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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessment of dynamic-screw-pinch-driven, current-scaled MagLIF target implosion performance using 3D magnetohydrodynamic simulations

Analytic studies and two-dimensional “clean” radiation-magnetohydrodynamic (rad-MHD) simulations employing dynamical similarity driver-target scaling prescriptions [Ruiz et al., Phys. Plasmas 30, 032708 (2023)] suggest that Magnetized Liner Inertial Fusion (MagLIF) target implosions can scale to > 10 MJ DT fusion yields when peak drive current is increased beyond 60 MA. We present results from three-dimensional (3D) rad-MHD simulations of similarity-scaled MagLIF target implosions at peak drive currents ranging from 15 to 40 MA. Simulations in this study suggest that magneto-Rayleigh–Taylor instability (MRTI) growth and feedthrough to the fuel region are more severe at higher drive current scales, which reduces the fusion yield compared to prior analytic and 2D clean simulation predictions. In contrast to standard MagLIF, simulations of current-scaled MagLIF target implosions driven by a dynamic screw pinch (DSP) demonstrate reduced MRTI feedthrough and greater fuel magnetization, resulting in improved thermonuclear performance and enhanced performance scaling with peak drive current. DSP drive enables additional scaling of the liner mass to increase liner radius but maintain implosion time, resulting in higher implosion velocities at the expense of increased susceptibility to MRTI. We present a current- and mass-scaled simulated DSP-MagLIF target implosion at the ∼ 40 MA peak current level that produces ignition scale performance, demonstrating a burn-averaged Lawson ignition parameter above unity and DT fusion yield above 1 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Quantification of Key Peroxy and Hydroperoxide Intermediates in the Low-Temperature Oxidation of Dimethyl Ether

Dimethyl ether (DME) oxidation is a model chemical system with a small number of prototypical reaction intermediates that also has practical importance for low-carbon transportation. Although it has been studied experimentally and theoretically, ambiguity remains in the relative importance of competing DME oxidation pathways in the low-temperature autoignition regime. To focus on the primary reactions in DME autoignition, we measured the time-resolved concentration of five intermediates, CH 3 OCH 2 OO (ROO), OOCH 2 OCH 2 OOH (OOQOOH), HOOCH 2 OCHO (hydroperoxymethyl formate, HPMF), CH 2 O, and CH 3 OCHO (methyl formate, MF), from photolytically initiated experiments. We performed these studies at P = 10 bar and T = 450-575 K, using a high-pressure photolysis reactor coupled to a time-of-flight mass spectrometer with tunable vacuum-ultraviolet synchrotron ionization at the Advanced Light Source. Our measurements reveal that the timescale of ROO decay and product formation is much shorter than predicted by current DME combustion models. The models also strongly underpredict the observed yields of CH 2 O and MF and do not capture the temperature dependence of OOQOOH and HPMF yields. Adding the ROO + OH $\rightarrow$ RO + HO 2 reaction to the chemical mechanism (with a rate coefficient approximated from similar reactions) improves the prediction of MF. Increasing the rate coefficients of ROO ↔ QOOH and QOOH + O 2 ↔ OOQOOH reactions brings the model predictions closer to experimental observations for OOQOOH and HPMF, while increasing the rate coefficient for the QOOH $\rightarrow$ 2 CH 2 O + OH reaction is needed to improve the predictions of formaldehyde. In conclusion, to aid future quantification of DME oxidation intermediates by photoionization mass spectrometry, we report experimentally determined ionization cross-sections for ROO, OOQOOH, and HPMF.

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

Droplet Evaporation-Based Approach for Microliter Fuel Property Measurements

Small-volume, high-throughput screening techniques are sought to enable downselection from a large candidate pool of bio-blendstocks to a select few, having physical properties consistent with requirements of downsized, turbo-boosted internal combustion engines. Herein, this work presents a droplet evaporation-based approach to predict heat of vaporization, vapor pressure, diffusion coefficient, and Lennard–Jones parameters for an unknown fuel. Two different schemes, considering the isothermal evaporation of a moving droplet in ambient air, are proposed, which combine droplet velocity and temperature measurements, with some known properties to predict unknown properties. The schemes utilize an inverse solution of a transient model of droplet evaporation solved in an iterative fashion. A baseline scheme, which only requires droplet size change measurements, is evaluated using test data for three liquid fuels, comprising of alkanes and alcohols, as obtained in a temperature-controlled chamber. Results yield temperature-dependent heat of vaporization and vapor pressure predictions within 10 % and 22 %, respectively, of reference values. The advanced scheme, which additionally requires droplet temperature measurement, is numerically evaluated in the current work and will be experimentally validated in future efforts. The advanced scheme is found to significantly improve prediction quality, with deviations less than 2 % and 1 % for heat of vaporization and vapor pressure, while also predicting diffusion coefficient and Lennard–Jones parameters within 5 % and 8 %, respectively. The combined set of approaches, which primarily track droplet evaporation, can be incorporated into a small-volume, high-throughput fuel screening process.

