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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 OSTI2018

Reactor design and integration with product detection to accelerate screening of electrocatalysts for carbon dioxide reduction

Identifying new catalyst materials for complex reactions such as the electrochemical reduction of CO 2 poses substantial instrumentation challenges due to the need to integrate reactor control with electrochemical and analytical instrumentation. Performing accelerated screening to enable exploration of a broad span of catalyst materials poses additional challenges due to the long time scales associated with accumulation of reaction products and the detection of the reaction products with traditional separation-based analytical methods. The catalyst screening techniques that have been reported for combinatorial studies of (photo)electrocatalysts do not meet the needs of CO 2 reduction catalyst research, prompting our development of a new electrochemical cell design and its integration to gas and liquid chromatography instruments. To enable rapid chromatography measurements while maintaining sensitivity to minor products, the electrochemical cell features low electrolyte and head space volumes compared to the catalyst surface area. Additionally, the cell is operated as a batch reactor with electrolyte recirculation to rapidly concentrate reaction products, which serves the present needs for rapidly detecting minor products and has additional implications for enabling product separations in industrial CO 2 electrolysis systems. To maintain near-saturation of CO 2 in aqueous electrolytes, we employ electrolyte nebulization through a CO 2 -rich headspace, achieving similar gas-liquid equilibration as vigorous CO 2 bubbling but without gas flow. The instrument is demonstrated with a series of electrochemical experiments on an Au-Pd combinatorial library, revealing non-monotonic variations in product distribution with respect to catalyst composition. The highly integrated analytical electrochemistry system is engineered to enable automation for rapid catalyst screening as well as deployment for a broad range of electrochemical reactions where product distribution is critical to the assessment of catalyst performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion–cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly. To overcome these challenges, computational approaches are increasingly employed to develop accurate predictive models that can accelerate IL discovery and design. In this study, we present two deep learning (DL) models for predicting the surface tension of ILs across a broad temperature range at a constant pressure. The models use simplified molecular input line entry system, SMILES, representations of ILs to extract molecular features as inputs. Both DL models demonstrate excellent agreement with experimental data, achieving an R 2 value of 0.990 and a root-mean-square error of 0.792 mN/m. In conclusion, these results offer valuable insights for the rapid screening and rational design of ILs with tailored surface tension values.

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

Structure- and Interaction-Based Design of Anti-SARS-CoV-2 Aptamers

Aptamer selection against novel infections is a complicated and time-consuming approach. Synergy can be achieved by using computational methods together with experimental procedures. In this study, we aim to develop a reliable methodology for a rational aptamer in silico et vitro design. The new approach combines multiple steps: (1) Molecular design, based on screening in a DNA aptamer library and directed mutagenesis to fit the protein tertiary structure; (2) 3D molecular modeling of the target; (3) Molecular docking of an aptamer with the protein; (4) Molecular dynamics (MD) simulations of the complexes; (5) Quantum-mechanical (QM) evaluation of the interactions between aptamer and target with further analysis; (6) Experimental verification at each cycle for structure and binding affinity by using small-angle X-ray scattering, cytometry, and fluorescence polarization. By using a new iterative design procedure, structure- and interaction-based drug design (SIBDD), a highly specific aptamer to the receptor-binding domain of the SARS-CoV-2 spike protein, was developed and validated. The SIBDD approach enhances speed of the high-affinity aptamers development from scratch, using a target protein structure. The method could be used to improve existing aptamers for stronger binding. This approach brings to an advanced level the development of novel affinity probes, functional nucleic acids. It offers a blueprint for the straightforward design of targeting molecules for new pathogen agents and emerging variants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Design of an alumina forming coating for Nb-base refractory alloys

