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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Degradation science: Integrating modeling and experiments to predict localized corrosion processes (Annual Progress Report)

Additively manufactured (AM) eutectic high-entropy alloys (EHEAs), such as nano-lamellar AlCoCrFeNi 2.1 , have excellent strength, ductility, and wear resistance even at elevated temperatures, but their corrosion behavior in aggressive acids at different length scales remain poorly understood. This work investigates the corrosion behavior of laser powder bed–fused (L-PBF) AlCoCrFeNi 2.1 as a function of annealing temperatures, probing degradation mechanisms from nanoscopic to macroscopic length scales. The alloy is dual phase consisting of a ductile FCC L1 2 phase and a high-strength BCC B2 phase. Rapid solidification during L-PBF produces a far-from-equilibrium nano-lamellar structure with nearly homogeneous elemental distribution, which tends to evolve upon annealing toward Cr/Co/Fe-enriched FCC and Al/Ni-enriched B2. Three conditions were studied: as-printed, 600 °C/5 h, and 1000 °C/1 h, over which B2 lamellae coarsen, lamellar spacing increases, and elemental segregation becomes more prominent. Microstructure and chemistry were characterized by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), while in-situ electrochemical atomic force microscopy (EC-AFM) was used to link early (<5 h) local dissolution to microstructure after exposure in sulfuric acid. EC-AFM highlights preferential dissolution of the BCC/B2 phase where the surrounding matrix is Cr-depleted and directly quantifies the dissolution rates within individual phases, tracks the transition from early nano-scale attack to partial repassivation, to correlate height differences with current and impedance responses. To monitor longer-term behavior (up to 96 h), ex-situ AFM, SEM, and confocal imaging were combined with conventional bulk electrochemical tests, bridging nanoscale observations to micro/meso-scale damage morphologies. At the meso-scale, the deepest dissolution channels align with the build-direction lamellae and melt-pool boundaries, indicating that printing directionality guides the propagation of these localized corrosion sites. Annealing modifies corrosion by restructuring BCC/FCC phase fractions, lamellar spacing, and Cr/Al segregation, thereby changing the cathode/anode ratio and passive film stability. The results clarify how as-printed and annealed nano-lamellar architectures differ in their susceptibility to selective dissolution; how elemental segregation competes with residual stresses along the build direction. With these insights, future work will use CALPHAD-guided alloy modification to stabilize higher Cr contents in the B2 phase while retaining a dominant FCC+B2/BCC microstructure, with the goal of designing mechanically robust, corrosion-resistant EHEAs for safety-critical applications to leverage the LLNL’s broader national and global security mission.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Life Cycle Inventories and Data Gap Analysis for Rare Earth Elements: Neodymium and Dysprosium from Mining to Magnets

The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Mechanical Characterization of the NIF Ignition Target TMPSA Bonding Flexure

The Thermo-Mechanical Package Sub-Assembly (TMPSA) provides a critical mechanical, thermal, and electrical interface between a silicon arm and a TMP aluminum can in a NIF ignition target. During assembly, sixteen silicon pads are bonded to the aluminum using a fixture that positions the components and applies a repeatable prescribed displacement through a compliant flexure. The flexure converts fixture interference into displacement and reaction force. Because bondline thickness variation must be maintained within +/-4 µm, consistent flexure behavior is important to the assembly process. With a recent string of TMPSAs exhibiting low bond strength, the flexures were inspected. Despite being manufactured to the same specifications, flexures were found to exhibit variation in measured stiffness. Additionally, the measured stiffness values did not always follow the presumed beam mechanics model. This work addresses two questions: whether the bonding fixture is working as intended, and whether the previously made stiffness measurements are accurate representations of the use case. The flexures are analyzed using Euler-Bernoulli beam theory, measured beam dimensions, finite element analysis, and tolerance stack-up calculations. The analysis shows that the fixed-guided beam mechanics model appropriately represents the flexure during TMPSA bonding, but the chisel-tip stiffness measurement method introduces a deformation to the inner ring of the flexure that is not represented during use. The measured stiffness values should therefore be interpreted as test-condition stiffness values rather than direct measurements of operational flexure stiffness. The discrepancy is therefore attributed primarily to the measurement boundary condition rather than to failure of the fixed-guided beam model. Recommendations are provided for GD&T, dimensional inspection, and a representative stiffness testing method to better control bondline variation.

