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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Highly tunable band inversion in AB 2 X 4 (A=Ge, Sn, Pb; B=As, Sb, Bi; X=Se, Te) compounds

Topological materials have been discovered so far largely by searching for existing compounds in crystallographic databases, but there are potentially new topological materials with desirable features that have not been synthesized. One of the desirable features is high tunability resulting from the band inversion with a very small direct band gap, which can be tuned by changes in pressure or strain to induce a topological phase transition. Here, using density-functional theory (DFT) calculations, we have studied the septuple layered AB 2 X 4 series compounds, where A=(Ge, Sn and Pb), B=(As, Sb and Bi), and X=(Se and Te). With the DFT thermodynamic stability validated by the already-reported compounds in these series, we predict stable Se compounds, which are not found in crystallographic database. Among them, we find that GeBi 2 Se 4 and GeSb 2 Se 4 having a small direct band gap at the Z point are very close to a strong topological insulator, which can be tuned by a moderate pressure to induce the band inversion. Importantly, the topological features with the small direct band gap are well isolated in both momentum and energy windows, which offers high tunability for studying the topological phase transition.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Defining the Antitumor Mechanism of Action of a Clinical-stage Compound as a Selective Degrader of the Nuclear Pore Complex

Cancer cells are acutely dependent on nuclear transport due to elevated transcriptional activity, suggesting an unrealized opportunity for selective therapeutic inhibition of the nuclear pore complex (NPC). Through large-scale phenotypic profiling of cancer cell lines, genome-scale functional genomic modifier screens, and mass spectrometry–based proteomics, we discovered that the clinical drug PRLX-93936 is a molecular glue that binds and reprograms the TRIM21 ubiquitin ligase to degrade the NPC. Upon compound-induced TRIM21 recruitment, the nuclear pore is ubiquitylated and degraded, resulting in the loss of short-lived cytoplasmic mRNA transcripts and the induction of cancer cell apoptosis. Direct compound binding to TRIM21 was confirmed via surface plasmon resonance and X-ray crystallography, whereas compound-induced TRIM21–nucleoporin complex formation was demonstrated through multiple orthogonal approaches in cells and in vitro. Phenotype-guided optimization yielded compounds with 10-fold greater potency and drug-like properties, along with robust pharmacokinetics and efficacy against pancreatic cancer xenografts and patient-derived organoids.

Yuan, Linjie [Stanford School of Medicine, CA (Uni↗
DOE OSTI2025

Genetic tools for engineering Zymomonas mobilis , Cereibacter sphaeroides and Novosphingobium aromaticivorans to improve production of bioenergy compounds

Limited genetic tools for non-model bacteria are one of the limiting factors for genetic studies. This review compiles genetic tools used for three non-model alpha-proteobacteria, such as Zymomonas mobilis, Cereibacter (Rhodobacter) sphaeroides, and Novosphingobium aromaticivorans, which hold significant potential to produce industrially essential bioenergy compounds due to their distinctive metabolic pathways and resilience in extreme environments. Each of these strains has a unique genetic profile that enables them to efficiently carry out key reactions relevant to producing bioenergy compounds, such as converting sugars into bioenergy compounds and breaking down lignotoxins. Genetic tools can further optimize these strains for enhanced bioenergy compound production. This review explores the metabolic advantages of these organisms. It highlights the available array of genetic toolkits that can be shared among them to unlock their full potential for sustainable biofuel production.

Biofuel↗
DOE OSTI2024

Calibration of V-Notch and Compound Weirs for Subsurface Drainage Water Level Control Structures

