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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Spectroscopy and crystallography define carotenoid oxygenases as a new subclass of mononuclear non-heme Fe II enzymes

Carotenoid cleavage dioxygenases (CCDs) are non-heme Fe II enzymes that catalyze the oxidative cleavage of alkene bonds in carotenoids, stilbenoids, and related compounds. How these enzymes control the reaction of dioxygen (O 2 ) with their alkene substrates is unclear. Here, we apply spectroscopy in conjunction with X-ray crystallography to define the iron coordination geometry of a model CCD, CAO1 (Neurospora crassa carotenoid oxygenase 1), in its resting state and following substrate binding and coordination sphere substitutions. Resting CAO1 exhibits a five-coordinate (5C), square pyramidal Fe II center that undergoes steric distortion toward a trigonal bipyramidal geometry in the presence of piceatannol. Titrations with the O 2 -analog, nitric oxide, show a >100-fold increase in iron–nitric oxide affinity upon substrate binding, defining a crucial role for the substrate in activating the Fe II site for O 2 reactivity. The importance of the 5C Fe II structure for reactivity was probed through mutagenesis of the second-sphere Thr151 residue of CAO1, which occludes ligand binding at the sixth coordination position. A T151G substitution resulted in the conversion of the iron center to a six-coordinate state and a 135-fold reduction in apparent catalytic efficiency toward piceatannol compared with the wildtype enzyme. Substrate complexation resulted in partial six-coordinate to 5C conversion, indicating solvent dissociation from the iron center. Additional substitutions at this site demonstrated a general functional importance of the occluding residue within the CCD superfamily. Taken together, these data suggest an ordered mechanism of CCD catalysis occurring via substrate-promoted solvent replacement by O 2 . CCDs thus represent a new class of mononuclear non-heme Fe II enzymes.

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

New Particle Formation and Growth Dynamics for α-Pinene Ozonolysis in a Smog Chamber and Implications for Ambient Environments

Secondary organic aerosol (SOA) plays an important role in the Earth’s energy balance and air quality. Understanding the formation of SOA accompanying the oxidation of volatile organic compounds (VOCs) remains incomplete because of the complex reactions and partitioning mechanisms. In this study, we investigated new particle formation (NPF) of SOA derived from α-pinene ozonolysis in the Harvard Environmental Chamber using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the condensing species as two products, one of lower volatility (LVOC, 0.01 μg m –3 ) and one of higher volatility (SVOC, 1.0 μg m –3 ). A module for the classical nucleation theory was included. The surface tension (σ), bulk diffusivity (D b ), and mass accommodation coefficient (α) were varied until convergence between the simulated and observed particle number size distributions. α of LVOC was constrained as 0.03–0.1 with the reported σ of α-pinene-ozonolysis-derived SOA and Tolman surface correction (~23.0–27.5 mN m –1 ) and Db from 10 –15.5 to >10 –11 cm 2 s –1 . The observed particle number size distribution of α-pinene ozonolysis was well-simulated to derive the physical parameters, including σ, Db, and α, suggesting the possible application to group the reported complex oxidation products and further application for other atmospheric-related oxidations for quantifying NPF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Nitrogen increases early‐stage and slows late‐stage decomposition across diverse grasslands

1. To evaluate how increased anthropogenic nutrient inputs alter carbon cycling in grasslands, we conducted a litter decomposition study across 20 temperate grasslands on three continents within the Nutrient Network, a globally distributed nutrient enrichment experiment 2. We determined the effects of addition of experimental nitrogen (N), phosphorus (P) and potassium plus micronutrient (K μ ) on decomposition of a common tree leaf litter in a long-term study (maximum of 7 years; exact deployment period varied across sites). The use of higher order decomposition models allowed us to distinguish between the effects of nutrients on early- versus late-stage decomposition. 3. Across continents, the addition of N (but not other nutrients) accelerated early-stage decomposition and slowed late-stage decomposition, increasing the slowly decomposing fraction by 28% and the overall litter mean residence time by 58%. 4. Synthesis. Using a novel, long-term cross-site experiment, we found widespread evidence that N enhances the early stages of above-ground plant litter decomposition across diverse and widespread temperate grassland sites but slows late-stage decomposition. These findings were corroborated by fitting the data to multiple decomposition models and have implications for N effects on soil organic matter formation. For example, following N enrichment, increased microbial processing of litter substrates early in decomposition could promote the production and transfer of low molecular weight compounds to soils and potentially enhance the stabilization of mineral-associated organic matter. By contrast, by slowing late-stage decomposition, N enrichment could promote particulate organic matter (POM) accumulation. Such hypotheses deserve further testing.