Refractory multi-principal element alloys (RMPEAs) promise to significantly enhance gas turbine engine efficiency, but their poor oxidation performance inhibits their implementation. Alumina-forming bond coat alloys can provide oxidation protection, but discovering suitable chemistries remains a challenge. We employed a design methodology that screens for alumina-formation capability using Al activity and phase constitution predictions from CalPhaD (Thermo-Calc). Alloy down-selection from approximately 7,800 alloys in the Nb-Si-Ti-Al-Hf system was conducted via analysis of calculated thermodynamic properties with number-density topology style maps. This approach is validated by creating and testing the composition Nb 12 Si 23 Ti 24 Al 36 Hf 5 , which forms protective alumina scales up to 1400 °C and resists pesting at 800 °C. Further, the alloy has an average coefficient of thermal expansion of ~10.1 ppm/K, making it well matched to Nb-based refractory alloys. The methodology will be useful for the design of coatings for RMPEAs, enabling their implementation and significant efficiency benefits sooner.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Characterization of the Temperature Capabilities of Advanced Disk Alloy ME3

The successful development of an advanced powder metallurgy disk alloy, ME3, was initiated in the NASA High Speed Research/Enabling Propulsion Materials (HSR/EPM) Compressor/Turbine Disk program in cooperation with General Electric Engine Company and Pratt & Whitney Aircraft Engines. This alloy was designed using statistical screening and optimization of composition and processing variables to have extended durability at 1200 F in large disks. Disks of this alloy were produced at the conclusion of the program using a realistic scaled-up disk shape and processing to enable demonstration of these properties. The objective of the Ultra-Efficient Engine Technologies disk program was to assess the mechanical properties of these ME3 disks as functions of temperature in order to estimate the maximum temperature capabilities of this advanced alloy. These disks were sectioned, machined into specimens, and extensively tested. Additional sub-scale disks and blanks were processed and selectively tested to explore the effects of several processing variations on mechanical properties. Results indicate the baseline ME3 alloy and process can produce 1300 to 1350 F temperature capabilities, dependent on detailed disk and engine design property requirements.

Gabb, Timothy P.↗
DOE OSTI2021

Sustainable Lactic Acid Production from Lignocellulosic Biomass

Lignocellulosic biomass is a promising feedstock for sustainable biofuels and bioproducts. Among emerging bioproducts, lactic acid has attracted significant interest because of its growing application in many industries (e.g., packaging, medical, and pharmaceutical). In this study, BioSTEAM—an open-source platform—was leveraged for the design, simulation, and evaluation (via techno-economic analysis, TEA, and life cycle assessment, LCA) of lignocellulosic lactic acid biorefineries. With a minimum product selling price (MPSP) between 1.38 and 1.91 kg –1 (5th–95th percentiles, baseline at 1.57 kg –1 ), the biorefinery was capable of producing market-competitive lactic acid (market price between 1.7 and 2.1 kg –1 ), and its performance could be further enhanced (e.g., MPSP down to 1.09 kg –1 , global warming potential of 2.79 kg CO 2 -eq·kg –1 , and fossil energy consumption of 31.7 MJ·kg –1 ) with advancements in key technological parameters (fermentation yield and separation process conversions) and optimization in process operation. Sensitivity analyses focused on the fermentation unit (across titer, yield, and productivity; neutral vs low-pH fermentation) and feedstock characteristics (carbohydrate content and price) were also included to quantify their impact on the sustainability of the biorefinery. Overall, this research highlights the ability of agile TEA/LCA to screen promising biorefinery designs, prioritize research needs, and establish a road map for the continued development of bioproducts and biofuels.

