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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accelerating Engineered Microbe Optimization through Machine Learning and Multi-Omics Datasets [Abstract]

The Agile Biofoundry (ABF) is a multi-national lab consortium funded by the DOE Bioenergy Technologies Office that has developed a biofoundry enabling the rapid deployment of bioproducts into the market. The ABF is a flexible platform that can adjust to the needs of numerous government, academic and industrial partners, thus enabling them to rapidly develop and optimize the production of a wide range of bioproducts. To demonstrate this capability, three ABF labs (NTESS, LBNL, and PNNL) have collaborated with the biomanufacturer Lygos, Inc. to use the ABF to demonstrate a high-throughput Design-Build-Test-Learn (DBTL) engineering cycle incorporating multi-omics analysis and machine learning with best in industry cycle times.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Time Resolved Reflectivity Measurements of Convective Clouds

National Aeronautics and Space Administration's Investigations of Convective Updrafts (INCUS) mission aims to document convective mass flux through changes in the radar reflectivity (ΔZ) in convective cores captured by a constellation of three Ka-band radars sampling the same convective cells over intervals of 30, 90, and 120 s. Here, high spatiotemporal resolution observations of convective cores from surface-based radars that use agile sampling techniques are used to evaluate aspects of the INCUS measurement approach using real observations. Analysis of several convective cells confirms that large coherent ΔZ structure with measurable signal (>5 dB) can occur in less than 30 s and are correlated with underlying convective motions. The analysis indicates that the INCUS mission radar footprint and along track sampling are adequate to capture most of the desirable ΔZ signals. This unique demonstration of reflectivity time-lapse provides the framework for estimating convective mass flux independent from Doppler techniques with future radar observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Powder metallurgy hot isostatic pressing - workshop report

The findings of the powder metallurgy (PM) hot isostatic pressing (HIP) workshop confirmed that the accelerated adoption of this technology requires the following action items: 1. By 2035, the HIP infrastructure must be scaled to sizes larger than 4 .0 meters for manufacturing components relevant to energy and defense industries at large. 2. The community must develop cost-effective and agile standards, qualifications, and certification pathways for these HIP parts. 3. At the same time, domestic powder production should be expanded to a wide range of metallic alloys, including Cr-Mo steels, stainless steels, and titanium- and nickel-based superalloys, as well as to emerging alloy classes in the refractory space. 4. The above initiatives can be enhanced by implementing digital modeling and controls, including AI-driven simulations, in situ monitoring, and digital twins.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sustainable Triacetic Acid Lactone Production from Sugarcane by Fermentation and Crystallization

Triacetic acid lactone (TAL) has the potential to serve as a bioderived platform chemical for commercial products including sorbic acid and recyclable polydiketoenamine plastics. In this study, we leveraged BioSTEAM to design, simulate, and evaluate (via techno-economic analysis, TEA, and life cycle assessment, LCA) TAL production from sugarcane. We experimentally characterized TAL solubility, calibrated solubility models, and designed a process to separate TAL from fermentation broths by crystallization. The biorefinery could produce TAL at a minimum product selling price (MPSP) of $\$3.73$–5.86·kg –1 (5th–95th percentiles; baseline at $\$4.60$·kg –1 ) and a carbon intensity (CI) of 5.31 [2.60–8.71] kg CO 2 -eq·kg –1 , which could enable financially viable, low-CI production of sorbic acid and polydiketoenamines. To drive down costs and CI, we explored the theoretical fermentation space (titer, yield, productivity combinations), operation scheduling and capacity expansion strategies (e.g., integrated sorghum processing), and potential separation improvements (mitigating TAL loss through pH control). Advancements in key design and technological parameters could further reduce MPSP by 51% to $\$2.26$·kg –1 [$\$1.97$–2.80·kg –1 ] and CI by 43% to 3.05 [1.91–4.15] kg CO 2 -eq·kg –1 . This research highlights the ability of agile TEA-LCA to screen promising designs, navigate sustainability trade-offs, prioritize research needs, and chart quantitative roadmaps to advance bioproducts and biofuels.

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

Carbon optimized production of 3-hydroxypropionic acid in the Agile BioFoundry organism Rhodosporidium toruloides

The modern global economy relies heavily on carbon-based products that are derived from petroleum, which presents sustainability, resource management, and greenhouse gas exacerbated climate change challenges. Due to these challenges, there is the need for a global industrial transition towards green and sustainable production. Microbial production of valuable chemicals from renewable biomass represents one promising route. However, high-volume low-value products such as commodity chemicals are still difficult to make profitable. One fundamental bottleneck is a waste of more than 1/3 of the feedstock carbon as CO 2 in the fermentation process. Here the project focuses on fundamentally reconfiguring the metabolism to reduce CO 2 loss in central metabolic pathways thereby also improving bioproduct yields. Here we present technologies to prevent CO 2 loss and balance reducing equivalents within the cell to enable complete conversion of glucose from renewable feedstocks into bioproducts.

