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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Accelerating Learning with Set-Based Concurrent Engineering

The traditional design approach for product development is to develop a design based on customer requirements and technical knowledge, build the product according to the detail requirements provided, and then test the product to validate that it works as intended. The team typically starts by identifying a single design approach and spends their time validating a single design during testing. Design teams often encounter issues in the course of development with performance, manufacturability, interfaces and more. To compensate for test failures, teams often build in time into the schedule for additional design loopbacks. Further, when the loopbacks lead to change, the late changes are costly and risky, causing the team to focuses on "fixing the bare minimum" to meet cost and schedule expectations.

42 ENGINEERING↗
DOE OSTI2025

STARFIRE – Science and Technology Accelerated Research for Fusion Innovation and Reactor Engineering progress report

The STARFIRE hub leverages foundational science and technology (S&T) research and integrated plant modeling tools to advance Inertial Fusion Energy (IFE). S&T breakthroughs achieved in each thrust are incorporated into an integrated modeling tool to assess and prioritize remaining gaps to bridge for IFE to become viable. Scientific highlights span high-gain target design, diode-pumped solid-state lasers, and innovative target manufacturing. Key highlights include the investigation of shock propagation through various foam structures among other limiting factors of compression of IFE target, exploration of novel 3D printing techniques such as light-sheet additive manufacturing for 2-photon polymerization foams, and diode-pumped solid-state laser architectures required for several IFE approaches. In addition, the STARFIRE team has established two public-private working groups to address laser diode supply chain issues and test advanced laser diode configurations. Finally, a lite version of the integrated modeling tool has been developed to facilitate outreach and engage with private companies. STARFIRE’s management structure has been designed to ensure agile resource allocation, robust performance assessment, and alignment with the broader fusion energy mission. STARFIRE’s breakthroughs are disseminated to the community via reports, presentations, and publications, while agreements with private companies are set in place to protect proprietary information.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Data from Development of a CRISPR/Cas9 System for High-Efficiency Multiplexed Gene Deletion in Rhodosporidium toruloides

The oleaginous yeast Rhodosporidium toruloides is considered a promising candidate for production of chemicals and biofuels thanks to its ability to grow on lignocellulosic biomass, and its high production of lipids and carotenoids. However, efforts to engineer this organism are hindered by a lack of suitable genetic tools. Here we report the development of a CRISPR/Cas9 system for genome editing in R. toruloides based on a fusion 5S rRNA–tRNA promoter for guide RNA (gRNA) expression, capable of greater than 95% gene knockout for various genetic targets. Additionally, multiplexed double‐gene knockout mutants were obtained using this method with an efficiency of 78%. This tool can be used to accelerate future metabolic engineering work in this yeast.

Conversion↗
DOE OSTI2019

Development of a CRISPR/Cas9 system for high efficiency multiplexed gene deletion in Rhodosporidium toruloides

The oleaginous yeast Rhodosporidium toruloides is considered a promising candidate for production of chemicals and biofuels thanks to its ability to grow on lignocellulosic biomass, and its high production of lipids and carotenoids. However, efforts to engineer this organism are hindered by a lack of suitable genetic tools. In this paper, we report the development of a CRISPR/Cas9 system for genome editing in R. toruloides based on a fusion 5S rRNA–tRNA promoter for guide RNA (gRNA) expression, capable of greater than 95% gene knockout for various genetic targets. Additionally, multiplexed double-gene knockout mutants were obtained using this method with an efficiency of 78%. Finally, this tool can be used to accelerate future metabolic engineering work in this yeast.

