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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

IMPECCABLE: Integrated Modeling Pipeline for COVID Cure by Assessing Better Leads

ABSTRACT The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100× to 1000× improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ~10 11 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Crystal structure of the human PRPK–TPRKB complex

Mutations of the p53-related protein kinase (PRPK) and TP53RK-binding protein (TPRKB) cause Galloway-Mowat syndrome (GAMOS) and are found in various human cancers. We have previously shown that small compounds targeting PRPK showed anti-cancer activity against colon and skin cancer. Here we present the 2.53 Å crystal structure of the human PRPK-TPRKB-AMPPNP (adenylyl-imidodiphosphate) complex. The structure reveals details in PRPK-AMPPNP coordination and PRPK-TPRKB interaction. PRPK appears in an active conformation, albeit lacking the conventional kinase activation loop. We constructed a structural model of the human EKC/KEOPS complex, composed of PRPK, TPRKB, OSGEP, LAGE3, and GON7. Disease mutations in PRPK and TPRKB are mapped into the structure, and we show that one mutation, PRPK K238Nfs*2, lost the binding to OSGEP. Our structure also makes the virtual screening possible and paves the way for more rational drug design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

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

Exploring the Origin of Anionic Redox Activity in Super Li-Rich Iron Oxide-Based High-Energy-Density Cathode Materials

Super alkali-rich materials (alkali:transition metal ratio of ≥2:1), such as Li 5 FeO 4 , exhibit the potential to realize anionic redox upon deep delithiation. Li 5 FeO 4 was shown to undergo reversible cycling between Li 4 FeO 3.5 and Li 3 FeO 3.5 with combined cation/anion redox, a remarkable capacity of 189 mAh/g, and no O 2 release. However, the impact of phase transformations on the reaction thermodynamics and the correlation between the structural changes and reaction reversibility remain unclear. In this study, we use first-principles calculations to examine the delithiation and (re)lithiation reactions of Li 5 FeO 4 . We show that the experimentally observed charge and discharge processes go through non-equilibrium pathways. Upon delithiation, the compound undergoes a phase transformation from Li 5 FeO 4 , with tetrahedrally coordinated (T d ) Fe ions, to a delithiated disordered rocksalt structure, with octahedral (O h ) Fe ions. Fe-ion migration has an asymmetric kinetic barrier that makes T d → O h migration facile, whereas the reverse process has a much larger barrier, explaining the difficulties in reaction reversibility. We further elucidate the transition metal and O redox sequences during the charge cycle and identify the complex electrochemistry associated with the dual participation of cationic redox (Fe 3+ /Fe 4+ ) and anionic redox (O 2– /O – /O o ). Armed with this knowledge, we conduct high-throughput screening of known alkali-rich transition metal oxides by evaluating their potential to enable reversible anionic redox, with multiple candidates proposed for further experimental trials. Furthermore, our work provides a useful guide for the further development of super alkali-rich anionic-redox-active electrodes for high-energy-density batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High Strength Steel-Aluminum Components by Vaporizing Foil Actuator Welding

This project aimed to address the challenge of effectively welding dissimilar materials—high-strength steel and high-strength aluminum for creating lightweight, multi-material automotive components. For automotive companies, reducing weight of a vehicle is critical task regulated by the government to solve the issue of greenhouse gas emissions. Production of lightweight cars and trucks can be achieved by substitution of current all-steel structures with multi-material lightweight structures that include high strength-to-weight-ration materials such as high-strength steels, aluminum alloys, magnesium alloys, titanium alloys, coupled with lightweight designs. This requires dissimilar metal welding, which is challenging for state of the art joining processes such as resistance spot welding. The cycle of melting-cooling-freezing during traditional welding that can easily ruin the designed outstanding properties of the advanced base metals, such as aluminum alloys, making the welded area much weaker than the base metals. To weld two different metals with great difference in melting points, such as aluminum and steel, it’s even more difficult or impossible because of the formation of brittle intermetallic compounds at the welded interface. In this project, a novel welding method, developed at OSU, was selected for validation and development. This novel technology enables welding by impact without melting and proves to be robust to join various dissimilar lightweight metals. Termed as vaporizing foil actuator welding or VFAW, the technology uses a thin aluminum foil that is rapidly vaporized by a high current pulse to produce an explosive-like pressure pulse to drive one metallic piece into another at the high speed required for impact welding. This project entailed development of the early-stage welding technology in terms of (a) the consumables, the welding apparatus and the power sources, (b) coupon scale screening of many material combinations including corrosion studies, (c) computational modeling and design of the welded interface as well as of the multi-material prototype component, and (d) mechanical testing for strength and durability at coupon scale and to a certain extent the prototype scale. The all-steel engine cradle of 2016 Chevrolet Cruze was chosen as the baseline prototype component. The target set for the project was to demonstrate a 20% weight reduction at a cost premium of less than $\$ $5/lb saved without compromising on baseline mechanical properties. At project completion, a 12% lighter prototype component was demonstrated with an estimated cost premium of $\$ $9.8/lb saved. Besides prototype level demonstration of the technology, this project also enabled elevation of the technology’s readiness level to where a hydraulically actuated welding head was developed and made ready for deployment at a research and development facility for Tier 1 automotive supplier.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the Gas-Phase Behavior of Nitazene Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry

2-benzylbenzimidazoles, or “nitazenes”, are a class of novel synthetic opioids (NSOs) that are increasingly being detected alongside fentanyl analogs and other opioids in drug overdose cases. Nitazenes can be 20x more potent than fentanyl but are not routinely tested for during postmortem or clinical toxicology drug screens; thus, their prevalence in drug overdose cases may be under-reported. Traditional analytical workflows utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) often require additional confirmation with authentic reference standards to identify a novel nitazene. However, additional analytical measurements with ion mobility spectrometry (IMS) may provide a path towards reference-free identification, which would greatly accelerate NSO identification rates in toxicology labs. Presented here are the first IMS and collision cross section (CCS) measurements on a set of fourteen nitazene analogs using a Structures for Lossless Ion Manipulations (SLIM)-Orbitrap MS. All nitazenes exhibited two high intensity baseline-separated IMS distributions, which fentanyls and other drug and drug-like compounds also exhibit. Incorporating water into the electrospray ionization (ESI) solution caused the intensities of the higher mobility IMS distributions to increase the intensities of the lower mobility IMS distributions to decrease. Nitazenes lacking a nitro group at the R1 position exhibited the greatest shifts in signal intensities due to water. Furthermore, IMS-MS/MS experiments showed that the higher mobility IMS distributions of all nitazenes produced fragment ions with m/z 72, 100, and other low intensity fragments while the lower mobility IMS distributions only produced fragment ions with m/z 72 and 100. The IMS, solvent, and fragmentation studies provide experimental evidence that nitazenes potentially exhibit three gas-phase protomers. In conclusion, the cyclic IMS capability of SLIM was also employed to partially resolve four sets of structurally similar nitazene isomers (e.g., protonitazene/isotonitazene, butonitazene/isobutonitazene/secbutonitazene), showcasing the potential of using high-resolution IMS separations in MS-based workflows for reference-free identification of emerging nitazenes and other NSOs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Human in vitro metabolism of an environmental mixture of polycyclic aromatic hydrocarbons (PAH) found at the Portland Harbor Superfund Site

Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants that pose health risks to humans. Toxicity testing approaches of PAHs have evolved from traditional rodent models to New Approach Methodologies (NAMs), such as high-throughput screening in zebrafish, enabling rapid evaluation of chemical hazards. However, translating toxicity findings from laboratory systems to humans remains difficult due to complexity and species-specific differences. Chemical dosimetry modeling offers a quantitative framework to bridge this gap, but its accuracy depends on robust knowledge of PAH metabolism. The objective of this study was to measure human metabolism rates of Supermix-10, the ten most abundant PAHs found at the Portland Harbor Superfund Site, to support development of human pharmacokinetic models. We incubated individual PAHs from Supermix-10 in pooled human liver microsomes and quantified parent PAH disappearance using high-performance liquid chromatography (HPLC) with UV and florescent detection. To assess the potential of mixture interactions, we also measured metabolism of all 10 compounds in an equimolar mixture and compared rates of parent disappearance to those observed for individual PAHs. All Supermix-10 PAHs demonstrated rapid parent compound disappearance in human hepatic microsomes. PAHs grouped into three metabolism patterns: high metabolism rates and capacity (2-methylnaphthalene, acenaphthylene, fluorene, naphthalene), high affinity metabolism that rapidly achieves low-level saturation (benzo[a]anthracene, chrysene), and moderate metabolism rates and capacity (fluoranthene, pyrene, retene, phenanthrene). Smaller PAHs exhibited faster metabolism, and higher metabolism rates correlated inversely with molecular weight. When incubated in an equimolar mixture, Supermix-10 demonstrated significantly slower metabolism (47–89 %) compared to metabolism of individual PAHs at the same concentration. These findings enhance our understanding of PAH metabolism in humans and demonstrate significant mixture interactions under the conditions tested. Furthermore, our findings offer insights into the metabolic behavior of Supermix-10 and provide critical metabolism rate data to support the development of physiological based pharmacokinetic (PBPK) models. Dosimetry models can translate PAH chemical dosimetry from high-throughput testing platforms, like zebrafish and cellular system assays, to human exposures enhancing the accuracy and reliability of PAH risk assessments.

2-methylnaphthalene↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational Investigation of a CO 2 Conversion Strategy via Diels–Alder Reaction in a Carbon Capture Solvent

Molecular-level insights into reactive separations are crucial for the design of new conversion pathways of carbon dioxide (CO 2 ). This work explores a postulated pathway that directs CO 2 to undergo inverse-electron-demand Diels–Alder reactions to produce heterocycles using the CO 2 chemically fixed on water-lean solvent molecules. Density functional theory calculations are applied to evaluate the lowest unoccupied molecular orbital (LUMO) energies of three types of reactants (1,3-butadiene, 1,3-cyclohexadiene, and 1,2,4,5-tetrazine) with various functional substituents. These calculations also provide a data set (5.8k data) for developing a machine learning model to efficiently predict LUMO energies. A computational screening of LUMO energies for an additional 47k diene and tetrazine candidates is performed, and a list of candidates with lowered LUMO energies by electron-withdrawing substituents is provided. These candidates are further examined by their reaction energy barriers computed from the interatomic potential or density functional theory. Two major energy barriers are identified, one for the proton transfer within the water-lean solvent and the other for the CO 2 transfer from the solvent molecule to the reactant candidate (diene or tetrazine). The functional substituents have a more significant impact on the second barrier but a very slight one on the first barrier. This exploratory work demonstrates a new possibility for guiding experimental efforts toward the chemical conversion of fixated CO 2 to value-added compounds.

Chemical reactions↗
DOE OSTI2020

Emerging Contaminants: Identification, Life Cycle, and Key Challenges - 20415

The term 'emerging contaminants' and its multiple variants has come to refer to previously overlooked compounds discovered in the environment which represent a potential threat to human and ecological receptors. These compounds may be discovered in the environment as new analytical techniques are developed and/or as new monitoring programs for drinking water and groundwater are implemented, while the threat to receptors may be realized through advances in understanding of toxicology and pathways to receptors. Emerging contaminants present unique and considerable challenges as demands to address them can outpace the understanding of their toxicity, their need for regulation, their occurrence, and techniques for treating the environmental media they affect. With these challenges in mind, this paper serves as a primer for understanding emerging contaminants. The paper will lay a foundation for understanding emerging contaminants through three key topics: 1. Who identifies emerging contaminants? An overview of the various stakeholder groups around the world working on identifying potential emerging contaminants and developing systems for screening potential candidates, prioritizing, and ultimately generating regulations will be provided. 2. How Chemicals Rise into Awareness as Emerging Contaminants. The process by which contaminants emerge through a cycle of awareness, characterization, management and regulation will be discussed. Current emerging contaminants will be discussed in the context of the life cycle: emerging contaminants where impacts to drinking water supplies are driving public awareness (e.g. per and polyfuoroalkyl substances (PFASs) and 1,4-dioxane), emerging contaminants impacted by changing regulatory standards or development and debate on toxicity (e.g., hexavalent chromium, 1,2,3-trichloropropane, trichloroethylene in vapor intrusion), and those with evolving understanding of impacts and usage (e.g., microplastics). 3. Key Challenges. Unique challenges associated with emerging contaminants, including visibility and public sensitivity, uncertainty and vulnerability, will be discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