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

Continuous multimodal technologies in industrial microbiology: potential for achieving high process performance and agility

The field of industrial microbiology has great potential to tackle the most pressing challenges facing society, but today, this entails much more than classical fermentation technology followed by product upgrading. Methods are needed to confer unit-operation-level functionality and whole-system-level robustness, as well as flexibility. As examples of this concept, we focus here on methodologies often deployed at industrial scale as discrete processes—such as biological, chemical, and electrochemical unit operations—that are also capable of substantial complementarity when combined sequentially and operated continuously. A key and relatively new type of conversion step is made possible by catalyst immobilization methods at both the enzymatic and cellular level. Cases drawn from electro-fermentation for acetoin production and syngas-mediated microbial electrosynthesis of ethanol exemplify the synergy of combined operational approaches. Because modular processes can be treated as a series of reactor elements, considerable flexibility is possible. As discussed, however, not all processes are amenable to this intensification.

09 BIOMASS FUELS↗
DOE OSTI2021

Developing Multi-Gene CRISPRa/I Programs to Accelerate DBTL Cycles in ABF Hosts Engineered for Chemical Production (CRADA 468)

Bacterial metabolism is comprised of large and complex gene networks that can produce valuable chemical products. Sophisticated organism engineering efforts are required to optimize production of high-value compounds from these networks. In principle, synthetic multi-gene transcriptional programs could be constructed to reengineer these networks for efficient industrial chemical production. In practice, however, our incomplete ability to understand and model the underlying networks, combined with our limited ability to predictably control the expression of multiple genes makes achieving this goal difficult. To overcome these challenges, we will combine new CRISPR-Cas multi-gene expression programs with computational modeling, machine learning, and multi-omics data to enhance the efficacy of design-build-test-learn (DBTL) cycles. For industrially promising microorganisms in early stages of development, creating technologies for rapidly engineering complex multi-gene programs could be transformative for accelerating data- and model-driven strain design. New CRISPR-Cas tools allow programmable gene activation (CRISPRa) or repression (CRISPRi) at multiple genes simultaneously, using the catalytically inactive Cas9 protein (dCas9) with guide RNAs that recognize DNA targets through predictable Watson-Crick base pairing. To enable accelerated DBTL cycles, we will combine these technologies with advanced Agile BioFoundry (ABF) capabilities for multi-omics data collection and machine learning. We will demonstrate the immediate applicability of these tools by rapidly improving the production of an industrial aromatic in multiple ABF organisms. We recently identified and optimized new transcriptional activators that can be linked to programmable CRISPR-Cas DNA binding domains to activate gene expression in E. coli. We can now use these CRISPRa tools as generalizable trans-acting regulators for combinatorial multi-gene expression tuning that can be easily transferred to new pathways and networks without additional genome engineering. We anticipate these tools will also transfer to new hosts. We have recently found that CRISPRa systems developed in E. coli can be readily ported to Pseudomonas putida, suggesting that multi-gene CRISPRa/i programs for diverse ABF organisms may be within reach.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Application of Machine Learning to Improve Biobased Glucaric Acid Production (CRADA 504)

The Agile Biofoundry (ABF) is a multi-national lab consortium funded by the DOE Bioenergy Technologies Office that has developed a biofoundry that enables the rapid deployment of bioproducts into the market. Working with four ABF laboratory members (National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory (ANL)), Kalion will use advanced high throughput techniques, advanced analytics, and machine learning to optimize the production environment for glucaric acid. Low-cost, high-purity glucaric acid enables products in a wide range of fields to improve the chemical properties of products and applications ranging from water treatment, polymers & textiles, coatings, detergents, and pharmaceuticals. With its broad range of applications, glucaric acid has the potential to demonstrate how such bio-based materials offer great benefit to the US economy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Tailoring thermal insulation architectures from additive manufacturing

Abstract Tailoring thermal transport by structural parameters could result in mechanically fragile and brittle networks. An indispensable goal is to design hierarchical architecture materials that combine thermal and mechanical properties in a continuous and cohesive network. A promising strategy to create such a hierarchical network targets additive manufacturing of hybrid porous voxels at nanoscale. Here we describe the convergence of agile additive manufacturing of porous hybrid voxels to tailor hierarchically and mechanically tunable objects. In one strategy, the uniformly distributed porous silica voxels, which form the basis for the control of thermal transport, are non-covalently interfaced with polymeric networks, yielding hierarchic super-elastic architectures with thermal insulation properties. Another additive strategy for achieving mechanical strength involves the versatile orthogonal surface hybridization of porous silica voxels retains its low thermal conductivity of 19.1 mW m −1 K −1 , flexible compressive recovery strain (85%), and tailored mechanical strength from 71.6 kPa to 1.5 MPa. The printed lightweight high-fidelity objects promise thermal aging mitigation for lithium-ion batteries, providing a thermal management pathway using 3D printed silica objects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Metabolomics (PB-DP5)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Culture samples were collected at 0, 0.5, 1, 2, 4, 6, and 8 hours for extracellular sucrose analysis. Circadian metabolomics data was acquired using a Agilent single quadrupole gas chromatography-mass spectrometer and processed using Agilent Mass Hunter for targeted sucrose quantification. Metabolomic analysis of PCC 7942 light-dark cycle cultures transitioned to constant light revealed distinct temporal patterns in sucrose production. Processed metabolomic datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed GC-MS results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Multi-fidelity thermal modeling of laser powder bed additive manufacturing