09 BIOMASS FUELS↗
DOE OSTI2022

Cell-Free Protein Synthesis for High-Throughput Biosynthetic Pathway Prototyping

Biological systems provide a sustainable and complimentary approach to synthesizing useful chemical products. Metabolic engineers seeking to establish economically viable biosynthesis platforms strive to increase product titers, rates, and yields. Despite continued advances in genetic tools and metabolic engineering techniques, cellular workflows remain limited in throughput. It may take months to test dozens of unique pathway designs even in a robust model organism, such as Escherichia coli. In contrast, cell-free protein synthesis enables the rapid generation of enzyme libraries that can be combined to reconstitute metabolic pathways in vitro for biochemical synthesis in days rather than weeks. Cell-free reactions thereby enable comparison of hundreds to thousands of unique combinations of enzyme homologs and concentrations, which can quickly identify the most productive pathway variants to test in vivo or further characterize in vitro. Furthermore, this cell-free pathway prototyping strategy provides a complementary approach to accelerate cellular metabolic engineering efforts toward highly productive strains for metabolite production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification of a specific exporter that enables high production of aconitic acid in Aspergillus pseudoterreus

Aconitic acid is an unsaturated tricarboxylic acid that is attractive for its potential use in the manufacture of biodegradable and biocompatible polymers, plasticizers, and surfactants. Previously Aspergillus pseudoterreus was engineered as a platform to produce aconitic acid by deleting the cadA (cis-aconitic acid decarboxylase) gene in the itaconic acid biosynthetic pathway. In this study aconitic acid transporter gene (aexA) was identified using comparative global discovery proteomics analysis between the wild-type and cadA deletion strains. Deletion of aexA almost eliminated aconitic acid secretion, while its overexpression led to a significant increase in aconitic acid production. Transportation of aconitic acid across the plasma membrane is a key limiting step. In vitro proteoliposome transport assay further validated AexA’s function and its substrate specificity. This research provides new approaches to efficiently pinpoint and characterize exporters of fungal organic acids and accelerate the metabolic engineering to improve secretion capability and lower cost for bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

"Forward" Projects Boost U.S. Leadership in Advanced Computing and Artificial Intelligence

High-performance computing (HPC) has been an indispensable research tool for accessing physical realms difficult, or impossible, achieve with experiment alone. For several decades, the Department of Energy’s (DOE’s) Office of Science has deployed sophisticated HPC systems for solving the nation’s most pressing grand challenge problems in energy, climate change, and human health. In addition, DOE’s National Nuclear Security Administration (NNSA) has adeptly applied HPC in support of key national security objectives, such as nuclear science and stockpile modernization and stewardship. Over time, HPC systems have become increasingly more complex and capable, and as each new machine has come online, scientists and engineers have taken advantage of vast increases in compute power to accelerate scientific discoveries and engineering innovation.

42 ENGINEERING↗
DOE OSTI2019

High-throughput mapping of CoA metabolites by SAMDI-MS to optimize the cell-free biosynthesis of HMG-CoA

Metabolic engineering uses enzymes to produce small molecules with industrial, pharmaceutical, and energy applications. However, efforts to optimize enzymatic pathways for commercial production are limited by the throughput of assays for quantifying metabolic intermediates and end products. We developed a multiplexed method for profiling CoA-dependent pathways that uses a cysteine-terminated peptide to covalently capture CoA-bound metabolites. Captured metabolites are then rapidly separated from the complex mixture by immobilization onto arrays of self-assembled monolayers and directly quantified by SAMDI mass spectrometry. We demonstrate the throughput of the assay by characterizing the cell-free synthesis of HMG-CoA, a key intermediate in the biosynthesis of isoprenoids, collecting over 10,000 individual spectra to map more than 800 unique reaction conditions. We anticipate that our rapid and robust analytical method will accelerate efforts to engineer metabolic pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Unraveling the Stable Cathode Electrolyte Interface in all Solid‐State Thin‐Film Battery Operating at 5 V

