Modeling Perovskite Oxide Thermodynamics via the Compound Energy Formalism for Solar Thermochemistry.
Abstract not provided.
Bush, Hagan Evan↗
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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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Model data for infrastructure-aware simulation of compound flooding at Alligator Bayou Watershed, southeast Texas
This dataset supports infrastructure-aware hydrologic modeling and flood scenario analysis for the Alligator Bayou Watershed, a highly managed urban watershed in Southeast Texas. It includes Jupyter notebooks for figure reproduction, model configuration files, simulation outputs, and derived products used to quantify the influence of engineered stormwater infrastructure on flood behavior across multiple spatial scales. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations on a channel-aligned mesh with explicit representations of pump stations, gate structures, detention basins, and impervious surfaces. Outputs include time series of gate and pump flows, stage observations, and water balance components, as well as spatially explicit fields of peak ponded depth and flood duration across multiple infrastructure scenarios spanning a single-location detention basin expansion, distributed drainage limitations, and compound coastal flooding. These data facilitate full reproducibility of the manuscript figures and support further research on urban flood dynamics and the role of stormwater infrastructure in shaping watershed-scale flood response.
EARTH SCIENCE > OCEANS > COASTAL PROCESSES↗
Coculture with hemicellulose-fermenting microbes reverses inhibition of corn fiber solubilization by Clostridium thermocellum at elevated solids loadings
Abstract Background The cellulolytic thermophile Clostridium thermocellum is an important biocatalyst due to its ability to solubilize lignocellulosic feedstocks without the need for pretreatment or exogenous enzyme addition. At low concentrations of substrate, C. thermocellum can solubilize corn fiber > 95% in 5 days, but solubilization declines markedly at substrate concentrations higher than 20 g/L. This differs for model cellulose like Avicel, on which the maximum solubilization rate increases in proportion to substrate concentration. The goal of this study was to examine fermentation at increasing corn fiber concentrations and investigate possible reasons for declining performance. Results The rate of growth of C. thermocellum on corn fiber, inferred from CipA scaffoldin levels measured by LC–MS/MS, showed very little increase with increasing solids loading. To test for inhibition, we evaluated the effects of spent broth on growth and cellulase activity. The liquids remaining after corn fiber fermentation were found to be strongly inhibitory to growth on cellobiose, a substrate that does not require cellulose hydrolysis. Additionally, the hydrolytic activity of C. thermocellum cellulase was also reduced to less-than half by adding spent broth. Noting that > 15 g/L hemicellulose oligosaccharides accumulated in the spent broth of a 40 g/L corn fiber fermentation, we tested the effect of various model carbohydrates on growth on cellobiose and Avicel. Some compounds like xylooligosaccharides caused a decline in cellulolytic activity and a reduction in the maximum solubilization rate on Avicel. However, there were no relevant model compounds that could replicate the strong inhibition by spent broth on C. thermocellum growth on cellobiose. Cocultures of C. thermocellum with hemicellulose-consuming partners— Herbinix spp. strain LL1355 and Thermoanaerobacterium thermosaccharolyticum —exhibited lower levels of unfermented hemicellulose hydrolysis products, a doubling of the maximum solubilization rate, and final solubilization increased from 67 to 93%. Conclusions This study documents inhibition of C. thermocellum with increasing corn fiber concentration and demonstrates inhibition of cellulase activity by xylooligosaccharides, but further work is needed to understand why growth on cellobiose was inhibited by corn fiber fermentation broth. Our results support the importance of hemicellulose-utilizing coculture partners to augment C. thermocellum in the fermentation of lignocellulosic feedstocks at high solids loading.
09 BIOMASS FUELS↗
The effect of imidazole, cyanamide, and polyornithine on the condensation of nucleotides in aqueous systems.
Development of two models for the condensation of nucleotides under possibly prebiotic conditions. In the first of these models this type of reaction is promoted by the presence of imidazole and substituted imidazole compounds. The second model involves the condensation of mononucleotides with cyanamide in the presence and absence of a prototemplate such as polyornithine. A tentative mechanism for the role of imidazole catalysis in phosphodiester bond formation between adjacent TMP molecules is suggested.
Ibanez, J.↗
Machine-learning enabled thermodynamic model for the design of new rare-earth compounds
We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an ‘in-house’ rare-earth database with more than 600 + compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu 2 type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed to get an in-depth understanding of the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.
36 MATERIALS SCIENCE↗
Discovery of first-in-class inhibitors of ASH1L histone methyltransferase with anti-leukemic activity
ASH1L histone methyltransferase plays a crucial role in the pathogenesis of different diseases, including acute leukemia. While ASH1L represents an attractive drug target, developing ASH1L inhibitors is challenging, as the catalytic SET domain adapts an inactive conformation with autoinhibitory loop blocking the access to the active site. Here, by applying fragment-based screening followed by medicinal chemistry and a structure-based design, we developed first-in-class small molecule inhibitors of the ASH1L SET domain. The crystal structures of ASH1L-inhibitor complexes reveal compound binding to the autoinhibitory loop region in the SET domain. When tested in MLL leukemia models, our lead compound, AS-99, blocks cell proliferation, induces apoptosis and differentiation, downregulates MLL fusion target genes, and reduces the leukemia burden in vivo. This work validates the ASH1L SET domain as a druggable target and provides a chemical probe to further study the biological functions of ASH1L as well as to develop therapeutic agents.