Highlights Accurate discharge estimation is important when evaluating edge-of-field conservation practices. V-notch weir equations were developed for three sizes of subsurface drainage water level control structures. Compound weir equations were developed for subsurface drainage water level control structures. The compound weir equation accurately estimates discharge for flows within and overtopping the V-notch. Abstract.Numerous edge-of-field conservation practices use subsurface drainage water level control structures to monitor water levels and estimate discharge. In a control structure, procedures for calculating discharge when flow depth (head) exceeds the V-notch depth and overflows in the rectangular portion of the compound weir (CW) are ambiguous. In this study, we developed calibration equations for V-notch weirs in Agri Drain inline water level control structures of different sizes for flows within the V-notch and overtopping flow events. The discharge equation for overtopping events (Q CW , L s -1 ) was determined as: Q CW = a 1 (h b1 -h 1 b1 )+a 2 (W e -W v )h 1 b2 , where h and h 1 are heads above vertex/bottom and top of V-notch (cm), respectively, W is the effective crest width of rectangular weir (cm), W v is the top width of V-notch (cm), a 1 and b 1 are parameters for V-notch weir obtained by calibration, and a 2 and b 2 are calibration parameters for rectangular weir obtained from literature. Results were compared with a weir equation available in the literature (Q V+R ), which combines a V-notch equation with a head equal to V-depth and a rectangular weir equation for flow above V-depth. Discharge at overflow was estimated with high accuracy with Q CW, whereas Q V+R underestimated discharge (e.g., PBIAS of 0.67% vs. 17.82% for a 15.2 cm structure). An example using Q V+R resulted in a 14% lower annual estimation of nitrate-N load diverted to a saturated buffer than Q CW due to underestimation of drainage discharge during overflow events. Results suggest that the developed equation (Q CW ) accurately estimates discharge and will thus improve the estimated N load compared to Q V+R . Keywords: Compound weir, Flow monitoring, Subsurface drainage, V-notch weir, Water level control structure, Weir calibration.

Agriculture↗
DOE OSTI2019

Electrochemically-Driven Phase Transformations in Battery Compounds (Final Technical Report)

Ion-insertion compounds for advanced batteries frequently exhibit phase transformations as the concentration of the working ion varies. Under the large electrochemical driving forces inherent to practical use, systems are often driven far from equilibrium and exhibit phase transition behavior not seen in other materials. An improved understanding of phase transformation pathways in electrode materials upon cycling will lead to new materials design concepts and electrochemical duty cycle management strategies that improve capacity utilization at high charge/discharge rates, reduce voltage and capacity hysteresis, and extend battery life. The main goal of this project is to develop a predictive understanding of the phase transition behavior of battery compounds when electrochemically driven far from equilibrium through a combined experimental- theoretical approach. Research focuses on elucidating how the phase transition behaviors in a diverse group of selected model systems (olivines, lithium metal, etc.) are regulated by various factors including transformation strains, plasticity, metastable transformation pathways, electrode microstructure, surface reaction and ion diffusion kinetics, and what are the unique features of phase transitions in battery compounds. To this end, operando X-ray-based characterization techniques are applied to interrogate the structure and composition evolution of electrode materials and interfaces at nano- and meso-scales. Mesoscale modeling techniques such as phase-field simulation are employed to shed light on the experimental observations and establish a unified framework for describing phase transition behaviors in battery compounds.

25 ENERGY STORAGE↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Electrochemically-Induced Phase Transformations in Battery Storage Compounds (Final Technical Report)

Compounds of interest for ion storage in advanced batteries frequently exhibit phase transformations as the working ion concentration varies. Under large electrochemical driving forces inherent to practical use, systems are often driven far from equilibrium. This program combines experiments and theory to understand the phase transition behavior of ion insertion compounds when electrochemically driven far from equilibrium. As model systems, we focus on alkaline metal phosphates AMPO4 (A = alkali; M = first row transition metal) of olivine structure, which are both technologically interesting and ideally suited for fundamental study due to the ability to systematically tune transformation strain, and along with it, the phase transformation pathway. Behavior in compositions having large transformation strains (~15 vol%) requiring plasticity for strain accommodation is emphasized. Experimental techniques include operando characterization of structure while simultaneously varying electrokinetic parameters, and high resolution microscopy of nanoscale and interfacial phenomena. Phase-field modeling is used to model the thermodynamics and kinetics of competing transformation pathways, extended to include the effects of plasticity, and integrated with porous electrode kinetic theory to treat multi-particle effects. Success in this project will lead to an ability to design ion storage compounds with predictable transformation pathways, electrochemical kinetics, capacity utilization, and durability. New technologically important compounds may also be discovered.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structural Changes in Molten Salt Fuel and/or Waste Stream Compounds Cs 2 UCl 6 and Cs 2 UO 2 Cl 4 from Room Temperature to Melting and Related Materials [Slides]

Perovskite phases have been explored in recent literature as waste forms for long term storage of radioactive waste from molten salt reactors specifically. Synthesis of new halide perovskite phases can inform these efforts as well as molten salt reactor chemistry in general. Inorganic perovskites have also shown promise as new scintillating materials for detection of X-rays and gamma rays. Modifying the structure and chemistry of these compounds, perhaps with organic components, could be used to synthesize new scintillating compounds or modify the properties of existing ones. Both U 6+ and U 4+ readily form compounds with a striking similarity to vacancy ordered perovskites (Cs 2 UO 2 Cl 4 and Cs 2 UCl 6 ). Each of these compounds is relevant to molten salt reactor chemistry and/or waste streams. These phases could from in uranium chloride salt systems or waste streams: Cs being a fission product.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Novel Microbial Routes to Synthesize Industrially Significant Precursor Compounds