09 BIOMASS FUELS↗
NASA NTRS2001

Disk Alloy Development

The advanced powder metallurgy disk alloy ME3 was designed using statistical screening and optimization of composition and processing variables in the NASA HSR/EPM disk program to have extended durability at 1150 to 1250 "Fin large disks. Scaled-up disks of this alloy were produced at the conclusion of this program to demonstrate these properties in realistic disk shapes. The objective of the UEET disk program was to assess the mechanical properties of these ME3 disks as functions of temperature, in order to estimate the maximum temperature capabilities of this advanced alloy. Scaled-up disks processed in the HSR/EPM Compressor / Turbine Disk program were sectioned, machined into specimens, and tested in tensile, creep, fatigue, and fatigue crack growth tests by NASA Glenn Research Center, in cooperation with General Electric Engine Company and Pratt & Whitney Aircraft Engines. Additional sub-scale disks and blanks were processed and tested to explore the effects of several processing variations on mechanical properties. Scaled-up disks of an advanced regional disk alloy, Alloy 10, were used to evaluate dual microstructure heat treatments. This allowed demonstration of an improved balance of properties in disks with higher strength and fatigue resistance in the bores and higher creep and dwell fatigue crack growth resistance in the rims. Results indicate the baseline ME3 alloy and process has 1300 to 1350 O F temperature capabilities, dependent on detailed disk and engine design property requirements. Chemistry and process enhancements show promise for further increasing temperature capabilities.

Gabb, Tim↗
DOE OSTI2017

Targeting long non-coding RNA ASBEL with oligonucleotide antagonist for breast cancer therapy

Long non-coding RNAs (lncRNAs) are defined as a class of RNA transcripts longer than 200 nucleotides encoded by mammalian genomes that lack protein-coding potential. LncRNA ASBEL has been identified as an anti-sense transcript of BTG3 (B cell translocation gene 3) gene, which encodes an anti-proliferation protein. Remarkable down-regulation of BTG3 has been reported in triple-negative breast cancer (TNBC). In the present study, a number of single-stranded modified anti-sense DNA oligonucleotides (antago) were designed, synthesized and screened for specific lncRNA ASBEL knockdown. We showed here that anti-ASBEL antago played a significant tumor suppressive role in TNBC by effective down-regulating lncRNA ASBEL, which in turn led to increased BTG3 expression. The obtained data suggest lncRNA ASBEL as a novel therapeutic target in TNBC. - Highlights: • LncRNA ASBEL is a promising therapeutic target in TNBC. • Antago is more powerful than siRNA in modulating lncRNA. • Antago3 is a useful therapeutic tool for TNBC treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗
DOE OSTI2023

In vitro continuous protein evolution empowered by machine learning and automation

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, here we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Directed combinatorial mutagenesis of Escherichia coli for complex phenotype engineering

Strain engineering for industrial production requires a targeted improvement of multiple complex traits, which range from pathway flux to tolerance to mixed sugar utilization. Here, we report the use of an iterative CRISPR EnAbled Trackable genome Engineering (iCREATE) method to engineer rapid glucose and xylose co-consumption and tolerance to hydrolysate inhibitors in E. coli. Deep mutagenesis libraries were rationally designed, constructed, and screened to target ~40,000 mutations across 30 genes. These libraries included global and high-level regulators that regulate global gene expression, transcription factors that play important roles in genome-level transcription, enzymes that function in the sugar transport system, NAD(P)H metabolism, and the aldehyde reduction system. Specific mutants that conferred increased growth in mixed sugars and hydrolysate tolerance conditions were isolated, confirmed, and evaluated for changes in genome-wide expression levels. As a result, we tested the strain with positive combinatorial mutations for 3-hydroxypropionic acid (3HP) production under high furfural and high acetate hydrolysate fermentation, which demonstrated a 7- and 8-fold increase in 3HP productivity relative to the parent strain, respectively.

09 BIOMASS FUELS↗
DOE OSTI2026

First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs

Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo 6+ , V 5+ ) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.

organic↗
DOE OSTI2019

Towards Prediction of Non-Radiative Decay Pathways in Organic Compounds I: The Case of Naphthalene Quantum Yields