Laser powder bed fusion (LPBF) Additive manufacturing (AM) has attracted interest as an agile method of building production metal parts to reduce design-build-test cycle times for systems. However, predicting part performance is difficult due to inherent process variabilities. This makes qualification challenging. Computational process models have attempted to address some of these challenges, including mesoscale, full physics models and reduced fidelity conduction models. The goal of this work is credible multi-fidelity modeling of the LPBF process by investigating methods for estimating the error between models of two different fidelities. Two methods of error estimation are investigated, adjoint-based error estimation and Bayesian calibration. Adjoint-based error estimation is found to effectively bounding the error between the two models, but with very conservative bounds, making predictions highly uncertain. Bayesian parameter calibration applied to conduction model heat source parameters is found to effectively bound the observed error between the models for melt pool morphology quantities of interest. However, the calibrations do not effectively bound the error in heat distribution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Can protein expression be ‘solved’?

Recombinant protein expression is central to biotechnology’s application in academic exploration as well as human health, climate applications and the bioeconomy in general. However, not all proteins can be expressed in all organisms, and the field lacks a predictive model of soluble protein overexpression that could replace laborious experimental trial-and-error. Here, we discuss the state of the field and identify the lack of large, high-fidelity datasets as the primary bottleneck to progress. We review possible assays that could be used for data collection to identify a path toward an extensible experimental platform for collecting soluble recombinant protein overexpression data across organisms. We suggest that the resulting dataset should be used to train increasingly generalizable predictive models of protein expression to answer the question: “How can predictive protein expression be solved?”.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accelerating engineered microbe optimization through machine learning and multi-omics datasets

This project demonstrated the use of a combination of multi-omics data with deep learning and a high-throughput Design- Build-Test-Learn (DBTL) cycle to improve the production of malonic acid, a versatile product with a large market. The project leveraged the unique capabilities of both Lygos and the Agile BioFoundry (ABF): Lygos provided its expertise efficiently designing, building, and cultivating P. kudriavzevii strains; LBNL, PNNL, and NTESS provided multi-omics analysis in the Test phase, LBNL provided machine learning techniques in the Learn phase to analyze the -omics datasets and make recommendations so as to increase malonic acid production in the next DBTL cycle. This project is the first to use large amounts of multi-omics time-series data to feed deep learning models, creating around 80,000 data points in a single DBTL cycle. This project has 1) demonstrated the utility of combining deep learning and multi-omics data sets by improving the production of malonic acid two fold, 2) created a large time-series datasets to be released publicly for external development of new machine learning algorithms, and 3) shown that supply chain problems, strain building bottlenecks, and adaptation times for new ML approaches are key obstacles for fast DBTL cycle times.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ongoing Cooperative Engagement Facilitates Agile Pandemic and Outbreak Response: Lessons Learned Through Cooperative Engagement Between Uganda and the United States

Pathogens threaten human lives and disrupt economies around the world. This has been clearly illustrated by the current COVID-19 pandemic and outbreaks in livestock and food crops. Here, to manage pathogen emergence and spread, cooperative engagement programs develop and strengthen biosafety, biosecurity, and biosurveillance capabilities among local researchers to detect pathogens. In this case study, we describe the efforts of a collaboration between the Los Alamos National Laboratory and the Uganda Virus Research Institute, the primary viral diagnostic laboratory in Uganda, to implement and ensure the sustainability of sequencing for biosurveillance. We describe the process of establishing this capability along with the lessons learned from both sides of the partnership to inform future cooperative engagement efforts in low- and middle-income countries. We found that by strengthening sequencing capabilities at the Uganda Virus Research Institute before the COVID-19 pandemic, the institute was able to successfully sequence SARS-CoV-2 samples and provide data to the scientific community. We highlight the need to strengthen and sustain capabilities through in-country training, collaborative research projects, and trust.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Functional properties of Yttrium Iron Garnett thin films on graphene-coated Gd 3 Ga 5 O 12 for remote epitaxial transfer