60 APPLIED LIFE SCIENCES↗
Organic compounds in meteorites
The problem of whether organic compounds originated in meteorites as a primary condensate from a solar gas or whether they were introduced as a secondary product into the meteorite during its residence in a parent body is examined by initially attempting to reconstruct the physical conditions during condensation (temperature, pressure, time) from clues in the inorganic matrix of the meteorite. The condensation behavior of carbon under these conditions is then analyzed on the basis of thermodynamic calculations, and compounds synthesized in model experiments on the condensation of carbon are compared with those actually found in meteorites. Organic compounds in meteorites seem to have formed by catalytic reactions of carbon monoxide, hydrogen, and ammonia in the solar nebula at 360 to 400 K temperature and about 3 to 7.6 microtorr pressure. The onset of these reactions was triggered by the formation of suitable catalysts (magnetite, hydrated silicates) at these temperatures.
Anders, E.↗
Estimation of secondary organic aerosol viscosity from explicit modeling of gas-phase oxidation of isoprene and α -pinene
Abstract. Secondary organic aerosols (SOA) are major components of atmospheric fine particulate matter, affecting climate and air quality. Mounting evidence exists that SOA can adopt glassy and viscous semisolid states, impacting formation and partitioning of SOA. In this study, we apply the GECKO-A (Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere) model to conduct explicit chemical modeling of isoprene photooxidation and α-pinene ozonolysis and their subsequent SOA formation. The detailed gas-phase chemical schemes from GECKO-A are implemented into a box model and coupled to our recently developed glass transition temperature parameterizations, allowing us to predict SOA viscosity. The effects of chemical composition, relative humidity, mass loadings and mass accommodation on particle viscosity are investigated in comparison with measurements of SOA viscosity. The simulated viscosity of isoprene SOA agrees well with viscosity measurements as a function of relative humidity, while the model underestimates viscosity of α-pinene SOA by a few orders of magnitude. This difference may be due to missing processes in the model, including autoxidation and particle-phase reactions, leading to the formation of high-molar-mass compounds that would increase particle viscosity. Additional simulations imply that kinetic limitations of bulk diffusion and reduction in mass accommodation coefficient may play a role in enhancing particle viscosity by suppressing condensation of semi-volatile compounds. The developed model is a useful tool for analysis and investigation of the interplay among gas-phase reactions, particle chemical composition and SOA phase state.
54 ENVIRONMENTAL SCIENCES↗
A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events
Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel policy-guided tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks – encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model—a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, in this study, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS
The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.
59 BASIC BIOLOGICAL SCIENCES↗
Structure-Based Discovery and Development of Highly Potent Dihydroorotate Dehydrogenase Inhibitors for Malaria Chemoprevention
Malaria remains a serious global health challenge, yet treatment and control programs are threatened by drug resistance. Dihydroorotate dehydrogenase (DHODH) was clinically validated as a target for treatment and prevention of malaria through human studies with DSM265, but currently no drugs against this target are in clinical use. We used structure-based computational tools including free energy perturbation (FEP+) to discover highly ligand efficient, potent, and selective pyrazolebased Plasmodium DHODH inhibitors through a scaffold hop from a pyrrole-based series. Optimized pyrazole-based compounds were identified with low nM-to-pM Plasmodium falciparum cell potency and oral activity in a humanized SCID mouse malaria infection model. The lead compound DSM1465 is more potent and has improved absorption, distribution, metabolism and excretion/pharmacokinetic (ADME/PK) properties compared to DSM265 that support the potential for once-monthly chemoprevention at a low dose. This compound meets the objective of identifying compounds with potential to be used for monthly chemoprevention in Africa to support malaria elimination efforts.
60 APPLIED LIFE SCIENCES↗
Vacancy formation and extraction energies in semiconductor compounds and alloys
A model for calculating the extraction energies and vacancy-formation energies in diamond-cubic and zinc-blende semiconductors is developed on the basis of Harrison's (1980, 1983) tight-binding theory. The extraction energies provide a reference from which other final states of the removed atoms can be calculated. The results of calculations show that, in a given compound, the calculated extraction energies are larger for the anion than for the cation, with the difference between the cation and the anion being larger in the II-VI than in the III-V compounds. This is in agreement with experimental results.
Berding, M. A.↗
CALPHAD Models to Guide Refractory Alloys Additive Manufacturing: In-Situ Compounds Formation, Nanoparticles, and Impurities Considerations
This technical memorandum (TM) seeks to model and discuss aspects of nanoparticle and micrometer-scale particle or ‘microparticle’ inclusions in refractory metal alloy manufacturing, via a currently used additive manufacturing (AM) method that results in equivalent wt.% addition of dissolved elements in the melt pool composition and subsequent formation of nanometer-scale MC and MO inclusions. Additionally, the authors begin to consider the effects of oxygen (O), a ubiquitous impurity, in AM; and seek to understand a combined optimization of alloying, nano- and micro strengthening and refinement, elemental additions, and even in-situ compounds and alloys species formation. To gain insights, this TM focuses on a pair of refractory alloys currently of interest to NASA: tungsten-rhenium-tantalum carbide (with O impurities) (W-Re-TaC(-O)) and tungsten-rhenium-tantalum-carbon (with O impurities) (W-Re-Ta-C(-O)), which are processed via powder bed fusion (PBF)-SLM.