Nutrient Network (NutNet)↗
DOE OSTI2023

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗
NASA NTRS1986

Spectroscopic observations of the planets

The first phase of a program to determine D/H in the atmospheres of all of the major planets and Titan was completed. D/H on Titan and Uranus is significantly higher than on Jupiter and Saturn. Titan has the highest value, consistent with the possibility for hydrogen escape. These measurements were all made in terms of CH3D/CH4. They suggest that at least two reservoirs for deuterium exist in the outer solar system - a large one in hydrogen and a much smaller one in hydrogen-containing compounds that have not isotopically equilibrated with the hydrogen. This result also demonstrates that a large fraction of the carbon in the outer solar nebula was in the form of methane and it supports the nucleation model for the formation of the outer planets. Other studies included an investigation of the phosphine abundance over the Great Red Spot (GRS) of Jupiter, pre-Voyager determination of the amount of acetylene on Uranus, and the carbon isotope ratio in Halley's Comet. UV spectra of the GRS obtained with the IUE show no evidence of the enhancement of phosphine that would be expected if the red color of this object is caused by P4.

Owen, T.↗
NASA NTRS2000

Increased Arginine and Ornithine Flux in Islets of Langerhans Cultured in a Microgravity Model System

Microgravity is associated with alterations in protein metabolism of both muscle and bone. That pancreas-derived insulin is essential to the normal maintenance of body protein balance is well known. The importance of altered endocrine pancreas function in microgravity is not yet established. We proposed to examine the influence of a microgravity model system, the High Aspect Ratio Vessel (HARV) upon islets of Langerhans from Wistar Furth rats. Islets were cultured in the HARV for 48 hr in Medium-199 and contrasted to static control islets (PLATE). Nitrogenous compounds elaborated into the media (micromoles/ml) were analyzed at 0 and 48 hr of culture and compared to PLATE with a 2-way ANOVA (HARV vs Hour).

Tobin, B. W.↗
NASA NTRS2001

Hydrogen Absorbing Materials for Use as Radiation Shielding During Extended Space Flight Missions

Minimizing radiation exposure from the galactic cosmic ray (GCR) environment during extended space missions is particularly crucial to crew health and safety. Here, an ideal candidate for shielding would be pure solid or liquid hydrogen, a material that effectively fragments heavy ions into ones of lower mass and energy that are more easily attenuated. Unfortunately, utilizing pure hydrogen is not presently feasible. It is, however, known that the hydrogen content of other materials (for example, metal hydrides, palladium alloys, and organic compounds) can exceed that of pure solid hydrogen and thus merit consideration as shielding candidates. This presentation will discuss an ongoing effort to develop novel shielding from such materials in concert with a coordinated testing/evaluation and modeling effort.

Grugel, Richard N.↗
NASA NTRS2001

Meteoric Material: An Important Component of Planetary Atmospheres

Interplanetary dust particles (IDPs) interact with all planetary atmospheres and leave their imprint as perturbations of the background atmospheric chemistry and structure. They lead to layers of metal ions that can become the dominant positively charged species in lower ionospheric regions. Theoretical models and radio occultation measurements provide compelling evidence that such layers exist in all planetary atmospheres. In addition IDP ablation products can affect neutral atmospheric chemistry, particularly at the outer planets where the IDPs supply oxygen compounds like water and carbon dioxide to the upper atmospheres. Aerosol or smoke particles from incomplete ablation or recondensation of ablated IDP vapors may also have a significant impact on atmospheric properties.

Grebowsky, Joseph M.↗
NASA NTRS2000

On the origin of Titan's atmosphere

The present atmosphere of Titan exhibits evidence of extensive evolution, in the form of rapid photochemical destruction of methane and a large fractionation of the nitrogen and oxygen isotopes. Attempts to recover the initial inventory of volatiles lead toward a model in which nitrogen was originally supplied as NH3, essentially unmodified from its relative abundance in the outer solar nebula. Titan's atmospheric methane, in contrast, appears to have been formed from carbon and other carbon compounds, either by gas phase reactions in the subnebula or by accretional heating during the formation of Titan. These conclusions can be tested by further studies of abundances and isotope ratios in Titan's atmosphere, augmented by studies of comets. The possible similarity of carbon and nitrogen inventories on Titan to those on the inner planets makes this investigation particularly intriguing.

