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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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NASA NTRS2003

Terrain Portrayal for Head-Down Displays Flight Test

The Synthetic Vision Systems General Aviation (SVS-GA) element of NASA's Aviation Safety Program is developing technology to eliminate low visibility induced General Aviation (GA) accidents through the application of synthetic vision techniques. SVS displays present computer generated 3-dimensional imagery of the surrounding terrain to greatly enhance pilot's situation awareness (SA), reducing or eliminating Controlled Flight into Terrain (CFIT), as well as Low-Visibility Loss of Control (LVLOC) accidents. In addition to substantial safety benefits, SVS displays have many potential operational benefits that can lead to flight in instrument meteorological conditions (IMC) resembling those conducted in visual meteorological conditions (VMC). Potential benefits could include lower landing minimums, more approach options, reduced training time, etc. SVS conducted research will develop display concepts providing the pilot with an unobstructed view of the outside terrain, regardless of weather conditions and time of day. A critical component of SVS displays is the appropriate presentation of terrain to the pilot. The relationship between the realism of the terrain presentation and resulting enhancements of pilot SA and pilot performance has been largely undefined. Comprised of coordinated simulation and flight test efforts, the terrain portrayal for head-down displays (TP-HDD) test series examined the effects of two primary elements of terrain portrayal: variations of digital elevation model (DEM) resolution and terrain texturing. Variations in DEM resolution ranged from sparsely spaced (30 arc-sec/2,953ft) to very closely spaced data (1 arc-sec/98 ft). Variations in texture involved three primary methods: constant color, elevation-based generic, and photo-realistic, along with a secondary depth cue enhancer in the form of a fishnet grid overlay. The TP-HDD test series was designed to provide comprehensive data to enable design trades to optimize all SVS applications, as well as develop requirements and recommendations to facilitate the implementation and certification of SVS displays. The TP-HDD flight experiment utilized the NASA LaRC Cessna 206 Stationaire and evaluated eight terrain portrayal concepts in an effort to confirm and extend results from the previously conducted TP-HDD simulation experiment. A total of 15 evaluation pilots, of various qualifications, accumulated over 75 hours of dedicated research flight time at Newport News (PHF) and Roanoke (ROA), VA, airports from August through October, 2002. This report will present results from the portion of testing conducted at Roanoke, VA.

Hughes, Monica F.↗
DOE OSTI2026

Multi-omic characterization of a soil microbial consortium reveals critical role of succinate and glutamate metabolism during calcium carbonate precipitation

Microbially induced calcium carbonate precipitation (MICP) holds potential for use in soil stabilization and carbon sequestration, with the overall efficiency of the process being a major determinant for use in many environmental and civil engineering applications. While the biogeochemical pathways and enzymes driving MICP are known, the microbial metabolic networks and community dynamics underlying such precipitation remain poorly characterized. To address this gap, we developed a four-member consortium of soil bacteria (Curtobacterium flaccumfaciens, Rhodococcus qingshengii, Microbacterium sp., and Bacillus toyonensis), termed carbon storing consortium - A (CSC-A), that is capable of MICP. Prior work shows that MICP production is higher in CSC-A compared to the sum of carbonate produced by each member, suggesting carbonate production is driven by consortium dynamics. To that end we used a multi-omic integration approach of genomics, transcriptomics, and metabolomics to investigate potential inter-species interactions that may influence the MICP phenotype. Genomic life history characterizations identified evidence of niche specialization by B. toyonensis and Microbacterium, while metatranscriptomic analysis suggests R. qingshengii is a keystone species during growth in urea. By comparing individual species’ metabolomes to the metabolic profile of a shared well of precipitated metabolites, we identified over 200 metabolites predicted to be produced or consumed by CSC-A members. Integrating both data types to search the KEGG reactome highlighted a network centered around glutamine metabolism and branched chain amino acid biosynthesis under regulation during CSC-A growth in urea. Succinate metabolism was also a major node in this network and laboratory assays confirmed that increasing the amount of succinate in the growth medium leads to increased carbonate precipitation by CSC-A, a critical confirmation of our modeling approach. By isolating and identifying the interconnected metabolic components underlying MICP in CSC-A, we identified keystone taxa, metabolites, and pathways important for future optimization of the application of this consortia to carbonate precipitation.

carbon storing consortium - A (CSC-A)↗
DOE OSTI2025

Leveraging prior mean models for faster Bayesian optimization of particle accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Baseflow Identification via Explainable AI With Kolmogorov‐Arnold Networks

Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance

baseflow↗
NASA NTRS1982

Optimal weighting of data to detect climatic change - Application to the carbon dioxide problem

It is suggested that a weighting of surface temperature data, using information about the expected level of warming in different seasons and geographical regions and statistical information about the amount of natural variability in surface temperature, can improve the chances of early detection of carbon dioxide concentration-induced climatic warming. A preliminary analysis of the optimal weighting method presented suggests that it is 25 per cent more effective in revealing surface warming than the conventional method, in virtue of the fact that 25 per cent more data must conventionally be analyzed in order to arrive at a similar probability of detection. An approximate calculation suggests that the warming ought to have already been detected, if the only sources of significant surface temperature variability had time scales of less than one year.

