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DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗

DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗

DOE OSTI2024

Low-Cost, High-Performance Carbon Fiber for Compressed Natural Gas Storage Tanks (Final Technical Report – Down Select Report)

The aim of this project is to reduce the cost of Type IV, carbon fiber (CF) composite overwrap compressed gas storage tanks by reducing the cost of CF and CF composites. The project team worked to reduce the cost of CF by exploring and testing opportunities for a low-cost alternative precursor material for CF production to supplant market-dominant and costly polyacrylonitrile (PAN). Concurrently, the team aimed to reduce the cost of the tanks at the composite level by improving the interfacial adhesion between the fibers and the matrix resin through the incorporation of low-cost nanoparticles recycled from waste materials, which would reduce the volume of costly CF required to achieve the same tank performance. At the end of the first year, the project team selected mesophase pitch as the primary precursor candidate from a field of materials based on the superior mechanical performance and cost-saving potential. During the second year, the team produced CFs derived from mesophase pitch achieving an average tensile strength of 365.6 ksi and average tensile modulus of 40.74 Msi. Facility availability for spinning and converting these fibers at greater scale has hindered scale-up demonstration, but the team has identified opportunities to conduct this work in the near term. Cost modeling shows that these mesophase pitch-derived CFs can be up to 40% less expensive than PAN-derived CFs due to the lower cost of the feedstock material, higher throughput, greater conversion yield, and lower cost spinning method and compared to PAN. Additionally, the team has demonstrated at lab-scale that nanoparticle coating CFs can significantly increase the interfacial shear strength and load transfer efficiency of CFs in a matrix. Single filament pull-out testing showed a 27% average increase in max interfacial shear strength due to this coating. A continuous method of applying these coatings to a tow of CF has been developed for scale-up. 26 m tows of coated CFs were produced using this system and formed into composite ring samples for ASTM ring burst testing. Issues with the testing protocol have limited assessment of these results. A prototype Type IV tank was designed to meet ANSI HGV2 standards, and the design criteria set out by DOE, using the CF properties developed by the team paired with a proprietary resin matrix, a polyamide liner, and aluminum end bosses. The tank weighs 153.1 kg and with a total capacity of 5.8 kg H2 (5.6 kg usable), which yields a gravimetric capacity of 1.17 kWh/kg. Cost modeling predicts that the tank will have a projected cost of $15.73/kWh. Tank performance modeling does not include considerations for fiber-matrix load transfer efficiency improvements offered by nanoparticle coating method.

08 HYDROGEN↗

DOE OSTI2022

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Inductively Coupled Nonthermal Plasma Synthesis of Size-Controlled γ-Al 2 O 3 Nanocrystals

Gamma alumina (γ-Al 2 O 3 ) is widely used as a catalyst and catalytic support due to its high specific surface area and porosity. However, synthesis of γ-Al 2 O 3 nanocrystals is often a complicated process requiring high temperatures or additional post-synthetic steps. Here, we report a single-step synthesis of size-controlled and monodisperse, facetted γ-Al 2 O 3 nanocrystals in an inductively coupled nonthermal plasma reactor using trimethylaluminum and oxygen as precursors. Under optimized conditions, we observed phase-pure, cuboctahedral γ-Al 2 O 3 nanocrystals with defined surface facets. Nuclear magnetic resonance studies revealed that nanocrystal surfaces are populated with AlO 6 , AlO 5 and AlO 4 units with clusters of hydroxyl groups. Nanocrystal size tuning was achieved by varying the total reactor pressure yielding particles as small as 3.5 nm, below the predicted thermodynamic stability limit for γ-Al 2 O 3 .

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Near-infrared Census of RR Lyrae Variables in the Messier 3 Globular Cluster and the Period–Luminosity Relations