Non-NASA Center↗
DOE OSTI2021

Potential Economic Values of Low-Vapor-Pressure Gasoline-Range Bio-Blendstocks: Property Estimation, and Blending Optimization

Adding non-traditional blendstocks derived from biomass into gasoline has gained renewed interest since it will reduce greenhouse gas emissions and potentially enhance fuel properties and boost engine efficiencies. A competitive bio-blendstock candidate should have either lower prices or superior properties or both relative to conventional gasoline blending components. Gasoline is a mixture of hundreds compounds specified by properties rather than compositions. The addition of bio-blendstocks, most likely oxygenates, will modify the property mixing rules due to the non-ideal interactions between polar and nonpolar components. This paper presents an equation-of-state-based model for predicting Reid vapor pressure, reduced order non-linear property blending models for computing key properties of final gasoline products, and a blending optimization approach to identify key economic drivers. This paper also evaluates the economic values of bio-blendstocks to petroleum refiners using those models. In additional to ethanol, six low-vapor-pressure bio-blendstock candidates were evaluated: i-propanol, n-propanol, i-butanol, diisobutylene, cyclopentanone, and a mixture of furans. Reid vapor pressure, distillation temperatures, and octane numbers were identified as the key economic drivers of adding bio-blendstock. The economic values of those bio-blendstocks range from 2.22 to 4.00 US dollar per gasoline gallon equivalent in 2013 to 2017 5-year averaged price basis. The bio-blendstocks with higher octane numbers may have higher economic values. The uncertainties in property predictions may lead to roughly 15% deviation in the economic value evaluation.

Jiang, Yuan↗
DOE OSTI2020

Using numerical methods to screen magnetocaloric materials in an active magnetic regenerative cycle

A 2-dimensional numerical model of a room temperature operating Active Magnetic Regenerator (AMR) that comprises of a regenerator, hot and cold heat exchangers, heat transfer fluid is developed. The regenerator is made of a magnetocaloric material (MCM) which heats up upon applying a magnetic field, H, and cools down when the field is removed; thus, making it the most essential part of an AMR. The model takes experimentally measured ΔT ad (H,T) and the C p (H,T) data as input and provides quantitative performance metrics of the magnetic cooling system, such as ΔT span and the cooling load, as output. With this model, it is possible to assess a wide range of MCMs in an AMR. 4 different MCMs were investigated using this model in terms of their ΔT span and the cooling loads—LaFe 10.96 Co 0.97 Si 1.07 , MnFeP (1-x) As (x) and AlFe 2 B 2 and Gd. During the screening of MCMs, all the important operating conditions of the device were fixed such as the fluid flow rate, ambient temperature, cycle duration, magnetic field strength. Our results indicate that Gd exhibits the maximum ΔT span with respectable cooling load, while AlFe 2 B 2 generates the lowest ΔT span . Even though LaFeCoSi compound did not perform as well as Gd, it could be the MCM choice for the realization of magnetic refrigeration on a global scale due to its much lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Voltage Mining for (De)lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulas is trained and shows state-of-the-art performance when compared to two established composition-based ML models for material properties predictions, Roost and CrabNet.

25 ENERGY STORAGE↗
DOE OSTI2020

Magnetic structure and exchange interactions in the layered semiconductor CrPS 4

Compounds with two-dimensional (2D) layers of magnetic ions weakly connected by van der Waals bonding offer routes to enhance quantum behavior, stimulating both fundamental and applied interest. CrPS 4 is one such magnetic van der Waals material, however, it has undergone only limited investigation. Here we present a comprehensive series of neutron scattering measurements to determine the magnetic structure and exchange interactions. The observed magnetic excitations allow a high degree of constraint on the model parameters not normally associated with measurements on a powder sample. The results demonstrate the 2D nature of the magnetic interactions, while also revealing the importance of interactions along 1D chains within the layers. The subtle role of competing interactions is observed, which manifest in a nontrivial magnetic transition and a tunable magnetic structure in a small applied magnetic field through a spin-flop transition. Our results on the bulk compound provide insights that can be applied to an understanding of the behavior of reduced layer CrPS 4 .

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

Abstract Background High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar ( Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves. Results The Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R 2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. Conclusions The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.