Many emerging technologies depend on human’s ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescent labeling in biomedical imaging, light harvesting in photovoltaics, and electroluminescence in light-emitting devices. All of these systems suffer from non-radiative loss pathways that dissipate electronic energy as heat, which causes the overall system efficiency to be directly linked to quantum yield (Φ) of the molecular excited state. Unfortunately, Φ is very difficult to predict from first principles because the description of a slow non-radiative decay mechanism requires an accurate description of long-timescale excited-state quantum dynamics. In the present study, we introduce an efficient semi-empirical method of calculating the fluorescence quantum yield (Φ fl ) for molecular chromophores, which converts simple electronic energies computed using time-dependent density functional theory (TDDFT) into an estimate of Φ fl . As with all machine learning strategies, the algorithm needs to be trained on fluorescent dyes for which Φ fl ’s are known, so as to provide a black-box method which can later predict Φfl’s for chemically similar chromophores that have not been studied experimentally. Here, as a first illustration of how our proposed algorithm can be trained, we examine a family of 25 naphthalene derivatives. The simplest application of the energy gap law is found to be inadequate to explain the rates of internal conversion (IC) or intersystem crossing (ISC) – the electronic properties of at least one higher-lying electronic state (S n or T n ) or one far-from-equilibrium geometry are typically needed to obtain accurate results. Indeed, the key descriptors turn out to be the transition state between the Franck–Condon minimum and a distorted local minimum near an S 0 /S 1 conical intersection (which governs IC) and the magnitude of the spin-orbit coupling (which governs ISC). The resulting Φ fl ’s are predicted with reasonable accuracy (~ 22%), making our approach a promising ingredient for high-throughput screening and rational design of the molecular excited states with desired Φ fl ’s. We thus conclude that our model, while semi-empirical in nature, does in fact extract sound physical insight into the challenge of describing non-radiative relaxations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Where Are We Heading in Nanotechnology Environmental Health and Safety and Materials Characterization?

Every chemist, material scientist, physicist, engineer, or commercial enterprise involved in the synthesis and/or production of engineered nanomaterials (ENM) or nano-enabled products aspires to develop safe materials. Nanotechnology environmental health and safety (nanoEHS) is a research discipline that involves the study of the possible adverse health and biological effects that nanomaterials may have on humans and environmental organisms and ecosystems. NanoEHS research has provided a body of experimental evidence indicating the possibility of hazardous outcomes as a result of the interactions of unique ENM physicochemical properties with similar scale processes occurring at a wide range of nano/bio interfaces, including at biomolecular, cellular, subcellular, organ, systemic, whole organism, or ecosystems levels. This projected hazard and risk potential warrants rigorous attention to safety assessment, safe use, safe implementation, benign design, regulatory oversight, governance, and public awareness to address the possibility and prevention of nanotoxicity, now or at any time in the future.1 Thus, we should understand the properties of the ENMs that are responsible for the toxicological response, so that we can re-engineer their physicochemical characteristics for risk prevention and safer ENM design.2 However, in spite of widespread use, no human toxicological disease or major environmental impact has been reported for ENMs. Thus, while “Nanotoxicology” is a thriving sub-discipline of Nano-EHS, the use of the “root” word toxicology may elicit a feeling that nanomaterials are inherently toxic despite the fact that toxicity has not been established in real-life use so far. As a community, we may want to rename this sub-discipline as “Nanosafety,” since the objective is to use toxicology information to guide the design of safer nanomaterials for use in medicine, biology, electronics, lighting systems, etc. At ACS Nano, we are interested in publishing articles and forward-looking Perspectives and Reviews that determine and establish ENM physicochemical properties, structure-activity relationships, catalytic effects at the nano/bio interface, mechanistic injury responses, in vitro to in vivo prediction making, safer-by design strategies, actionable screening and detection methods, hazard and risk ranking, fate and transport, ENM categorization, theory and modeling, societal implications, and regulatory/governance decisions.3 Context is important in the immediate and longer-range impact of this research, as we are interested in realistic nanoEHS exposure scenarios conducted with systematic variation of ENM physicochemical properties rather than investigations of a single or a limited number of materials in isolated in vitro studies that only address cytotoxicity at unrealistic doses. In order to make these data useful for researchers, government and regulatory agencies, and other interested parties, these studies, where possible, should include either appropriate positive and negative controls or benchmark materials to answer the important question, “as compared to what?” Dosimetry should be explained in terms of appropriate dose metrics relative to the type of materials, their mechanisms of injury, and exposure conditions, using in vitro to in vivo extrapolations where possible. Another important component of these studies includes appropriate physicochemical characterization of the nanomaterials.