42 ENGINEERING↗
DOE OSTI2025

Cyber-Informed Engineering (CIE) Workbook: End-of-Train (EoT) / Head-of-Train (HoT) Communications

This workbook presents a vulnerability (CVE-2025-1727 ) found in train applications and guides a digital risk assessment and mitigation analysis and application of Cyber-Informed Engineering principles to mitigate the potential consequences and ultimately the hazard through the engineering discipline because of exploiting this vulnerability. Workshop participants are encouraged to use the workbook to capture insights and lessons learned. The workbook guides the participant to: • Understand the HE communication vulnerability • Map digital threats to physical consequences • Use bowtie analysis to illustrate both “security” and “engineering” barriers • Apply CIE principles to ensure that even if communications are compromised, the physical engineered system still behaves safely. • Produce an actionable set of engineered and infosec controls for implementation

42 - ENGINEERING↗
DOE OSTI2026

Purpose, Design, and Analysis of the NSTX-U Centerstack Casing Lateral Support Shims

The National Spherical Torus Experiment Upgrade (NSTX-U) centerstack casing surrounds the central magnet core of NSTXU. It provides the inner vacuum boundary of the plasma chamber and supports the radially inboard plasma facing components lining the plasma chamber. The casing is supported like a cantilever, fixed at the lower flange by a connection to the TF Bundle through a G-10 ring. It is flexibly supported at the top by bellows, and insulated shims intended to react the largest part of the inventory of the disruption loads at the upper support, thereby protecting the upper and lower connections from excessive loads. A large part of the qualification of the casing and tiles requires addressing and quantifying disruption loads. These have been estimated based on extensions of the behavior of NSTX during its run period roughly 15 years ago. Structural qualification of the Upgrade used these estimates of the loading as input to calculations to demonstrate adequacy for a “most probable” disruption. The estimates are included in a requirements document to guide conservative design of all components loaded by disruptions. However, the changes made for the upgrade made the extrapolation of disruption behavior uncertain. Disruptions include axisymmetric loading from axisymmetric currents induced in the vessel shells. In addition, more complex currents called Halo currents are a result of plasma poloidal currents impinging on the casing. The halo currents add another layer of uncertain behavior and net lateral loading to the casing. As a result, NSTX-U is equipped with a number of instruments intended to assess the nature and severity of disruptions. The casing shims are required for lateral support of the casing, but these have also been converted to load cells to provide another measure of the magnitude of symmetric and asymmetric loading. Some of the logic in the development of the requirements document are presented. Electromagnetic analysis of axisymmetric and halo loading is described. Global simulations of the casing transient dynamic response, and local analysis and testing of the shim/load cell are included. The shim load cells and other instrumentation will be an important guide in planning NSTX-U experiments beyond its next run period expected in late 2026 or early 2027.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI1991

Pressure Safety Training (Rev. 8)

This is the workbook for the Pressure Safety Training Course. It is intended as a reference manual and guide for all work with pressurized liquids or gases. This workbook contains basic references to make work with pressure safer. It is intended to supplement classroom instruction, rather than serve as a definitive text on pressure. Earlier versions of the manual were intended specifically for training at Lawrence Livermore National Laboratory. This revision is a generic version intended for training at all DOE facilities. The information in the Standards chapter is from the LLNL Health and Safety Manual. It is included here as a convenient reference and guide for developing similar standards at your own facility.

42 ENGINEERING↗
DOE OSTI2023

Workflow for High-throughput Screening of Enzyme Mutant Libraries Using Matrix-assisted Laser Desorption/Ionization Mass Spectrometry Analysis of Escherichia coli Colonies

High-throughput molecular screening of microbial colonies and DNA libraries are critical procedures that enable applications such as directed evolution, functional genomics, microbial identification, and creation of engineered microbial strains to produce high-value molecules. A promising chemical screening approach is the measurement of products directly from microbial colonies via optically guided matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Measuring the compounds from microbial colonies bypasses liquid culture with a screen that takes approximately 5 s per sample. We describe a protocol combining a dedicated informatics pipeline and sample preparation method that can prepare up to 3,000 colonies in under 3 h. The screening protocol starts from colonies grown on Petri dishes and then transferred onto MALDI plates via imprinting. The target plate with the colonies is imaged by a flatbed scanner and the colonies are located via custom software. The target plate is coated with MALDI matrix, MALDI-MS analyzes the colony locations, and data analysis enables the determination of colonies with the desired biochemical properties. This workflow screens thousands of colonies per day without requiring additional automation. The wide chemical coverage and the high sensitivity of MALDI-MS enable diverse screening projects such as modifying enzymes and functional genomics surveys of gene activation/inhibition libraries.