Bell, T. L.↗
DOE OSTI2020

Vertex Reordering for Real-world Graphs and Applications: An Empirical Evaluation

Vertex reordering is a way to improve locality in graph computations. Given an input (or ``natural'') order, reordering aims to compute an alternate permutation of the vertices that is aimed at maximizing a locality-based objective. Given decades of research on this topic, there are tens of graph reordering schemes, and there are also several linear arrangement ``gap'' measures for treatment as objectives. However, a comprehensive empirical analysis of the efficacy of the ordering schemes against the different gap measures, and against real-world applications is currently lacking. In this study, we present an extensive empirical evaluation of up to 11 ordering schemes, taken from different classes of approaches, on a set of 34 real-world graphs emerging from different application domains. Our study is presented in two parts: a) a thorough comparative evaluation of the different ordering schemes on their effectiveness to optimize different linear arrangement gap measures, relevant to preserving locality; and b) extensive evaluation of the impact of the ordering schemes on two real-world, parallel graph applications, namely, community detection and influence maximization. Our studies show a significant divergence among the ordering schemes (up to $40\times$ between the best and the poor) in their effectiveness to reduce the gap measures; and a wide ranging impact of the ordering schemes on various aspects including application runtime (up to $4\times$), memory and cache use, load balancing, and parallel work and efficiency. The comparative study also help reveal the nuances of a parallel environment (compared to serial) on the ordering schemes and their role in optimizing applications.

Barik, Reet↗
DOE OSTI2020

Photoinduced charge transfer in transition metal dichalcogenide heterojunctions – towards next generation energy technologies

Increasing interest in using two-dimensional transition metal dichalcogenides (2D TMDCs) in optical energy conversion technologies creates a demand for improving the yields and lifetimes of photogenerated charge carriers. Despite inherently fast photocarrier decay in neat 2D TMDCs, the unique photophysics in these quantum-confined systems motivates continued effort to control the evolution of photoexcited states and create functional devices. Here, an intriguing strategy to accomplish this goal is to employ TMDCs in heterojunctions with appropriate semiconductors, where energy level offsets drive photoinduced charge transfer (PCT) across material interfaces. PCT in TMDC-based systems can be optimized for many different applications, such as driving free carriers to photocatalytic sites for redox reactions like water splitting, extracting charge to perform work in photovoltaics or photodetectors, and manipulating the spin and momentum valley electronic degrees of freedom for quantum computing systems. Here, we review recent strides in optimizing PCT for such applications through greater fundamental understanding of the photophysics that occurs at TMDC/semiconductor interfaces. After giving an overview of isolated TMDC properties, synthetic methods, and the basics of PCT, we discuss TMDCs in heterojunctions with several classes of materials, including other TMDCs, small molecule semiconductors, polymers, single-walled carbon nanotubes, quantum dots, perovskites, and electrolytes. In addition to highlighting the unique benefits of each materials category, we also identify parallels across common themes, such as the roles of charge-transfer states, spin, electronic coupling, delocalization, interfacial atomic morphology, and the precise design of energy landscapes to direct charge and energy motion. We hope to capture a broad range of the valuable work in this fast-paced field to inspire new research directions for employing PCT in targeted TMDC-based systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Operando Tailoring of Defects and Strains in Corrugated β-Ni(OH) 2 Nanosheets for Stable and High-Rate Energy Storage

Nickel hydroxide represents a technologically important material for energy storage, such as hybrid supercapacitors. It has two different crystallographic polymorphs, α- and β-Ni(OH) 2 , showing advantages in either theoretical capacity or cycling/rate performance, manifesting a trade-off trend that needs to be optimized for practical applications. In this work, the synergistic superiorities in both activity and stability of corrugated β-Ni(OH) 2 nanosheets are demonstrated through an electrochemical abuse approach. With ≈91% capacity retention after 10 000 cycles, the corrugated β-Ni(OH) 2 nanosheets can deliver a gravimetric capacity of 457 C g -1 at a high current density of 30 A g -1 , which is nearly two and four times that of the regular α- and β-Ni(OH) 2 , respectively. Operando spectroscopy and finite element analysis reveal that greatly enhanced chemical activity and structural robustness can be attributed to the in situ tailored lattice defects and the strain-induced highly curved micromorphology. This work demonstrates a multi-scale defect-and-strain co-design strategy, which is helpful for rational design and tuned fabrication of next-generation electrode materials for stable and high-rate energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phase Transformation and Water Adsorption Behavior of As‐Deposited and Annealed Ru Metal Thin Films Prepared by Atomic Layer Deposition