09 BIOMASS FUELS↗
NASA NTRS2009

Modeling Unsteady Cavitation and Dynamic Loads in Turbopumps

A computational fluid dynamics (CFD) model that includes representations of effects of unsteady cavitation and associated dynamic loads has been developed to increase the accuracy of simulations of the performances of turbopumps. Although the model was originally intended to serve as a means of analyzing preliminary designs of turbopumps that supply cryogenic propellant liquids to rocket engines, the model could also be applied to turbopumping of other liquids: this can be considered to have been already demonstrated, in that the validation of the model was performed by comparing results of simulations performed by use of the model with results of sub-scale experiments in water. The need for this or a similar model arises as follows: Cavitation instabilities in a turbopump are generated as inlet pressure drops and vapor cavities grow on inducer blades, eventually becoming unsteady. The unsteady vapor cavities lead to rotation cavitation, in which the cavities detach from the blades and become part of a fluid mass that rotates relative to the inducer, thereby generating a fluctuating load. Other instabilities (e.g., surge instabilities) can couple with cavitation instabilities, thereby compounding the deleterious effects of unsteadiness on other components of the fluid-handling system of which the turbopump is a part and thereby, further, adversely affecting the mechanical integrity and safety of the system. Therefore, an ability to predict cavitation- instability-induced dynamic pressure loads on the blades, the shaft, and other pump parts would be valuable in helping to quantify safe margins of inducer operation and in contributing to understanding of design compromises. Prior CFD models do not afford this ability. Heretofore, the primary parameter used in quantifying cavitation performance of a turbopump inducer has been the critical suction specific speed at which head breakdown occurs. This parameter is a mean quantity calculated on the basis of assumed steady-state operation of the inducer; it does not account for dynamic pressure loads associated with unsteady flow caused by instabilities. Because cavitation instabilities occur well before mean breakdown in inducers, engineers have, until now, found it necessary to use conservative factors of safety when analyzing the results of numerical simulations of flows in turbopumps.

Hosangadi, Ashvin↗
DOE OSTI2021

Severity factor kinetic model as a strategic parameter of hydrothermal processing (steam explosion and liquid hot water) for biomass fractionation under biorefinery concept

Hydrothermal processes are an attractive clean technology and cost-effective engineering platform for bio-refineries based in the conversion of biomass to biofuels and high-value bioproducts under the basis of sustainability and circular bioeconomy. The deep and detailed knowledge of the structural changes by the severity of biomasses hydrothermal fractionation is scientifically and technological needed in order to improve processes effectiveness, reactors designs, and industrial application of the multi-scale target compounds obtained by stream explosion and liquid hot water systems. The concept of the severity factor [log 10 (R 0 )] established > 30 years ago, continue to be a useful index that can provide a simple description of the relationship between the operational conditions for biomass fractionation in second generation of biorefineries. Furthermore, this review develops a deep explanation of the hydrothermal severity factor based in lignocellulosic biomass fractionation with emphasis in research advances, pretreatment operations and the applications of severity factor kinetic model.

09 BIOMASS FUELS↗
DOE OSTI2023

Substantially positive contributions of new particle formation to cloud condensation nuclei under low supersaturation in China based on numerical model improvements

New particle formation (NPF) and subsequent particle growth are important sources of condensation nuclei (CN) and cloud condensation nuclei (CCN). While many observations have shown positive contributions of NPF to CCN at low supersaturation, negative NPF contributions were often simulated in polluted environments. Using the observations in a coastal city of Qingdao, Beijing, and Gucheng in north China, we thoroughly evaluate the simulated number concentrations of CN and CCN using an NPF-explicit parameterization embedded in the WRF-Chem model. For CN, the initial simulation shows large biases of particle number concentrations at 10–40 and 40–100 nm. By adjusting the process of gas–particle partitioning, including the mass accommodation coefficient (MAC) of sulfuric acid, the phase changes in primary organic aerosol emissions, and the condensational amount of nitric acid, the improvement of the particle growth process yields substantially reduced overestimation of CN. Regarding CCN, secondary organic aerosol (SOA) formed from the oxidation of semi-volatile and intermediate-volatility organic compounds (S/IVOCs) is called SI-SOA, the yield of which is an important contributor. At default settings, the SI-SOA yield is too high without considering the differences in precursor oxidation rates. Lowering the SI-SOA yield under linear H 2 SO 4 nucleation scheme results in much-improved CCN simulations compared to observations. On the basis of the bias-corrected model, we find substantially positive contributions of NPF to CCN at low supersaturation (~ 0.2 %) over broad areas of China, primarily due to competing effects of increasing particle hygroscopicity, a result of reductions in SI-SOA amount, surpassing that of particle size decreases. The bias-corrected model is robustly applicable to other schemes, such as the quadratic H 2 SO 4 nucleation scheme, in terms of CN and CCN, though the dependence of CCN on SI-SOA yield is diminished likely due to changes in particle composition. This study highlights potentially much larger NPF contributions to CCN on a regional and even global basis.

54 ENVIRONMENTAL SCIENCES↗