The impacts of charge transfer, localization, and metallicity on hydrogen retention and transport capacity

Solid state hydrides such as early transition metal hydrides are of inestimable importance for the future of hydrogen energy and are actively being investigated for energy conversion and storage applications such as fuel cells, solid-state batteries and neutron moderators. The retention and transport behavior of hydrogen in these hydrides has a huge role on the extended performance of components. While early transition-metal-based compounds exhibit many peculiar properties due to their unique correlated electronic signatures arising from -orbital electrons, the fundamental chemistry and transport behavior of hydrogen in such hydrides is not well understood. In the present work, using density functional theory, a highly intricate bonding feature is revealed through the theoretical investigation of the electronic structure of early transition metal hydrides YH 2 and ZrH 2 . In particular, a pronounced charge transfer from the transition element to H, results in localized electron densities at deep energy levels. The interplay between intrinsic charge transfer, charge localization, and metallicity in YH 2 and ZrH 2 leads to strong chemical bonding between metal and hydrogen atoms and large energy barriers for the migration of hydrogen vacancies. Specifically, hydrogen vacancies are found to be stable in the neutral state due to electron screening effects, accompanied by substantially high migration barriers between 0.8–1.2 eV along different crystallographic directions. In contrast, recent literature shows the migration barrier for charged H vacancies in insulating s-block metal hydrides lie between 0.1–0.4 eV, which is suitable for fast conduction applications. This pivotal electron structure difference exploited between early transition metal hydrides and alkali/alkaline earth metal hydrides determines extended hydrogen retention in these early transition metal hydrides. Finally, this work explains fundamental differences between the electronic structure of s-block and d-block metal hydrides, and its impact on the mobility of hydrogen vacancies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Metal Oxide Sorbents for the Separation of Radium and Actinium

The inorganic, normal-phase sorbents alumina, titania, and zirconia were evaluated and screened for the separation of Ba, La, 228 Ra, 228 Ac, and 212 Pb in a dilute acid matrix. The results show that the sorbents are capable of fractionating the ions between pH 1 and 8. A stationary zirconia column was used to isolate Ba from La and Ra from Ac/Pb; elution profiles of each separation are presented. The utility and implications of these sorbents are discussed with respect to large-scale production of 225 Ac from radium targets.

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

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Determining Multi-Component Phase Diagrams with Desired Characteristics Using Active Learning

Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi-component systems from a high-dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi-component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1-ω)(Ba 0.61 Ca 0.28 Sr 0.11 TiO 3 )-ω(BaTi 0.888 Zr 0.0616 Sn 0.0028 Hf 0.0476 O 3 ) with a relatively high transition temperature and triple point, as well as the NiTi-based pseudo-binary phase diagram (1-ω)(Ti 0.309 Ni 0.485 Hf 0.20 Zr 0.006 )-ω(Ti 0.309 Ni 0.485 Hf 0.07 Zr 0.068 Nb 0.068 ) designed for high transition temperature (ω ≤ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today’s computational methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Standard Analytical Methods for Pyrolysis Bio-Oils

There has been significant recent interest in the production of renewable fuels and chemicals from biomass and waste feedstocks. Pyrolysis pathways produce a liquid bio-oil product, which must be processed further, or upgraded, to yield fuel or chemical products. Bio-oils are very complex and often unstable samples, and research and development on upgrading processes needs reliable analytical information. In particular, chemical characterization techniques are needed to quantify both functional groups and individual compounds present in bio-oils. Reliable analytics are also needed to enable the bioenergy industry, as industrial facilities often have different analytical needs and capabilities than research facilities. In this presentation, we will discuss the development of a suite of standard analytical methods for pyrolysis bio-oils. Analytical methods to be discussed include: Determination of Carbon, Hydrogen, Nitrogen, and Oxygen in bio-oils; Accelerated Aging of Fast Pyrolysis Bio-oil using Carbonyl Titration; Determination of Water Content in Bio-oils by Volumetric Karl Fischer Titration; Determination of Carbon Functional Groups; Elemental Analysis of Bio-oils by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) - Na, K, Mg, Ca, S, P, and Fe; Determination of Phenolic Groups in Bio-oils using Revised Folin-Ciocalteu Methods: Single Cuvette and Plate Reader; Corrosivity of Bio-oils: Screening Test using Metal Leaching; Determination of Biogenic Content by 14C Measurement using Liquid Scintillation Counter. These new analytical methods are publicly available as Laboratory Analytical Procedures (https://www.nrel.gov/bioenergy/bio-oil-analysis.html), along with previously developed standard methods: GC-MS, Acid Titration, Carbonyl Titration, and 31P NMR. Additionally, the development of diffusion ordered NMR for characterization of bio-oil molecular weight will be discussed. Collectively, this suite of analytical methods represents the most comprehensive set of standard methods available for pyrolysis bio-oils. These standard methods are commonly used by the bioenergy community, and provide reliable information that enables research, scaleup, and industrial processing of biomass to produce renewable fuels and chemicals.