ABSTRACT Surfaces play a central role in catalytic processes, and understanding the transformation of ruthenium metal into ruthenium oxide during annealing is essential for tailoring functional catalytic interfaces. In this study, we systematically investigate ≈22 nm thick Ru metal films deposited by atomic layer deposition (ALD) at 300°C, focusing on their chemical composition, structural evolution, and surface hydration behavior following post‐deposition annealing in air from 400 to 600°C. Lab‐based and synchrotron X‐ray photoelectron spectroscopy (XPS) reveal a gradual conversion from metallic Ru to fully oxidized Ru 4+ with increasing annealing temperature, accompanied by a corresponding increase in lattice oxygen. X‐ray diffraction (XRD) shows amorphous Ru oxide phases at 400°C and 500°C that evolve into crystalline RuO 2 at 600°C, while atomic force microscopy (AFM) indicates enhanced grain growth and surface roughening upon annealing. Ambient‐pressure XPS (AP‐XPS) under controlled H 2 O vapor environments (1–17 Torr) demonstrates that samples annealed at 400°C and 500°C exhibit initially high hydroxyl coverage that decreases with increasing water vapor pressure, concurrent with a rise in molecular H 2 O adsorption. In contrast, the crystalline RuO 2 surface formed at 600°C maintains stable hydroxylation and supports increased water uptake. Overall, this work provides fundamental insight into Ru oxide–H 2 O interactions and establishes design principles for engineering oxide surfaces optimized for electrocatalytic applications.

APXPS↗
DOE OSTI2022

Size-Dependent Photon Avalanching in Tm 3+ Doped LiYF 4 Nano, Micro, and Bulk Crystals

Photon avalanche (PA) is a highly nonlinear mode of upconversion that is characterized by 100–1000-fold increase in luminescence intensity upon minute increments of pumping power. The practical realization of numerous possible nano-bio-technology applications utilizing the PA phenomenon will require information on its susceptibility to the material volume and surface. In this report these parameters are investigated via experimental and theoretical PA. The two-color, highly nonlinear PA emission at 475 and 800 nm is clearly observed in bulk single crystal, individual microcrystals, and ensembles of colloidal core and core–shell nanoparticles of LiYF 4 host doped with either 3 or 8% of thulium ions. The properties of PA emission, such as PA nonlinearity, PA gain, PA intensity, and luminescence kinetics in these materials show dependence on crystal volume and surface quenching. Theoretical simulations provide understanding of key physical processes that influence PA performance. Moreover, photon avalanche single beam super-resolution imaging is realized for the first time in 3% Tm 3+ doped LiYF 4 core–shell nanoparticles. The obtained insights and predictions form a solid background for further development and applications of new optimized PA materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Benefits of using multiple Raman laser wavelengths for characterizing defects in a UO 2 matrix

Abstract Raman spectroscopy is one of the most useful techniques for studying the structure of UO 2 and changes due to specific defects caused by doping, changes in stoichiometry, irradiation, or heating under oxidizing conditions. In this paper, we illustrate several aspects of the application of Raman techniques to the study of UO 2 , including the use of wavelength‐dependent excitation (455, 532, and 785 nm) to assess the effects of doping (Nd, Th, and Zr), ion irradiation, and in situ heating and oxidation (UO 2 to U 3 O 8 ). Additionally, we show examples of how correlative microscopy is possible using electron backscatter diffraction combined with Raman maps of specific vibration bands or of laser‐induced luminescence generated by rare‐earth dopants in the matrix. For each of these applications, we suggest optimal excitation wavelengths that vary depending on the desired data. Blue (455 nm) excitation tends to promote oxidation even at low powers, but because Raman spectra change little with doping, irradiation‐induced changes are easier to observe. Green (532 nm) excitation is optimal for observing electron–phonon resonance effects in UO 2 and offers a good compromise for high‐temperature oxidation experiments, delivering high‐quality spectra for both UO 2 and U 3 O 8 . Infrared (785 nm) excitation is best for observing “defect” bands associated with doping in UO 2 , as changes with irradiation are small. Raman spectroscopy is particularly suited for studying the stability of UO 2 towards oxidation in the presence of dopants simulating fission products, where electron–phonon resonant effects, dopant ion luminescence, and mapping can be used together to investigate structural rearrangement as a function of temperature. These techniques can offer insight into microstructural changes in UO 2 fuels at higher burnups envisioned in future reactors.