Remote epitaxial growth via a graphene interlayer and subsequent mechanical exfoliation of a free-standing membrane is a recently developed technique used to transfer complex oxide thin films onto non-native substrates to form heterogeneously integrated structures for various device applications. One such oxide is Yttrium Iron Garnet (YIG), a material of choice for a wide range of magnetoelectric and spintronic devices owing to its strong magnetic properties and low microwave losses. YIG is predominantly grown on lattice matched Gadolinium Gallium Garnet (GGG) substrates, but by utilizing the remote epitaxy technique, high quality YIG films can be transferred from GGG onto another substrate such as piezoelectric Lithium Niobate (LN). Mechanical strain coupling between the layers and magnetostrictive nature of YIG would allow for the investigation of the interplay in YIG/LN structures leading to the design of novel frequency agile magneto-acoustic devices. In this study functional properties of a YIG film grown using PLD on graphene-coated GGG substrate were investigated and compared to traditional YIG on GGG. Both materials were characterized in terms of crystal structure, surface morphology, FMR and Gilbert damping, and Raman and XAS spectroscopy. Further, it was found that YIG on graphene-coated GGG exhibits significantly higher microwave losses than standard YIG on GGG (FMR linewidth 30.9 vs 2.1 Oe at 10 GHz, and Gilbert damping coefficient 15.4x10 -4 vs 3.4x10 -4 respectively), which was attributed to increased concentration of Fe 2+ cations in YIG/Graphene/GGG. While the damping is higher in these studied films compared to YIG grown directly on GGG, the resulting properties are still very favorable compared to many other competing materials which can be grown without the need for lattice matched substrates, such as metallic ferromagnets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Gas‐Phase Ion‐Molecule Interactions in a Collision Reaction Cell with ICP‐MS / MS : Investigations with CO 2 as the Reaction Gas

Carbon dioxide (CO 2 ) was used as a reaction gas to investigate the gas‐phase ion‐molecule interactions using the Agilent 8900 ICP‐MS/MS. A solution containing forty‐five elements representative of the periodic table was used to supply the ions to react with CO 2 in the collision/reaction cell (CRC). The only significant product ions formed were monoxides. The general reactivity was shown to be consistent with density functional theory (DFT)‐predicted reaction enthalpies, such that all predicted exothermic reactions produced product ions at levels of at least 1% of the unreacted ion. Most endothermic reactions observed had sufficient kinetic energy in excess of the reaction enthalpies. Our results suggest that reaction enthalpy is a reasonable predictor of reactivity with CO 2 on the timescales of the interactions in non‐thermal ICP‐MS/MS systems. The ease and rapidity of data collection with the ICP‐MS/MS and DFT calculations using the NWChem suite has value given the scarcity of thermochemical data of CO 2 reactions in the literature. These studies are especially useful for the identification of targeted reaction chemistries to be leveraged for analytical method development, such as for the inline separation of isobaric interferences from analytes of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluating the performance of a high resolution SLIM IM module integrated with an Agilent 6560 IM-QTOF MS - CRADA 413 (Final Report)

Developing new technologies that enhance our abilities to identify molecular signatures is important for the health of the public, the environment, and national security. Deploying these novel technologies requires multiple stages of validation and evaluation. This project focused on evaluating a technology developed at PNNL named Structures for Lossless Ion Manipulations (SLIM) in the context of a commercially available ion mobility mass spectrometer instrument from Agilent Technologies. There is a benefit to integrating the recently developed SLIM module into a real-world instrument platform. Data from the combined instrument will provide new standards for characterizing samples using ion mobility. These standards, related to both the drift time of ions and their resulting collisional cross sections (CCS) will be of value in characterizing samples from both humans and environmental samples, such as soil microbiomes. Because no previous instrument has been designed and implemented to simultaneously analyze the same sample in both constant and oscillating drift fields, the project will provide a new research capability that will support research in a wide variety of applications. The evaluation approach included measuring the resolving power, mass range, sensitivity, and accuracy of two SLIM variations (constant and oscillating fields). Although SLIM performed similarly to the commercial instrument in terms of sensitivity, mass range, and accuracy the resolving power of SLIM far exceeded that of the commercially-available instrument. This validation will provide critically useful data to our commercial partners which they can use to reach informed decisions on the advantages of the SLIM technology such as the outstanding performance and the low cost of manufacturing.

42 ENGINEERING↗
DOE OSTI2025

MCPC Characterization Round 1

Characterization data for the MCPC LDRD Agile investment is collected in multiple rounds. Contained herein is data from Round 1 in which eleven distinct samples and one control sample are characterized by five modalities: EBSD, HARDNESS, OPTICAL, SEM, and ULTRASONIC. Data is organized in subdirectories for the five modalities and each one modality containing subdirectories for the datasets and other information.

36 MATERIALS SCIENCE↗