Abstract Spinel‐type LiNi 0.5 Mn 1.5 O 4 (LNMO) is one of the most promising 5 V‐class cathode materials for Li‐ion batteries that can achieve high energy density and low production costs. However, in liquid electrolyte cells, the high voltage causes continuous cell degradation through the oxidative decomposition of carbonate‐based liquid electrolytes. In contrast, some solid‐state electrolytes have a wide electrochemical stability range and can withstand the required oxidative potential. In this work, a thin‐film battery consisting of an LNMO cathode with a solid lithium phosphorus oxynitride (LiPON) electrolyte is tested and their interface before and after cycling is characterized. With Li metal as the anode, this system can deliver stable performance for 600 cycles with an average Coulombic efficiency >99%. Neutron depth profiling indicates a slight overlithiated layer at the interface prior to cycling, a result that is consistent with the excess charge capacity measured during the first cycle. Cryogenic electron microscopy further reveals intimate contact between LNMO and LiPON without noticeable structure and chemical composition evolution after extended cycling, demonstrating the superior stability of LiPON against a high voltage cathode. Consequently, design guidelines are proposed for interface engineering that can accelerate the commercialization of a high voltage cell with solid or liquid electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2024

Developing Multiphysics, Integrated, High-Fidelity, Massively Parallel Computational Capabilities for Fusion Applications Using MOOSE

As the need for fusion as a clean, sustainable, and abundant energy source grows internationally, so does the need for multiphysics, computational tools to model, study, and predict the complex interactions between plasma, materials, and engineering processes. These tools have a crucial role to play in solving scientific and engineering challenges and accelerating fusion energy deployment. To address these needs, modeling capabilities should enable massively parallel, multiphysics, fully integrated high-fidelity simulations of fusion systems. Additional attributes, such as being open source and modular while maintaining high software quality assurance standards will maximize impact by ensuring accessibility for all and wide acceptance, rapid expansion and development, as well as reliability, efficiency, and robustness. In this paper, we describe how the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has a track record of success in the fission space thanks to the attributes listed above, can be leveraged in the fusion energy field. We highlight key successes of the MOOSE application in the fission space and describe how MOOSE has been and is being applied to fusion applications in the United States---e.g., Tritium Migration Analysis Program, version 8 (TMAP8), MOOSE Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX)---and the United Kingdom---e.g., AURORA, Achlys, Apollo. These efforts aim to establish a suite of tools that can be further extended to accelerate fusion energy deployment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Combined-Accelerated Stress Testing of Photovoltaic Materials

By applying multiple environmental stresses in fieldrepresentative combinations and sequences, combined-accelerated stress testing (CAST) identifies degradation modes and failure mechanisms of photovoltaic (PV) modules and components that are missed by single stress factor accelerated testing.

14 SOLAR ENERGY↗
DOE OSTI2022

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it remains notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes the first GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs' inference efficiency. Specifically, on the algorithm level, GCoD integrates a divide and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently outperforms state-of-the-art designs in terms of accelerator efficiency while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices to better understand its advantages. All codes and pre-trained models will be released upon acceptance.

You, Haoran↗
DOE OSTI2025

Turbo‐charging crop improvement: harnessing multiplex editing for polygenic trait engineering and beyond

Multiplex CRISPR editing has emerged as a transformative platform for plant genome engineering, enabling the simultaneous targeting of multiple genes, regulatory elements, or chromosomal regions. This approach is effective for dissecting gene family functions, addressing genetic redundancy, engineering polygenic traits, and accelerating trait stacking and de novo domestication. Its applications now extend beyond standard gene knockouts to include epigenetic and transcriptional regulation, chromosomal engineering, and transgene‐free editing. These capabilities are advancing crop improvement not only in annual species but also in more complex systems such as polyploids, undomesticated wild relatives, and species with long generation times. At the same time, multiplex editing presents technical challenges, including complex construct design and the need for robust, scalable mutation detection. We discuss current toolkits and recent innovations in vector architecture, such as promoter and scaffold engineering, that streamline workflows and enhance editing efficiency. High‐throughput sequencing technologies, including long‐read platforms, are improving the resolution of complex editing outcomes such as structural rearrangements—often missed by standard genotyping—when targeting repetitive or tandemly spaced loci. To fully realize the potential of multiplex genome engineering, there is growing demand for user‐friendly, synthetic biology‐compatible, and scalable computational workflows for gRNA design, construct assembly, and mutation analysis. Experimentally validated inducible or tissue‐specific promoters are also highly desirable for achieving spatiotemporal control. As these tools continue to evolve, multiplex CRISPR editing is poised to become a foundational technology of next‐generation crop improvement to address challenges in agriculture, sustainability, and climate resilience.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication.