ICME integrated computational materials engineerin↗
A model of CO-CH4 global transport/chemistry. I - Chemistry model
A simplified chemistry model was developed to incorporate the CO-CH4 chemistry into the global transport model of these compounds. CO is important because of its effects on atmospheric chemistry and is partly responsible for controlling the hydroxyl radical (OH) concentration in the troposphere. The model includes the photodissociation rate coefficients expressed as functions of solar zenith angle and altitude, and it was applied to determine the sensitivity of the OH concentration to trace gaseous species, such as NOx, O3, and H2O. Also, the concentrations and diurnal variations of OH and HO2, and the contribution of individual reactions to OH generation and consumption were calculated.
Peters, L. K.↗
Thermodynamics of monoclinic and tetragonal hafnium dioxide (HfO 2 ) at ambient pressure
Accurate and precise thermodynamic models of oxide compounds and their phases are important for calculating the phase stability of oxide materials. We develop and use a coupled quantum mechanical and molecular dynamics approach to create thermodynamic models of hafnia (HfO2) polymorphs from 0 K to 3000 K at ambient pressure. The approach is based on the quasi-harmonic approximation below the Debye temperature and on ab-initio molecular dynamics calculations above the Debye temperature to predict constant pressure heat capacities (C-p). A Bayesian model provides interpolated values between these regimes. As a case study, we develop thermodynamic models of monoclinic and tetragonal HfO2 polymorphs. The predicted heat capacities are in excellent agreement with experiment, and the predicted temperature of the monoclinic to tetragonal phase transition (2173 K) is in good agreement with the experimental value (2078K). These results provide a comprehensive and accurate thermodynamic model of the monoclinic and tetragonal phases of hafnia on a broad range of temperatures and can serve as input to CALPHAD assessment of multi-component hafnia-based phase diagrams.
36 MATERIALS SCIENCE↗
Analysis of Satellite and Sub-Orbital Measurements
The objective of this project is to support the INTEX aircraft mission by developing experience in the integrated analysis of existing sub-orbital observations and satellite observations with numerical models. Specific tasks include providing guidance to INTEX by identifying discrepancies in satellite observations with (1) in situ measurements, (2) bottom-up emission inventories of nitrogen oxides and volatile organic compounds, and (3) model calculations of the export of pollution from North America to the global atmosphere. An important focus area is developing and improving bottom-up emission inventories by combining top-down and bottom-up information.
Gleason, James↗
ABF Past Accomplishments - TEA/LCA
The Integrated Analysis task in the Agile BioFoundry (ABF) conducts techno-economic analysis (TEA) and life cycle assessment (LCA) to quantify the economics and environmental impacts of bioprocesses under development. The team from Argonne National Laboratory (ANL) and the National Renewable Energy Laboratory (NREL) develops TEA and LCA models of selected compound targets and hosts of interest to the ABF in an effort to provide an analysis-based foundation to the R&D. To date, the Integrated Analysis task has performed process modeling and analyses on multiple pairings of bioproducts and hosts, centered around major metabolic "beachheads" and "exemplar" molecules chosen for each of the metabolic pathways of interest. This presentation provides an overview of the approach used for TEA and LCA in the ABF and of the progress, outcomes, and impacts of TEA and LCA to support ABF goals, with a focus towards developing bio-based products that are both environmentally and economically viable.
BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning
Abstract Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction of Photosystem II (PSII). However, due to the incoherent transition of the S-states, the resolved structures are a convolution from different catalytic states. Here, we train Decision Tree Classifier and K-means clustering models on Mn compounds obtained from the Cambridge Crystallographic Database to predict the S-state of the X-ray, XFEL, and CryoEM structures by predicting the Mn’s oxidation states in the oxygen-evolving complex. The model agrees mostly with the XFEL structures in the dark S 1 state. However, significant discrepancies are observed for the excited XFEL states (S 2 , S 3, and S 0 ) and the dark states of the X-ray and CryoEM structures. Furthermore, there is a mismatch between the predicted S-states within the two monomers of the same dimer, mainly in the excited states. We validated our model against other metalloenzymes, the valence bond model and the Mn spin densities calculated using density functional theory for two of the mismatched predictions of PSII. The model suggests designing a more optimized sample delivery and illumiation systems are crucial to precisely resolve the geometry of the advanced S-states to overcome the noncoherent S-state transition. In addition, significant radiation damage is observed in X-ray and CryoEM structures, particularly at the dangler Mn center (Mn4). Our model represents a valuable tool for investigating the electronic structure of the catalytic metal cluster of PSII to understand the water splitting mechanism.
Plant Sciences↗