analytical↗
DOE OSTI2021

Diagnostic, Prognostic, and Therapeutic Use of Radiopharmaceuticals in the Context of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) outbreak has devastated the healthcare systems and economies of over 200 countries in just a few months. The etiological agent of COVID-19, SARS-CoV-2, is a highly contagious virus that can be transmitted by asymptomatic and symptomatic carriers alike. While in vitro testing techniques have allowed for population-wide screening, prognostic tools are required to assess the disease severity and therapeutic response, contributing to improve the patient clinical outcomes. Moreover, no specific antiviral against COVID-19 exists at the time of publication, severely limiting treatment against the infection. Hence, there is an urgent clinical need for innovative therapeutic strategies that may contribute to manage the COVID-19 outbreak and prevent future pandemics. In this work, we critically examine recent diagnostic, prognostic, and therapeutic advancements for COVID-19 in the field of radiopharmaceuticals. First, we summarize the gold standard techniques used to diagnose COVID-19, including in vitro assays and imaging techniques, and then discuss how radionuclide-based nuclear imaging provides complementary information for prognosis and treatment management of infected patients. Second, we introduce new emerging types of radiotherapies that employ radioimmunoconjugates, which have shown selective cytotoxic response in oncological studies, and critically analyze how these compounds could be used as therapeutic agents against SARS-CoV-2. Finally, this Perspective further discusses the emerging applications of radionuclides to study the behavior of pulmonary SARS-CoV-2 aerosol particles.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Synthesis, Characterization, and Biological Activity of Hybrid Thiosemicarbazone–Alkylthiocarbamate Metal Complexes

A series of hybrid ligands (H 2 L 1 –H 2 L 3 ) derived from 4-methyl-3-thiosemicarbazide and hydrazinecarbothioic acid O-alkyl esters were synthesized and characterized by NMR. The ligands were chelated with copper (4–6), nickel (7–9), and zinc (10–12) and characterized by spectroscopy, electrochemistry, and single crystal X-ray crystallography. The chelated metals displayed substantial anodic shifts in the Cu II/I reduction potential of ~160 mV relative to their bis(thiosemicarbazone) analogues. The metal chelates 4–12 were evaluated for potential anticancer activity by MTT assays, and selected results were confirmed by clonogenic and trypan blue assays. The copper derivatives 4 and 6 were found to have potent and cancer-selective antiproliferative effects, with GI 50 values less than 100 nM in A549 lung adenocarcinoma cells compared with at least 20-fold less activity in IMR90 nonmalignant lung fibroblasts. In comparison, the nickel complexes were much less active and had little cancer-selectivity. Varying by ligand, the zinc complexes were less potent or had comparable activity compared to that of the corresponding copper complex. UV–visible spectroscopy indicated that zinc complex 10 was transmetalated in the presence of equimolar copper, whereas nickel complex 7 was not. Copper complexes 4 and 6 were also assessed in the NCI60 screen and were found to have cytotoxic activity against most solid tumor cell lines. In MTT assays, 4 and 6 were substantially more active against A549 cancer cells than Cu(ATSM) and were more cancer-selective (for A549 compared to IMR-90) than Cu(GTSM). Finally, our results suggest that hybrid thiosemicarbazone–alkylthiocarbamate copper complexes have potential for development as new anticancer agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