolyte. It has good cathode stability but is hindered from practical application because of (1) high viscosity and poor wetting capability and (2) poor anode stability. Here, we solve these issues by modifying the sulfone molecules using resonance and electron withdrawing effect. The viscosity is significantly reduced by delocalizing the electrons through introducing additional oxygen on the molecular backbone and applying appropriate fluorination. The resulting molecule 2,2,2-trifluoroethyl mesylate (TFEM) has decreased Lewis basicity and less reactivity toward Li + . The electrolyte based on TFEM as single solvent enables cycling of LMB under harsh conditions of low N/P ratio (21 mg/cm 2 NMC811 and 50 μm Li) with 90 % capacity retention after 160 cycles at C/3 discharge rate.

25 ENERGY STORAGE↗
DOE OSTI2025

Polysulfamates as “Macroisosteres” of Polyurethanes with Improved Degradability

Abstract Addressing the environmental persistence of plastics requires the development of next‐generation polymers that combine high performance with enhanced degradability. Progress toward this grand challenge has been impeded, in part, by the absence of a general blueprint for the macromolecular design of such materials. Herein, we introduce a “macroisostere” design strategy, where the carbonyl group (–CO–) in polyurethanes (PUs) is replaced with a sulfonyl group (–SO 2 –), resulting in a virtually unknown family of polymers called polysulfamates. This approach, inspired by the use of bioisosteres in drug discovery, aims to preserve key interchain interactions that contribute to thermomechanical performance while enhancing the hydrolytic lability of the polymer backbone. The optimization of a Sulfur(VI) Fluoride Exchange (SuFEx) polymerization allowed the synthesis of ten polysulfamates structurally analogous to common PUs. Comparative analysis of one PU and its polysulfamate analog showed that this isosteric substitution increases thermal stability, slightly lowers the glass transition temperature, and retains similar hardness and reduced Young's modulus. Notably, the S(VI)‐based polysulfamate demonstrated significantly enhanced hydrolytic degradability. These results highlight the potential of the “macroisostere” approach as a generalizable strategy for designing high‐performance, degradable alternatives to traditional plastics.

Chemistry↗
DOE OSTI2024

Algae to HEFA : Economics and potential deployment in the United States

Abstract To reach the goals set by the US Department of Energy's Sustainable Aviation Fuel (SAF) Grand Challenge, currently available feedstocks may be insufficient. Giving priority to developing, prototyping and reducing the cost of algal feedstock before investing and lining up locations is important. As the production of algal feedstocks advances, a simplified conversion approach using more mature technologies can help reduce the investment risk for algae‐based fuels. Reducing process complexity to the steps described here [namely, conversion of lipids to HEFA (hydroprocessed esters and fatty acids) fuels and relegating the remainder of the biomass to anaerobic digestion or food/feed production] enables the near‐term production of algal SAF but presents challenging economics depending on achievable cultivation costs and compositional quality. However, these economics can be improved by present‐day policy incentives. With these incentives, the modeled algae‐to‐HEFA pathway could reach a minimum fuel selling price as low as $4.7 per gasoline gallon equivalent depending on the carbon intensity reduction that can be achieved compared with petroleum. Uncertainty about algal feedstock production maturity in the current state of technology and the future will play a large role in determining the economic feasibility of building algae‐to‐HEFA facilities. For example, if immaturity increases the feedstock price by even 10%, SAF production in 2050 is about 58% of the production which could have been achieved with mature feedstock. Additionally, growth in this conversion pathway can be notably boosted through the inclusion of subsidies, and also through higher‐value coproducts or higher lipid yields beyond the scope of the process considered here.