Choe, Kisurb↗
DOE OSTI2023

SPRUCE Sphagnum Phytobiome Responses to Whole Ecosystem Warming and Elevated Atmospheric CO2 in July, 2017-2021

This dataset reports the carbon (C) and nitrogen (N) isotopic composition of Sphagnum moss tissue collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Tissues were collected inside the SPRUCE experimental study plot, where air and peat warming are combined in whole-ecosystem warming treatments, from 2019-2021. Additionally, rates of nitrogen fixation and methane oxidation measured for Sphagnum from 2017, 2019, and 2021. Sphagnum tissue % water content was measured at all timepoints. This dataset was used to conduct analyses within Petro et al (2023). Sphagnum C and N concentrations and 13C and 15N isotopic natural abundance were determined for three replicate Sphagnum tissue samples collected from inside the SPRUCE enclosures in July 2019-2021. Elemental and isotope analyses were conducted at the University of Georgia – Center for Applied Isotope Studies (CAIS; https://cais.uga.edu/). Plant elemental analysis was performed by the micro-Dumas method, while isotopic natural abundance was measured by isotope ratio mass spectrometry. 13C natural abundance is expressed as the per mille (‰) deviation from the Pee Dee Belemnite standard (PDB) 13C:12C ratio (δ13C), while 15N natural abundance is expressed as the ‰ deviation from the N2 atmospheric 15N:14N ratio (δ15N). Rates of nitrogen fixation and methane oxidation were measured using serum bottle incubations performed with Sphagnum collected inside the SPRUCE enclosures in July 2017, 2019, and 2021. Rates were calculated according to the amount of 15N-N2 or 13C-CH4 (methane) incorporated into the Sphagnum tissue or incubation headspace over the 48-hour intion period. In 2017, incubations were performed in growth chambers set to the temperature of the experimental plots during Sphagnum sampling. In 2019 and 2021, the incubations were performed directly inside the experimental plots. The 13C and 15N isotopic natural abundance data and rate measurements presented in this dataset provide insight into the impacts of whole-ecosystem warming and elevated atmospheric carbon dioxide (CO2) on N and C cycling within the Sphagnum phytobiome. This dataset contains two data files in comma-separate values (*.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format. Additional datasets on NH4-N availability, porewater concentrations of CH4 and CO2, and Sphagnum groundcover within the SPRUCE experimental plots are available. These are outlined in the Related Datasets section in the user guide.

delta 13 carbon↗
DOE OSTI2024

SPRUCE Quantitative PCR (qPCR) of Microbial Gene Copy Numbers, 2021-2022

This dataset provides the results for quantitative polymerase chain reaction (qPCR) of peat samples collected from ambient and experimental plots in the Spruce and Peatland Responses Under Climatic and Environmental Change (SPRUCE) experiment site in June and August of 2021, and June of 2022. SPRUCE is located within the Marcell Experimental Forest in northern Minnesota, USA. The dataset includes bacterial, archaeal, fungal gene copy numbers, along with corresponding logarithmic values, at 11 depth increments of two-meter deep peat cores taken from 12 sampling sites locations inside SPRUCE plots (10 chambered and 2 ambient plots). The sampling, sample prep and analysis followed standard methods outlined in prior publications (Wilson et al. 2016; Kluber et al. 2020) except that a higher yielding Omega Bio-Tek Mag-Bind Environmental DNA 96 Kit was used for extractions and DNA was quantified using Qubit dsDNA High Sensitivity Assay Kit. qPCR subsamples of peat cores from the SPRUCE plots characterize changes in the abundance and composition of microbial communities of peat seasonally showing how composition varies under multiple levels of experimental peat warming and atmospheric CO2 concentrations. This dataset contains one data file in comma-separated values (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separated values (.csv) format and a user guide in PDF (*.pdf) format. On 2026-07-07 this dataset was updated to add three columns to the data file: ‘Fungal_copy_dry’, ‘Log_fungal_copy_dry’, ‘Fungal_copy_wet’. No previously released data values were altered. Additionally, the abstract, data dictionary, and user guide were updated, and a file-level metadata file was added.