We present new near-infrared (NIR), JHK {sub s}, time-series observations of RR Lyrae variables in the Messier 3 (NGC 5272) globular cluster using the WIRCam instrument at the 3.6 m Canada–France–Hawaii Telescope. Our observations cover a sky area of ∼21′ × 21′ around the cluster center and provide an average of 20 epochs of homogeneous JHK {sub s}-band photometry. New homogeneous photometry is used to estimate robust mean magnitudes for 175 fundamental-mode (RRab), 47 overtone-mode (RRc), and 11 mixed-mode (RRd) variables. Our sample of 233 RR Lyrae variables is the largest thus far obtained in a single cluster with time-resolved, multiband NIR photometry. NIR-to-optical amplitude ratios for RR Lyrae in Messier 3 exhibit a systematic increase moving from RRc to short-period (P < 0.6 day) and long-period (P ≳ 0.6 day) RRab variables. We derive JHK {sub s}-band period–luminosity relations for RRab, RRc, and the combined sample of variables. Absolute calibrations based on the theoretically predicted period–luminosity–metallicity relations for RR Lyrae stars yield a distance modulus, μ=15.041±0.017 (statistical)±0.036 (systematic) mag, to Messier 3. When anchored to trigonometric parallaxes for nearby RR Lyrae stars from the Hubble Space Telescope and the Gaia mission, our distance estimates are consistent with those resulting from the theoretical calibrations, albeit with relatively larger systematic uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Transition to Petschek Reconnection in Subrelativistic Pair Plasmas: Implications for Particle Acceleration

While relativistic magnetic reconnection in pair plasmas has emerged in recent years as a candidate for the origin of radiation from extreme astrophysical environments, the corresponding subrelativistic pair-plasma regime has remained less explored, leaving open the question of how relativistic physics affects reconnection. In this paper, we investigate the differences between these regimes by contrasting two-dimensional particle-in-cell simulations of reconnection in pair plasmas with relativistic magnetization (σ ≫ 1) and subrelativistic magnetization (σ < 1). By utilizing unprecedentedly large domain sizes and outflow boundary conditions, we demonstrate that lowering the magnetization results in a change in the reconnection geometry from a plasmoid chain to a Petschek geometry, where laminar exhausts bounded by slow-mode shocks emanate from a single diffusion region. We attribute this change to the reduced plasmoid production rate in the low-σ case: When the secondary tearing rate is sufficiently low, plasmoids are too few in number to prevent the system from relaxing into a stable Petschek configuration. This geometric change also affects particle energization: We show that while high-σ plasmoid chains generate power-law energy spectra, low-σ Petschek exhausts merely heat incoming plasma and yield negligible nonthermal acceleration. These results have implications for predicting the global current sheet geometry and the resulting energy spectra in a variety of systems.

Plasma astrophysics↗

NASA NTRS1973

Theoretical and experimental investigation of the nonlinear behavior of angleplied boron/aluminum composites

Specimens from angleplied laminates of boron/aluminum composites were tested and the results analyzed. The specimens consisted of eight-ply symmetric layups and were loaded in tension at various angles to the 0 deg-ply direction. Stress-strain curves to specimen fracture were obtained. The analysis was performed using linear composite mechanics which includes residual stresses and a combined stress yield function. The results obtained indicate that angleplied boron/aluminum laminates exhibit a nonlinear stress-strain behavior to fracture. The residual stresses affect the initial and yield laminate properties. Linear composite mechanics is inadequate to predict the mechanical behavior of boron/aluminum angleplied laminates.

Chamis, C. C.↗

NASA NTRS1978

Theoretical and experimental study of the drag of multielement airfoils

The viscous/potential flow past single-element and multielement airfoils is studied theoretically and experimentally. A computerized analysis, based on iteratively coupled potential-flow and boundary-layer analysis, is used to predict the flow field of the airfoil. The method yields detailed characteristics of conventional laminar and turbulent boundary layers, turbulent wakes, and confluent boundary layers. The viscous flows are analyzed with a method that uses finite-difference solutions of the boundary-layer equations. Reynolds stress in the boundary layers and wakes is simulated with eddy viscosity models for the various flow zones. The viscous calculations are carried into the wake of the airfoil where the drag is found from the defect in the wake momentum.

Olson, L. E.↗

NASA NTRS1982

Modeling stiffness loss in boron/aluminum below the fatigue limit

Boron/aluminum can develop significant internal matrix cracking when fatigued. These matrix cracks can result in a 40 percent secant modulus loss in some laminates, even when fatigued below the fatigue limit. It is shown that the same amount of fatigue damage will develop during stress or strain-controlled tests. Stacking sequence has little influence on secant modulus loss. The secant modulus loss in unidirectional composites is small, whereas the losses are substantial in laminates containing off-axis plies. A simple analysis is presented that predicts unnotched laminate secant modulus loss due to fatigue. The analysis is based upon the elastic modulus and Poisson's ratio of the fiber and matrix, fiber volume fraction, fiber orientations, and the cyclic-hardened yield stress of the matrix material. Excellent agreement was achieved between model predictions and experimental results. With this model, designers can project the material stiffness loss for design load or strain levels and assess the feasibility of its use in stiffness-critical parts.