09 BIOMASS FUELS↗
DOE OSTI2023

Status of genome function annotation in model organisms and crops

Abstract Since the entry into genome‐enabled biology several decades ago, much progress has been made in determining, describing, and disseminating the functions of genes and their products. Yet, this information is still difficult to access for many scientists and for most genomes. To provide easy access and a graphical summary of the status of genome function annotation for model organisms and bioenergy and food crop species, we created a web application ( https://genomeannotation.rheelab.org ) to visualize, search, and download genome annotation data for 28 species. The summary graphics and data tables will be updated semi‐annually, and snapshots will be archived to provide a historical record of the progress of genome function annotation efforts. Clear and simple visualization of up‐to‐date genome function annotation status, including the extent of what is unknown, will help address the grand challenge of elucidating the functions of all genes in organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sequence-defined Pareto frontier of a copolymer structure

The correlations between the sequence of monomers in a macromolecule and its three-dimensional (3D) structure is a grand challenge in polymer science. The properties and functions of macromolecules depend on their 3D shape that has appeared to be dictated by their monomer sequence. However, the progress towards understanding the sequence–structure-property correlations and their utilization in materials engineering are slow because it is almost impossible to characterize an astronomically large number of possible sequences of a copolymer using traditional experimental and simulation methods. To address this problem, here, we combine evolutionary computing and coarse-grained molecular dynamics (CGMD) simulation and study the sequence-structure correlations of a model AB-type copolymer in a solution and assess the impact of sequence on the packing density in its bulk phase. The CGMD-based evolutionary algorithm (EA) screens the sequence space of a single chain copolymer efficiently and identifies a wide range of single-molecule structures including extremal radii of gyration. The data are utilized to estimate the Pareto front of the structure-space of a binary copolymer as a function of its composition. The monomer packings in single-molecule solution phase and multimolecular bulk phase are found to be identical. Finally, this work highlights the opportunities of sequence-specific control of macromolecular structure for designing target materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

Eismann, Stephan↗
DOE OSTI2024

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Modifying the Reactivity of Single Pd Sites in a Trimetallic Sn‐Pd‐Ag Surface Alloy: Tuning CO Binding Strength

Abstract Improving control over active‐site reactivity is a grand challenge in catalysis. Single‐atom alloys (SAAs) consisting of a reactive component doped as single atoms into a more inert host metal feature localized and well‐defined active sites, but fine tuning their properties is challenging. Here, a framework is developed for tuning single‐atom site reactivity by alloying in an additional inert metal, which this work terms an alloy‐host SAA. Specifically, this work creates about 5% Pd single‐atom sites in a Pd 33 Ag 67 (111) single crystal surface, and then identifies Sn based on computational screening as a suitable third metal to introduce. Subsequent experimental studies show that introducing Sn indeed modifies the electronic structure and chemical reactivity (measured by CO desorption energies) of the Pd sites. The modifications to both the electronic structure and the CO adsorption energies are in close agreement with the calculations. These results indicate that the use of an alloy host environment to modify the reactivity of single‐atom sites can allow fine‐tuning of catalytic performance and boost resistance against strong‐binding adsorbates such as CO.

Mohrhusen, Lars↗
DOE OSTI2022

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗
DOE OSTI2021

Hybrid machine learning/physics-based approach for predicting oxide glass-forming ability

Predicting the liquid compositions that will vitrify at experimentally accessible quench rates remains one of the grand challenges in the field of condensed matter physics. This glass-forming ability can be quantified as the critical quench rate needed to suppress crystallization. Knowledge of this critical quench rate also informs which glass composition could be used for new applications. There have been several physical and empirical models presented in the literature to predict the critical quench rate/glass forming ability. These models range from those theoretically derived to those quantified only through experimental characterization. In this work, we instead propose a new method to calculate the critical quench rate using the recently developed toy landscape model combined with machine learning. The toy landscape model accesses the underlying physics that control the vitrification behavior by directly simulating the liquid thermodynamics and kinetics. In conclusion, the results are discussed in terms of industrial impact, physical insights, and how the glass science community can develop improved predictions of glass-forming ability.