Nel, Andre↗
DOE OSTI2021

Correlating Li-Ion Solvation Structures and Electrode Potential Temperature Coefficients

Temperature coefficients (TCs) for either electrochemical cell voltages or potentials of individual electrodes have been widely utilized to study the thermal safety and cathode/anode phase changes of lithium (Li)-ion batteries. However, the fundamental significance of single electrode potential TCs is little known. Here, we discover that the Li-ion desolvation process during Li deposition/intercalation is accompanied by considerable entropy change, which significantly contributes to the measured Li/Li + electrode potential TCs. To explore this phenomenon, we compare the Li/Li + electrode potential TCs in a series of electrolyte formulations, where the interaction between Li-ion and solvent molecules occurs at varying strength as a function of both solvent and anion species as well as salt concentrations. As a result, we establish correlations between electrode potential TCs and Li-ion solvation structures and further verify them by ab initio molecular dynamics simulations. We show that measurements of Li/Li + electrode potential TCs provide valuable knowledge regarding the Li-ion solvation environments and could serve as a screening tool when designing future electrolytes for Li-ion/Li metal batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening

Computational screening techniques have been found to be an effective alternative to the trial and error of experimentation for discovery of new materials. With increased interest in development of advanced electrical energy storage systems, it is essential to find new electrolytes that function effectively. This Perspective reviews various methods for screening electrolytes and then describes a hierarchical computational scheme to screen multiple properties of advanced electrical energy storage electrolytes using high-throughput quantum chemical calculations. The approach effectively down-selects a large pool of candidates based on successive property evaluation. As an example, results of screening are presented for redox potentials, solvation energies, and structural changes of similar to 1400 organic molecules for nonaqueous redox flow batteries. Importantly, on the basis of high-throughput screening, in silico design of suitable candidate molecules for synthesis and electrochemical testing can be achieved. We anticipate that the computational approach described in this Perspective coupled with experimentation will have a significant role to play in the discovery of materials for future energy needs.

25 ENERGY STORAGE↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Assessing ligand selectivity for uranium over vanadium ions to aid in the discovery of superior adsorbents for extraction of UO 2 2+ from seawater

Uranium is used as the basic fuel for nuclear power plants, which generate significant amounts of electricity and have life cycle carbon emissions that are as low as renewable energy sources. However, the extraction of this valuable energy commodity from the ground remains controversial, mainly because of environmental and health impacts. Alternatively, seawater offers an enormous uranium resource that may be tapped at minimal environmental cost. Nowadays, amidoxime polymers are the most widely utilized sorbent materials for large-scale extraction of uranium from seawater, but they are not perfectly selective for uranyl, UO 2 2+ . In particular, the competition between UO 2 2+ and VO 2+ /VO2+ cations poses a significant challenge to the effi-cient mining of UO 2 2+ . Thus, screening and rational design of more selective ligands must be accomplished. One of the key components in achieving this goal is the establishment of computational techniques capable of assessing ligand selec-tivity trends. Here, we report an approach based on quantum chemical calculations that achieves high accuracy in repro-ducing experimental aqueous stability constants for VO 2+ /VO 2 + complexes with ten different oxygen donor lig-ands. The predictive power of the developed computational protocol was demonstrated for amidoxime-type ligands, providing greater insights into new design strategies for the development of the next generation of adsorbents with high selectivity toward UO 2 2+ over VO 2+ /VO 2 + ions. Furthermore, the results of calculations suggest that alkylation of amidox-ime moieties present in poly(acrylamidoxime) sorbents can be a potential route to better discrimination between the uranyl and competing vanadium ions within seawater.

07 ISOTOPE AND RADIATION SOURCES↗