09 BIOMASS FUELS↗
DOE OSTI2023

Modification of the MTS model for high strain-rate behavior of TI-6AL-4V

The Mechanical Threshold Stress (MTS) model provides excellent predictive capabilities for the material constitutive response for a wide range of temperatures and strain rates. However, the MTS model fails to capture the rapidly increasing yield stress at high strain rate behavior as the deformation controlling mechanism transitions from thermal activation to drag mechanisms, only capturing the linear behavior. Further, the model typically over predicts the flow stress behavior at yield and post yield due to its use of a constant work hardening rate parameter derived from the stress–strain response at constant saturation stress. An alternative approach to fitting portions of the MTS model is investigated and mathematical models are developed to address these issues. The results show that with appropriate experimental data, the mechanical threshold stress and work hardening rate parameters within the MTS model can quite easily and accurately be modified to extend applicability to high strain rate behavior and more accurately model the initial flow stress behavior at early work hardening rates without modification of the functions core to the MTS model itself.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2023

$f$ 0 (980) production in inelastic pp collisions at s = 5.02 TeV

The measurement of the production of f 0 (980) in inelastic pp collisions at $\sqrt{s}$ = 5.02 TeV is presented. This is the first reported measurement of inclusive f 0 (980) yield at LHC energies. The production is measured at midrapidity, |y| < 0.5, in a wide transverse momentum range, 0 < p T < 16 GeV/c, by reconstructing the resonance in the f 0 (980) → π + π - hadronic decay channel using the ALICE detector. The p T -differential yields are compared to those of pions, protons and $φ$ mesons as well as to predictions from the HERWIG 7.2 QCD-inspired Monte Carlo event generator and calculations from a coalescence model that uses the AMPT model as an input. The ratio of the p T -integrated yield of f 0 (980) relative to pions is compared to measurements in e + e - and pp collisions at lower energies and predictions from statistical hadronisation models and HERWIG 7.2. A mild collision energy dependence of the f 0 (980) to pion production is observed in pp collisions from SPS to LHC energies. All considered models underpredict the p T -integrated 2f 0 (980)/(π + +π - ) ratio. The prediction from the canonical statistical hadronisation model assuming a zero total strangeness content of f 0 (980) is consistent with the data within 1.9σ and is the closest to the data. The results provide an essential reference for future measurements of the particle yield and nuclear modification in p–Pb and Pb–Pb collisions, which have been proposed to be instrumental to probe the elusive nature and quark composition of the f 0 (980) scalar meson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Measurement of the Drell-Yan forward-backward asymmetry at high dilepton masses in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A measurement of the forward-backward asymmetry of pairs of oppositely charged leptons (dimuons and dielectrons) produced by the Drell-Yan process in proton-proton collisions is presented. The data sample corresponds to an integrated luminosity of 138 fb -1 collected with the CMS detector at the LHC at a center-of-mass energy of 13 TeV. The asymmetry is measured as a function of lepton pair mass for masses larger than 170 GeV and compared with standard model predictions. An inclusive measurement across both channels and the full mass range yields an asymmetry of 0.612 ± 0.005 (stat) ± 0.007 (syst). As a test of lepton flavor universality, the difference between the dimuon and dielectron asymmetries is measured as well. No statistically significant deviations from standard model predictions are observed. The measurements are used to set limits on the presence of additional gauge bosons. For a Z' boson in the sequential standard model the observed (expected) 95% confidence level lower limit on the Z' mass is 4.4 TeV (3.7 TeV).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Interplay Between Kondo and Magnetic Interactions in Pr 0.75 Gd 0.25 ScGeH