Archaea↗
DOE OSTI2023

Enhancing isoprenol production by systematically tuning metabolic pathways using CRISPR interference in E. coli

Regulation of metabolic gene expression is crucial for maximizing bioproduction titers. Recent engineering tools including CRISPR/Cas9, CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) have enabled effective knock-out, knock-down, and overexpression of endogenous pathway genes, respectively, for advanced strain engineering. CRISPRi in particular has emerged as a powerful tool for gene repression through the use of a deactivated Cas9 (dCas9) protein and target guide RNA (gRNA). By constructing gRNA arrays, CRISPRi has the capacity for multiplexed gene downregulation across multiple orthogonal pathways for enhanced bioproduction titers. In this study, we harnessed CRISPRi to downregulate 32 essential and non-essential genes in E. coli strains heterologously expressing either the original mevalonate pathway or isopentenyl diphosphate (IPP) bypass pathway for isoprenol biosynthesis. Isoprenol remains a candidate bioproduct both as a drop-in blend additive and as a precursor for the high-performance sustainable aviation fuel, 1,4-dimethylcyclooctane (DMCO). Of the 32 gRNAs targeting genes associated with isoprenol biosynthesis, a subset was found to vastly improve product titers. Construction of a multiplexed gRNA library based on single guide RNA (sgRNA) performance enabled simultaneous gene repression, yielding a 3 to 4.5-fold increase in isoprenol titer (1.82 ± 0.19 g/L) on M9-MOPS minimal medium. We then scaled the best performing CRISPRi strain to 2-L fed-batch cultivation and demonstrated translatable titer improvements, ultimately obtaining 12.4 ± 1.3 g/L isoprenol. Our strategy further establishes CRISPRi as a powerful tool for tuning metabolic flux in production hosts and that titer improvements are readily scalable with potential for applications in industrial bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Spaceflight-Associated Changes of snoRNAs in Peripheral Blood Mononuclear Cells and Plasma Exosomes—A Pilot Study

During spaceflight, astronauts are exposed to various physiological and psychological stressors that have been associated with adverse health effects. Therefore, there is an unmet need to develop novel diagnostic tools to predict early alterations in astronauts’ health. Small nucleolar RNA (snoRNA) is a type of short non-coding RNA (60–300 nucleotides) known to guide 2'-O-methylation (Nm) or pseudouridine (ψ) of ribosomal RNA (rRNA), small nuclear RNA (snRNA), or messenger RNA (mRNA). Emerging evidence suggests that dysregulated snoRNAs may be key players in regulating fundamental cellular mechanisms and in the pathogenesis of cancer, heart, and neurological disease. Therefore, we sought to determine whether the spaceflight-induced snoRNA changes in astronaut’s peripheral blood (PB) plasma extracellular vesicles (PB-EV) and peripheral blood mononuclear cells (PBMCs). Using unbiased small RNA sequencing (sRNAseq), we evaluated changes in PB-EV snoRNA content isolated from astronauts (n = 5/group) who underwent median 12-day long Shuttle missions between 1998 and 2001. Using stringent cutoff (fold change > 2 or log 2 -fold change >1, FDR < 0.05), we detected 21 down-and 9—up-regulated snoRNAs in PB-EVs 3 days after return (R + 3) compared to 10 days before launch (L-10). qPCR validation revealed that SNORA74A was significantly down-regulated at R + 3 compared to L-10. We next determined snoRNA expression levels in astronauts’ PBMCs at R + 3 and L-10 (n = 6/group). qPCR analysis further confirmed a significant increase in SNORA19 and SNORA47 in astronauts’ PBMCs at R + 3 compared to L-10. Notably, many downregulated snoRNA-guided rRNA modifications, including four Nms and five ψs. Our findings revealed that spaceflight induced changes in PB-EV and PBMCs snoRNA expression, thus suggesting snoRNAs may serve as potential novel biomarkers for monitoring astronauts’ health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Carbon neutrality in Malaysia and Kuala Lumpur: Insights from stakeholder-driven integrated assessment modeling

Several cities in Malaysia have established plans to reduce their CO2 emissions, in addition to Malaysia submitting a Nationally Determined Contribution to reduce its carbon intensity (against GDP) by 45% in 2030 compared to 2005. Meeting these emissions reduction goals will require a joint effort between governments, industries, and corporations at different scales and across sectors. In collaboration with national and sub-national stakeholders, we developed and used a global integrated assessment model to explore emissions mitigation pathways in Malaysia and Kuala Lumpur. Guided by current climate action plans, we created a suite of scenarios to reflect uncertainties in policy ambition, level of adoption, and implementation for reaching carbon neutrality. Through iterative engagement with all parties, we refined the scenarios and focus of the analysis to best meet the stakeholders’ needs. We found that Malaysia can reduce its carbon intensity and reach carbon neutrality by 2050, and that action in Kuala Lumpur can play a significant role. Decarbonization of the power sector paired with extensive electrification, energy efficiency improvements in buildings, transportation, and industry, and the use of advanced technologies such as hydrogen and carbon capture and storage will be major drivers to mitigate emissions, with carbon dioxide removal strategies being key to eliminate residual emissions. This study highlights the participatory process in which stakeholders contributed to the development of the model and guided the analysis, as well as insights into Malaysia’s decarbonization potential and the role of multilevel governance.