Ethylene is the most widely employed organic precursor compound in industry. The potential to impact ethylene formation via recently discovered microbial processes is tenable using plentiful CO2 feedstocks. The overall long-term objective of this project was to develop an industrially compatible microbial process to synthesize ethylene in high yields. The key objective of this project was to fully define and initially characterized a recently discovered and genetically regulated anaerobic pathway to produce high levels of ethylene called the Dihydroxyacetone Phosphate - Ethylene Pathway in phototrophic bacteria. This was addressed through the following specific aims: 1. Fully probe the catalytic potential of all enzymes of the DHAP ethylene pathway and determine the regulatory mechanism of DHAP-ethylene pathway gene expression. 2. Discover effective and active ethylene enzymes encoded in cultured and uncultured organisms from anoxic environments. 3.Model the thermodynamics and kinetics of ethylene synthetic pathways to guide engineering efforts in integrating best performing DHAP-ethylene pathway enzymes into model bacteria chassis for enhance ethylene yields. Through this project we discovered the initially missing genetic and enzyme component of the DHAP-ethylene pathway that directly synthesized ethylene and other important industrial compounds like methane and ethane from specific substrates. We uncovered and partially characterized a nitrogenase-like reductase that functions in DHAP-ethylene pathway specifically and in methionine synthesis in general. This nitrogenase-like system is called the Methylthio-Alkane Reductase (MAR) for its ability to cleave volatile organic sulfur compounds into methanethiol (CH3-SH) for methionine synthesis and a hydrocarbon byproduct. Key to the DHAP-ethylene pathway, MAR is the essential enzyme that cleaves 2-methylthioethanol (CH3-S-CH2-CH2-OH) into ethylene. Coordinately, we uncovered that the MAR genes and genes associated with conversion of methanethiol (CH3-SH) to methionine are under genetic control of a LysR Type Transcriptional Regulator called SalR, whose activity is dependent upon the amount of sulfate available to the cell. When sulfate as the preferred sulfur source for cell growth drops below 200 micromolar, SalR become active for expressing the MAR and methionine biosynthesis genes to enable the cell to grow from volatile organic sulfur compounds and make ethylene. Metabolic thermos-kinetic modeling revealed that these MAR reactions for ethylene and other hydrocarbon production are highly thermodynamically favorable and are one of the largest driving forces for ethylene production by the DHAP-ethylene pathway for high ethylene yields. Modeling also indicated that a key aldolase and to a lesser extent an isomerase of the DHAP-ethylene pathway for production of the ethylene precursor, 2-methylthioethanol, also would increase ethylene yields. Through metagenomic mining and gene synthesis by the JGI DNA synthesis program, over 500 aldolase and isomerase homologs were synthesized and screened. From this, variants were uncovered with substantially higher activity that increased ethylene yields 5-fold via the aldolase reaction and 1.5-fold via the isomerase reaction. Each of these elements that increase ethylene production were integrated together via plasmid under appropriate gene promoter elements in the phototrophic bacterium, Rhodospirillum rubrum, resulting in at least 3 orders of magnitude increase in ethylene yield from carbon dioxide feedstock.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy

Artificial neural networks (ANNs) are exceptional at forming non-linear correlations between multivariate input and target variables; however, they are often seen as a “black box” approach, since how ANNs form these correlations is somewhat ambiguous. Furthermore, the process underlying how ANNs learn from inlier and outlier samples within the input dataset is not fully understood. Intuitively, it is expected that training ANNs with inlier samples will increase prediction accuracy and training with outlier samples will reduce prediction accuracy; though, in practice, this is not always true. The present work identifies and analyzes inliers and outliers of existing experimental cetane number (CN) data encompassing a variety of compounds and compound groups. It also investigates how ANNs trained to predict CN perform with and without outliers included in the training data, and whether a relationship exists between inliers/outliers and ANN prediction accuracy across the whole dataset and for individual samples. Additionally, individual outlier compounds are analyzed, highlighting how they structurally differ from inlier compounds.