Combined experimental and density functional theory (DFT) study of Pr 0.75 Gd 0.25 ScGe and its hydride (Pr 0.75 Gd 0.25 ScGeH) reveals intricacies of composition-structure-property relationships in those distinctly layered compounds. Hydrogenation of the intermetallic parent, crystalizing in a tetragonal CeScSi-type structure, leads to an anisotropic volume expansion, that is, a(=b) lattice parameter decreases while the lattice expands along the c direction, yielding a net increase of cell volume. DFT calculations predict an antiparallel coupling of localized Gd and Pr magnetic moments in both materials at the ground state. While experiments corroborate this for the parent compound, there is no conclusive experimental proof for the hydride, where Pr moments do not order down to 3 K. DFT results also reveal that rare-earth – hydrogen interactions reduce spin-polarization of the Pr and Gd 5d and Sc 3d states at the Fermi energy, disrupt indirect exchange interactions mediated by conduction electrons, dramatically reduce the magnetic ordering temperature, and open a pseudo-gap in the majority-spin channel. Here, both experiments and theory show evidence of Kondo-like behavior in the hydride in the absence of an applied magnetic field, whereas increasing the field promotes magnetic ordering and suppresses Kondo-like behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Innovative approaches to neutron beam optimization: A case study of FP14 at LANSCE

This paper describes an experimental method to acquire high resolution energy- and spatially- resolved neutron beam spots using the time-gated neutron imaging system with Teledyne Pi-MAX4 camera. These experimental data offer a unique opportunity for benchmarking beam spot simulations. High-quality simulations depend significantly on a high-fidelity geometry model, which can be challenging for legacy facilities. We informed our MCNPX geometry model by latest metrology survey employing Leica laser tracker ATS600. It gave us a high fidelity description of our facility geometry. Such a robust integration of novel tools and methods yields a previously unattainable level of accuracy in both predicting and capturing neutron beam spots, marking a notable advancement over traditional methods reliant on static image plates. Here, to demonstrate the practical application of these tools, we are showing a non-uniform beam spot challenge at our Flight Path 14 (FP14) at the Los Alamos Neutron Science Center (LANSCE). Our precise MCNPX prediction of beam spot shifting as function of neutron energy was confirmed by experimental beam spot measured with extremely high level of detail. Results of this research demonstrate a significant leap in neutron beam optimization at LANSCE and set a new benchmark in beam spot characterization. The advanced methods presented here have potential for adoption at similar research facilities worldwide, aiming at substantial improvement in neutron beam delivery for experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Measurement of prompt D s + -meson production and azimuthal anisotropy in Pb–Pb collisions at s NN = 5.02 TeV

The production yield and angular anisotropy of prompt $D^{+}_{s}$ mesons were measured as a function of transverse momentum (p T ) in Pb–Pb collisions at a centre-of-mass energy per nucleon pair $\sqrt{s_{NN}}$ = 5.02 TeV collected with the ALICE detector at the LHC. $D^{+}_{s}$ mesons and their charge conjugates were reconstructed at midrapidity (|y| < 0.5) from their hadronic decay channel $D^{+}_{s}$ → φπ + , with φ → K - K + , in the p T intervals 2 < p T < 50 GeV/c and 2 < p T < 36 GeV/c for the 0–10% and 30–50% centrality intervals. For p T > 10 GeV/c, the measured $D^{+}_{s}$ -meson nuclear modification factor R AA is consistent with the one of non-strange D mesons within uncertainties, while at lower pT a hint for a $D^{+}_{s}$ -meson R AA larger than that of non-strange D mesons is seen. The enhanced production of $D^{+}_{s}$ relative to non- strange D mesons is also studied by comparing the pT-dependent $D^{+}_{s}$ /D 0 production yield ratios in Pb–Pb and in pp collisions. The ratio measured in Pb–Pb collisions is found to be on average higher than that in pp collisions in the interval 2 < p T < 8 GeV/c with a significance of 2.3σ and 2.4σ for the 0–10% and 30–50% centrality intervals. The azimuthal anisotropy coefficient v2 of prompt $D^{+}_{s}$ mesons was measured in Pb–Pb collisions in the 30–50% centrality interval and is found to be compatible with that of non-strange D mesons. The main features of the measured RAA, $D^{+}_{s}$ /D 0 ratio, and v 2 as a function of p T are described by theoretical calculations of charm-quark transport in a hydrodynamically expanding quark–gluon plasma including hadronisation via charm-quark recombination with light quarks from the medium. The p T -integrated production yield of $D^{+}_{s}$ mesons is compatible with the prediction of the statistical hadronisation model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Enhanced Selectivity for C 2 H 4 Production from C 2 H 6 on Partially Chlorinated IrO 2 (110) Surfaces