cities↗
DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2022

Photonic Luminescent Solar Concentrator Design for High Efficiency, Low Cost Multijunction Photovoltaics

Despite the extraordinary advances in solar cell efficiency in laboratory settings, the deployment of solar cells continues to be limited to low efficiency (<25%) silicon cells because of cost. In this work, we take advantage of the extraordinary optical properties afforded by nanophotonic structures to create a photonic luminescent solar concentrator for an InGaP-Si multijunction concentrator cell. Finite difference time domain (FDTD) simulations demonstrated a concentrator that could effectively capture, downconvert, and guide concentrated light to an InGaP subcell while still transmitting longer wavelengths to a Si subcell. We fabricated the photonic luminescent solar concentrator, which was comprised of CdSe/CdS quantum dots embedded within alternating layers of Si 3 N 4 and SiO 2 , and experimentally verified the optical performance, showing a 40% increase in light guiding and a significant reduction in reabsorption losses in the plane of the luminescent concentrator as compared to traditional designs. Finally, we utilized modified detailed balance calculations that accounted for cell and optical losses and showed >30% efficiencies are possible with this design, demonstrating the potential to meet the demands for high efficiency, inexpensive solar modules.

Eisler, Carissa N.↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Unraveling metabolism underpinning biomass composition shift in Scenedesmus obliquus under simulated outdoor conditions using 13 C-fluxomics

To render the resulting biomass more attractive and amenable for utilization as the basis for low-carbon intensity bioproducts, single-celled algae need to be biochemically and metabolically poised to assimilate and store the delivered carbon in the fastest and most efficient manner. Accelerating biochemical carbon storage, as primarily carbohydrates or lipids, is critical to achieve the high carbon capture potential that is assigned to algae. To guide strain optimization and engineering for maximizing carbon capture and storage, it is essential to elucidate the link between carbon metabolism and biomass composition. Most published metabolomics work in algae remains largely restricted to ideal and simplified environmental conditions in model organisms, thereby limiting their translation to outdoor implementation. In this work, we utilize 13 C isotopic labeling to characterize distinct intracellular metabolic fluxes before, during, and after nitrogen depletion-induced compositional shifts in Scenedesmus obliquus UTEX 393. The results indicate that a transition to carbohydrates is characterized by diverting flux to starch instead of replenishing the Calvin cycle for CO 2 fixation whereas the subsequent transition to lipids is fueled by NADPH produced by upregulating the phosphoenolpyruvate carboxylase (PEPC)–malic enzyme (ME) cycle flux. Our work highlights bottlenecks to carbohydrate- and lipid-rich biomass and can guide implementable strategies to control the fate of fixed carbon in S. obliquus.

09 BIOMASS FUELS↗
DOE OSTI2024

Fundamental physics studies in time domain and multi-messenger astronomy

The era of ime domain and multi-messenger astronomy is not only leading to the development of a much broader set of detectors and instruments for astrophysical observations, but is also providing the means for astronomy to tie directly to cutting-edge studies in physics. In this manner, fundamental physics (theory and experiment) coupled with a strong theoretical understanding of astrophysical phenomena (guided by high-performance computing simulations) can tie directly to the amazing new observations in astronomy. This paper discusses how physics, astrophysical models, and observations can not only help astronomy probe fundamental physics but guide the needs for next-generation astrophysical missions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

iNovo479: Metabolic Modeling Provides a Roadmap to Optimize Bioproduct Yield from Deconstructed Lignin Aromatics by Novosphingobium aromaticivorans

Lignin is an abundant renewable source of aromatics and precursors for the production of other organic chemicals. However, lignin is a heterogeneous polymer, so the mixture of aromatics released during its depolymerization can make its conversion to chemicals challenging. Microbes are a potential solution to this challenge, as some can catabolize multiple aromatic substrates into one product. Novosphingobium aromaticivorans has this ability, and its use as a bacterial chassis for lignin valorization could be improved by the ability to predict product yields based on thermodynamic and metabolic inputs. In this work, we built a genome-scale metabolic model of N. aromaticivorans, iNovo479, to guide the engineering of strains for aromatic conversion into products. iNovo479 predicted product yields from single or multiple aromatics, and the impact of combinations of aromatic and non-aromatic substrates on product yields. We show that enzyme reactions from other organisms can be added to iNovo479 to predict the feasibility and profitability of producing additional products by engineered strains. Thus, we conclude that iNovo479 can help guide the design of bacteria to convert lignin aromatics into valuable chemicals.

59 BASIC BIOLOGICAL SCIENCES↗