Crystallization↗
DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhanced mobility of dislocation network nodes and its effect on dislocation multiplication and strain hardening

Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked. Here, by comparing large-scale Molecular Dynamics (MD) with DDD simulations performed under identical conditions we uncover significant discrepancies in the predicted strength and microstructure evolution in BCC crystals under high-strain rate conditions. These are traced to unexpected behaviors of dislocation network nodes forming at dislocation intersections, that can move in ways not previously anticipated as revealed by MD. Once these newfound freedoms of nodal motion are incorporated, DDD simulations begin to closely match plastic evolution observed in MD. This additional mechanism of motion whereby non-screw dislocations can change their glide plane profoundly affects fundamental processes of dislocation multiplication, recovery and storage that define strength of metals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Elimination of NO Pollutant in Semi-enclosed Spaces over Sodium-promoted Cobalt Oxyhydroxide (CoOOH) by Oxidation and Adsorption Mechanism

Low concentrations of NOx accumulated in air in semi-enclosed spaces is a severe health hazard, but its efficient removal at low cost is a grand challenge. A simple method in preparing CoOOH for low-concentration NO removal at room temperature is described here. The samples are synthesized by oxidizing Co(OH)2 to CoOOH with different oxidants (Na2S2O8, O2 and H2O2). Among them, CoOOH prepared by Na2S2O8 (Co-NS) shows remarkable performance for NO abatement, while the samples synthesized by and H2O2 (Co-HO) and O2 (Co-OG) have no apparent NO removal capacity. It is found that the Co-NS material contains a higher concentration of residual Na, which contributes to the decreased crystallinity and increased concentrations of surface active species (Co2+, active oxygen) for NO storage. Following NO storage, the material can be readily regenerated by water washing. The authors from East China University of Science and Technology acknowledge support from National Basic Research Program of China (2013CB933200), National Natural Science Foundation of China (21577035, 21577034), Commission of Science and Technology of Shanghai Municipality (15DZ1205305) and 111 Project (B08021). Aiyong Wang acknowledges the China Scholarship Council for the Joint-Training Scholarship Program with the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle for the US DOE under contract DE-AC05-76RL01830. FG is supported by the U.S. DOE/Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office.

NOx abatement, NOx adsorber, ambient temperature, ↗
DOE OSTI2025

Real-time inference and extrapolation with Time-Conditioned UNet: Applications in hypersonic flows, incompressible flows, and global temperature forecasting

Neural Operators are fast and accurate surrogates for nonlinear mappings between functional spaces within training domains. Extrapolation beyond the training domain remains a grand challenge across all application areas. We present Time-Conditioned UNet (TC-UNet) as an operator learning method to solve time-dependent PDEs continuously in time without any temporal discretization, including in extrapolation scenarios. TC-UNet incorporates the temporal evolution of the PDE into its architecture by combining a parameter conditioning approach with the attention mechanism from the Transformer architecture. After training, TC-UNet makes real-time inferences on an arbitrary temporal grid. We demonstrate its extrapolation capability on a climate problem by estimating the global temperature for several years and also for inviscid hypersonic flow around a double cone. We propose different training strategies involving temporal bundling and sub-sampling. We demonstrate performance improvements for several benchmarks, performing extrapolation for long time intervals and zero-shot super-resolution time.

Deep learning↗
DOE OSTI2023

Operando methods: A new era of electrochemistry

One of the grand challenges facing electrochemistry is to directly resolve the complex nature of (electro)catalyst active sites and capture real-time “movies” of reaction dynamics, i.e. “watching chemistry in action”. The need for such fundamental understanding has stimulated the development of operando/in situ methods, which have greatly enhanced our ability to identify activity descriptors of electrocatalysts and establish structure–property relationships of energy materials. Here, this review summarizes the frontiers of operando electrochemical liquid-cell scanning transmission electron microscopy and correlative synchrotron X-ray methods, which are complementary tools to comprehensively investigate reaction dynamics across multiple spatiotemporal scales. In an effort to encourage greater adoption of advanced operando methods by the general electrochemistry community, this review points out the need to benchmark electrochemistry in confined and heterogenous liquid environment with minimal beam-induced damage. We anticipate that multimodal operando methods will become indispensable for understanding interfacial reaction mechanisms for the broad chemistry and energy materials communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