Johnson, W. S.↗

NASA NTRS1983

Modeling stiffness loss in boron/aluminum laminates below the fatigue limit

Johnson, W. S.↗

NASA NTRS1989

Effects of heat release on the structure and stability of a coflowing, chemically reacting jet

It is shown how linear stability analysis and full simulations can be used to study the stability and structure of reacting flows. The small effect of gravity on steady laminar flame heights can be modeled if allowance is made for the density variations due to heat release. It is found that cold transitional jet simulations yield perturbation growth rates which are consistent with the predictions of the inviscid stability theory.

Mahalingam, S.↗

NASA NTRS1990

Adaptive state estimation for control of flexible structures

This paper proposes a new approach of obtaining adaptive state estimation of a system in the presence of unknown system disturbances and measurement noise. In the beginning, a non-optimal Kalman filter with arbitrary initial guess for the process and measurement noises is implemented. At the same time, an adaptive transversal predictor (ATP) based on the recursive least-squares (RLS) algorithm is used to yield optimal one- to p- step-ahead output predictions using the previous input/output data. Referring to these optimal predictions the Kalman filter gain is updated and the performance of the state estimation is thus improved. If forgetting factor is implemented in the recursive least-squares algorithm, this method is also capable of dealing with the situation when the noise statistics are slowly time-varying. This feature makes this new approach especially suitable for the control of flexible structures. A numerical example demonstrates the feasibility of this real time adaptive state estimation method.

Chen, Chung-Wen↗

NASA NTRS1990

Recent developments in the micromechanics of advanced composites

A summary of recent developments and results in the area of inelastic response of advanced composites obtained with the micromechanics model proposed by Aboudi is presented. The recent developments include investigation of linear viscoelastic response of polymer matrix composites and yielding and elastoplastic response of metal matrix composites. The predictive capability and accuracy of the model is demonstrated through comparison of the predicted results with experimental data and results of finite-element analyses.

Pindera, M.-J.↗

NASA NTRS1997

[Petrological Analysis of Astrophysical Dust Analog Evolution]

This project "Petrological analysis of astrophysical dust analog evolution" was initiated to try to understand the vapor phase condensation, and the nature of the reaction products, in circumstellar environments, such as the solar nebula 4,500 Myrs ago, and in the interstellar medium. Telescope-based infrared [IR] spectroscopy offers a broad-scale inventory of the various types of dust in these environments but no details on small-scale variations in terms of chemistry and morphology and petrological phase relationships. Vapor phase condensation in these environments is almost certainly a non-equilibrium process. The main challenge to this research was to document the nature of this process that, based on astrophysical observations, seems to yield compositionally consistent materials. This observation may suggest a predictable character during non-equilibrium condensation. These astrophysical environments include two chemically distinct, that is, oxygen-rich and carbon-rich environments. The former is characterized by silicates the latter by carbon-bearing solids. According to cosmological models of stellar evolution circumstellar dust accreted into protoplanets wherein thermal and/or aqueous processes will alter the dust under initially, non-equilibrium conditions.

Rietmeijer, Frans J. M.↗

NASA NTRS2001

A Geosynchronous Lidar System for Atmospheric Winds and Moisture Measurements

An observing system comprised of two lidars in geosychronous orbit would enable the synoptic and meso-scale measurement of atmospheric winds and moisture both of which are key first-order variables of the Earth's weather equation. Simultaneous measurement of these parameters at fast revisit rates promises large advancements in our weather prediction skills. Such capabilities would be unprecedented and a) yield greatly improved and finer resolution initial conditions for models, b) make existing costly and cumbersome measurement approaches obsolete, and c) obviate the use of numerical techniques needed to correct data obtained using present observing systems. Additionally, simultaneous synoptic wind and moisture observations would lead to improvements in model parameterizations, and in our knowledge of small-scale weather processes. Technology and science data product assessments are ongoing. Results will be presented during the conference.

Emmitt, G. D.↗

NASA NTRS2003

The Magnetospheric Constellation Mission (MC-DRACO)

The Magnetospheric Constellation Mission Dynamic Response and Coupling Observatory (MC-DRACO), the logical outgrowth of a sequence of STP missions, will explore plasma transport and energy conversion processes over a broad range of spatial sizes. Designed to be a meso-/macroscope for the magnetotail, it will resolve persistent controversies and yield a new understanding on which to build a predictive science of next generation magnetospheric meteorology.

Moore, Thomas E.↗