09 BIOMASS FUELS↗
DOE OSTI2024

Object-oriented analysis as a foundation for building climate storylines of compounding short-term drought and crop heat stress

Introduction: Crops are vulnerable to precipitation and heat extremes during late spring through summer. Methods: We analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041- 2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap. Results: Identified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type. Discussion: The information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying the impacts of compound extremes on agriculture

Agricultural production and food prices are affected by hydroclimatic extremes. There has been a growing amount of literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics, focusing on corn in the United States during the 1981–2015 period. Supported by statistical evidence, the findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes. The results also confirm that wet heat is more damaging than dry heat for corn. This study shows the average yield damage from heat stress has been up to four times more severe when combined with water stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Guanidine compounds for carbon dioxide capture

A method for removing carbon dioxide from a gaseous source, the method comprising: (i) contacting said gaseous source with an aqueous solution of a carbon dioxide sorbent that reacts with carbon dioxide to form an aqueous-soluble carbonate or bicarbonate salt of said carbon dioxide sorbent; (ii) contacting the aqueous solution from step (i) with a bis-iminoguanidine carbon dioxide complexing compound, which is different from the carbon dioxide sorbent, to result in precipitation of a carbonate or bicarbonate salt of said carbon dioxide complexing compound and regeneration of the carbon dioxide sorbent; and (iii) removing the precipitated carbonate or bicarbonate salt from the aqueous solution in step (ii) to result in a solid form of said carbonate or bicarbonate salt of the carbon dioxide complexing compound. The method may further include a step (iv) of regenerating the carbon dioxide complexing compound by subjecting the precipitated salt to sufficient heat and/or vacuum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Methods of forming aromatic containing compounds

Methods that include acylating an aromatic containing compound by reacting the aromatic containing compound with an anhydride containing compound to form an acylated aromatic containing compound.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Homopiperazine-based catalysts for neutralization of organophosphorus-based compounds

Novel compositions of matter based on homopiperazine precursor materials and forming a homopiperazine-based ligand are disclosed, along with suitable techniques and materials for the synthesis and utilization thereof. In particular various synthetic schemes and techniques for applying the disclosed compositions of matter as a decontaminating agent. The decontaminating agents include homopiperazine-based ligand-metal complexes that are particularly effective at neutralizing toxicity of nerve agents, pesticides, and other toxic organophosphorus-based compounds. In preferred approaches, the homopiperazine-based ligand-metal complexes act as catalysts to facilitate substitution of a leaving group of the organophosphorus-based compound with a functional group that does not permit the organophosphorus-based compound to inactivate acetylcholinesterase upon introduction of the organophosphorus-based compound to a living organism such as insects and mammals. Advantageously, the catalytic homopiperazine-based ligand-metal complexes are formed using inexpensive, readily-available precursor materials, and may be utilized to neutralize toxins without relying on damaging caustic reactants or environmentally unfriendly organic solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Volatile organic compound detection device

A volatile organic compound detection device includes a collector comprising: a collector material configured to collect volatile organic compounds given off from a patient's skin; a heater comprising a heating element, the heating element configured to emit a thermal pulse to desorb the volatile organic compounds from the collector material; and a flow channel configured to receive the volatile organic compounds desorbed by the heater; and a fastener configured to secure the collector to the patient's skin.

Arnold, Gary Stephen↗
DOE OSTI2019

First-Principle Studies of Ferrimagnetic Double Perovskite Ca{sub 2}FeMoO{sub 6} Compound

Using first-principle calculations, the structural, electronic, and magnetic properties of the Ca{sub 2}FeMoO{sub 6} double perovskite compound is investigated. Different spin-ordering: ferrimagnetic (FiM), ferromagnetic (FM), and anti-ferromagnetic (AFM1 and AFM2) using the generalized gradient approximation (GGA) and GGA + U (Hubbard Coulomb onsite correction) are evaluated to determine the theoretical ground state. The value of the Hubbard Coulomb U parameter is varied from 1 to 4 eV. The ground state is found to be a FM spin-ordering within the GGA approach and FiM spin-ordering within the GGA + U approach (where U ≥ 3 eV) which is the experimental preferred configuration. We obtain the FiM spin-ordered half semiconducting state within the GGA + U approach for the Ca{sub 2}FeMoO{sub 6} compound. Within the GGA + U (where U ≥ 3 eV) approach, the FM phase maintains a half-metallic character with a net magnetic moment of 4 0 μ{sub B}, whereas the FiM phase have a spin gapless semiconducting (SGS) behavior at U = 3 eV, and an insulating character at U = 4 eV, with a net magnetic moment of 4 0 μ{sub B}. The main features found in the density of states profile show that the hybridization of the Fe and Mo d orbitals play an important role in determining the electronic and magnetic character of this compound.

36 MATERIALS SCIENCE↗