Modifying metal oxide surfaces to limit their oxidizing activity can provide a means of improving catalytic selectivity toward the partial oxidation of light alkanes. Here, in this study, we investigated the oxidation of C 2 H 6 on Cl-modified IrO 2 (110) surfaces using temperature-programmed reaction spectroscopy (TPRS) and first-principles microkinetic modeling. We find that substituting Cl for O in the IrO 2 (110) surface enhances the selectivity for C 2 H 6 conversion to C 2 H 4 during TPRS by suppressing extensive oxidation to CO x products, while also either enhancing C 2 H 4 production or altering it to a lesser extent, depending on the initial C 2 H 6 coverage. The C 2 H 4 selectivity increased with increasing C 2 H 6 and Cl coverage, but reached a limiting value below 50%. The Cl coverage changed negligibly during C 2 H 6 oxidation, and the surface reactivity decreased only marginally for Cl coverages up to 0.5 ML (monolayer). TPRS simulations using a microkinetic model predict C 2 H 4 and CO x product yields as a function of the Cl coverage that agree closely with the experimental results. According to the simulations, C 2 H 6 conversion to C 2 H 4 occurs on Cl-IrO 2 (110) by the hydrogenation of C 2 H 3 * species adsorbed in blocked states, in which neighboring sites are occupied only by unreactive HO and Cl species. The microkinetic modeling shows that H-hopping away from surface HO groups provides a relatively efficient route for C 2 H 3 * to escape blocked configurations and dehydrogenate, and that this process can limit the C 2 H 4 selectivity on Cl-IrO 2 (110) under the conditions studied. Overall, our results demonstrate that Cl-substitution into IrO 2 (110) enhances the selectivity for C 2 H 4 production from C 2 H 6 and provides insights into the reaction mechanism that can guide strategies to further improve the C 2 H 4 selectivity.

IrO2↗
DOE OSTI2025

Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT

NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 27Al crystalline solids using DFT and benchmarked them against 105 experimentally measured 27Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz for CQ, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of 27Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.

Sun, He↗
DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unusual products arising from the tandem dehydrogenation of Mg(BH 4 ) 2 and pyrrolidine

The dehydrogenation of Mg(BH 4 ) 2 in the presence of O-Lewis base donors has been widely explored but there have been very few studies of dehydrogenation of the borohydride in the presence of N-Lewis bases. Mg(BH 4 ) 2 has been found to react with pyrrolidine at room temperature to form BH 3 -pyrrolidine adduct. Upon heating, further dehydrogenation reactions occur to form bis(pyrrolidino)borane, tris(pyrrolidino)borane and other B–N intermediates. Reacting Mg(BH 4 ) 2 and pyrrolidine in a 1 : 6 molar ratio exclusively yields tris(pyrrolidino)borane which is in accordance with predicted stoichiometric factors. The formation of these products contrasts with the routine production of higher boranes and/or intractable polymers from the dehydrogenation of Mg(BH 4 ) 2 in the presence O-Lewis bases. The amount of H 2 released in these reactions was determined both by the Parr autoclave pressure readings and the eudiometer measurements. Both measurements align with the values predicted by the stoichiometric factors associated by the proposed pathway.

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

Effects of drive pulse shape on graded metal pushered single shell capsule implosions on the National Ignition Facility

Graded metal pushered single shells (PSS) are a viable alternative to low-Z capsules (Z is the atomic number) for indirect drive inertial confinement fusion implosions due to enhanced core tamping and radiation trapping, but they can be compromised by the pusher mixing with the fuel. We compare 2-shock and 3-shock laser pulses for Be/Cr PSS capsules filled with deuterium–tritium gas fuel at 6 mg/cc density. 1D radiation-hydrodynamic simulations predict higher core compression and, hence, ∼2× higher fusion yield for the 3-shock drive than for 2-shock. Nevertheless, we observe similar core ion temperatures and fusion yields for both drives. The implosion burn duration is 25% shorter and the core volume is ∼2.5× smaller for the 3-shock drive than for 2-shock, consistent with a higher compression. 1D LASNEX mix simulations using a buoyancy-drag model matching the measured yields also agree with the observed core sizes and burn durations and suggest ∼40% and ∼70% yield degradations for 2-shock and 3-shock drives due to hydrodynamic instabilities and atomic mix at the pusher–fuel interface. At the same time, 2D HYDRA simulations show that mid-mode (2–250) instability degradations are negligible for the 2-shock implosion (9%) and significant (45%) for 3-shock. Subtracting these from the 1D mix simulations, we infer similar degradations from high-mode instabilities and atomic mix for both drives. Due to its robustness to mid-mode instabilities, future pusher–gas mix studies will use the 2-shock drive.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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.↗