There is a growing interest in custom spatial accelerators for machine learning applications. These accelerators employ a spatial array of processing elements (PEs) interacting via custom buffer hierarchies and networks-on-chip. The efficiency of these accelerators comes from employing optimized dataflow (i.e., spatial/temporal partitioning of data across the PEs and fine-grained scheduling) strategies to optimize data reuse. The focus of this work is to evaluate these accelerator architectures using a tiled general matrix-matrix multiplication (GEMM) kernel. To do so, we develop a framework that finds optimized mappings (dataflow and tile sizes) for a tiled GEMM for a given spatial accelerator and workload combination, leveraging an analytical cost model for runtime and energy. Finally, our evaluations over five spatial accelerators demonstrate that the tiled GEMM mappings systematically generated by our framework achieve high performance on various GEMM workloads and accelerators.

42 ENGINEERING↗
DOE OSTI2025

Thermophilic site-specific recombination system for rapid insertion of heterologous DNA into the Clostridium thermocellum chromosome

Clostridium thermocellum is an anaerobic thermophile capable of producing ethanol and other commodity chemicals from lignocellulosic biomass. The insertion of heterologous DNA into the C. thermocellum chromosome is currently achieved via a time-consuming homologous recombination process, where a single stable insertion can take 2–4 weeks or more to construct. In this work, we developed a thermostable version of the Serine recombinase Assisted Genome Engineering (tSAGE) approach for gene insertion in C. thermocellum utilizing a site-specific recombinase from Geobacillus sp. Y412MC61, enabling quick and easy insertion of DNA into the chromosome for accelerated genetic tool screening and heterologous gene expression. Using tSAGE, chromosomal insertion of plasmid DNA occurred at a maximum transformation efficiency of 5 × 10 3 CFU/µg, which is comparable to the transformation efficiency of a replicating control plasmid in C. thermocellum. Using tSAGE, we chromosomally integrated and characterized 17 reporter genes, 15 homologous and 31 heterologous constitutive promoters of varying strengths, 4 inducible promoters, and 5 riboswitches in C. thermocellum. We also determined that a 6–7 nucleotide gap between the ribosome binding site (RBS) and the start codon is optimal for high expression by employing a library of superfolder green fluorescent protein expression constructs driven by our strongest tested promoter (P clo1313_1194 ) with different distances between the RBS and start codon. The tools developed here will aid in accelerating C. thermocellum strain engineering for producing sustainable fuels and chemicals directly from plant biomass.

Biofuels↗
DOE OSTI2020

Material-Dependent Antagonistic Effects between Soot and ZDDP

While soot in engine oil is known to accelerate the wear of diesel and gasoline direct-injection compression-ignition engines, there is a lack of consensus on the wear mechanism though various hypotheses have been proposed in the literature. Particularly, some recently observed antagonistic effects between soot and a common lubricant antiwear additive, zinc dialkyldithiophosphate (ZDDP), while others did not. The discrepancy is, in part, explained by the strong alloy dependence of such antagonism discovered in this study. Specifically, four alloys, 52100 steel and M2, M50, and A2 tool steels, are tested in lubricants containing carbon black (CB, a soot surrogate) with and without ZDDP present. Adding the CB alone to the oil increases the wear rate for all steel alloys as expected. However, distinct wear performance is observed for the four steel alloys when ZDDP is introduced to the CB-containing oil: while the 52100 steel has notable wear reduction, the three tool steels suffer significant wear increase. Comprehensive tribofilm characterization suggests that the Mo content in the steel alloy and the sulfur from ZDDP strongly influence wear behavior. The combination of CB/Mo-catalyzed sulfidation and CB-accelerated abrasion is hypothesized to be responsible for the high wear of the Mo-alloyed tool steels.

36 MATERIALS SCIENCE↗