42 ENGINEERING↗
DOE OSTI2023

Nanocomposite Materials for Accelerating Decarbonization

Here, decarbonization is demonstrated by catalytic conversion of CO 2 to fuel by means of exposure of cadmium selenide (CdSe) quantum dots-titania (TiO 2 ) nanophotocatalysts to sunlight illumination. The primary products resulted from this chemical reactions are methanol, carbon monoxide, and hydrogen after several hours of exposure to sun light. The overall CO 2 conversion efficiency of such quantum dot-titania nanostructures was compared with that of pure TiO 2 nanorod array photocatalyst. Data shows an improved conversion efficiency when composite quantum dot-titania nanostructures were used in comparison with titania nanophotocatalysts. It is postulated that this is due to the additional absorbance of visible light by the quantum dots and generation of additional charge separation at the CdSe-TiO 2 interfaces. The conversion efficiency of such an artificial photosynthesis process remains to be optimized for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identification and characterization of substrate- and product-selective nylon hydrolases

Enzymes can rapidly and selectively hydrolyze diverse natural and anthropogenic polymers, but few have been shown to hydrolyze synthetic polyamides. Here, in this work, we synthesized and characterized a panel of 95 enzymes from the N-terminal nucleophile hydrolase superfamily with 30%–50% pairwise amino acid identity. We found that nearly 40% of the enzymes had substantial nylon hydrolase activity, but there was no relationship between phylogeny and activity, nor any evidence of prior evolutionary selection for nylon hydrolysis. Several newly identified hydrolases showed substrate selectivity, generating up to 20-fold higher product titers with nylon-6,6 versus nylon-6. However, the yield was still less than 1%, necessitating further optimization before potential applications. Finally, we determined the crystal structure and oligomerization state of a nylon-6,6-selective hydrolase to elucidate structural factors that could affect activity and selectivity. These new enzymes provide insights into nylon hydrolase evolution and opportunities for analysis and engineering of improved hydrolases.

nylon↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Advances in multi-dimensional cellulose-based fluorescent carbon dot composites

Recently, fluorescent materials composed of carbon dots (CDs) have attracted increasing attention in diverse fields, including the environment, food, biology, and medicine. Cellulose has emerged as a promising class of materials for carriers due to its low cost, fascinating biodegradability, and various morphologies, especially in available different dimensions. By taking advantage of the respective superiority of fluorescent materials and cellulose carriers, the performance of fluorescence composite materials can be optimized and their application may be extended. Here, in this review, the study emphasizes the synthesis and applications of cellulose-based fluorescent materials with different dimensions. Moreover, some effective strategies and potential challenges for further development of cellulose-based fluorescent materials are discussed. A deeper understanding will provide a guide for the materials with ideal fluorescent performances.

42 ENGINEERING↗
DOE OSTI2025

Self-consistent equilibrium and transport simulations for NSTX-U plasmas enhanced via machine learning surrogate models

The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.

Equilibrium and transport modeling↗
DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

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

Radiolytic Evaluation of 3,4,3-LI(1,2-HOPO) in Aqueous Solutions

We report the octadentate hydroxypyridinone ligand 3,4,3-LI(1,2-HOPO) (abbreviated as HOPO) has been identified as a promising candidate for both chelation and f-element separation technologies, two applications that require optimal performance in radiation environments. However, the radiation robustness of HOPO is currently unknown. Here, we employ a combination of time-resolved (electron pulse) and steady-state (alpha self-radiolysis) irradiation techniques to elucidate the basic chemistry of HOPO and its f-element complexes in aqueous radiation environments. Chemical kinetics were measured for the reaction of HOPO and its Nd(III) ion complex ([Nd III (HOPO)] - ) with key aqueous radiation-induced radical transients (eaq - , H · atom, and · OH and NO 3 · radicals). The reaction of HOPO with eaq - is believed to proceed via reduction of the hydroxypyridinone moiety, while transient adduct spectra indicate that reactions with the H · atom and · OH and NO 3 · radicals proceeded by addition to HOPO's hydroxypyridinone rings, potentially allowing for the generation of an extensive suite of addition products. Complementary steady-state 241 Am(III)-HOPO complex ([ 241 Am III (HOPO)] - ) irradiations showed the gradual release of 241 Am(III) ions with increasing alpha dose up to 100 kGy, although complete ligand destruction was not observed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