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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Real-Time Screening for Uranium Enrichment by Paper Spray Ionization Mass Spectrometry for Field Applications

A rapid isotope ratio screening technique for uranium enrichment is demonstrated by utilizing paper spray ionization (PSI) high-resolution mass spectrometry (HRMS). Measurements were conducted using an ambient ionization mass spectrometer coupled to a custom-made JEOL PSI attachment apparatus, with de minimus sample preparation requirements. The current method detection limit for individual isotopes (e.g., 235 U and 238 U) is approximately 50 pg, with subsequent optimization expected to further improve U isotopic sensitivity. The PSI analytical method described herein can support rapid analysis (both in-field and in-lab screening) of isotopes-of-interest, as demonstrated by empirical differentiation of depleted uranium (DU) and low enriched uranium (LEU) analytical aliquots. Furthermore, this analytical workflow holds promise for applications in nuclear forensics, international nuclear safeguards, and nonproliferation missions.

Anions↗
DOE OSTI2021

Grafted nickel-promoter catalysts for dry reforming of methane identified through high-throughput experimentation

High-throughput synthesis of a series of monometallic and bimetallic catalysts (45 bimetallic and 50 monometallic samples) consisting of nickel and one of nine different metal promoters (B, Co, Cu, Fe, Mg, Mn, Sn, V and Zn) supported on one of six different metal oxides alumina, ceria, magnesia, silica and titania) is carried out via organometallic grafting using a robotic platform. The catalysts are evaluated for their activity and selectivity for the dry reforming of methane at a feed ratio of CH 4 :CO 2 of 1 at 650–800 °C in a parallel flow reactor system. The type of oxide support prevails over the type of additive for both catalyst activity and stability. On Al 2 O 3 and MgO, Fe was found to be the best promoter; on SiO 2 , Cu is the best promoter at 700 °C and higher, while on TiO 2 , Mn is found to enhance the conversion at 800 °C. On CeO 2 , all additives except Fe have beneficial effects. Twenty-five catalysts show > 90% methane conversion with ten catalysts showing > 95% conversion at 800 °C with the H 2 :CO ratios ranging from 0.8 to 1.2. Amongst the ten highest performers, NiFe/Al 2 O 3 and NiFe/MgO are more active than Ni/Al 2 O 3 and Ni/MgO, respectively and were stable over a period of 25 h at 800 °C. Characterization on the as-prepared samples reveals highly dispersed phase, while after reduction in H 2 , highly dispersed and reduced nickel particles up to 10 nm are formed. The particles do not increase in size under dry reforming reaction conditions at 800 °C. An increased hydrogen consumption observed during H 2 -TPR of the nickel particles is positively correlated with methane conversion for Al 2 O 3 -based catalysts. The resistance to deactivation by coking and variation in coke structure are investigated by spectroscopic and microscopic methods to identify the relationship between metal promoters, alloy formation, and type of surface carbon deposits. Carbon whiskers were observed on the ten selected spent samples and are preferentially deposited on Ni rather than on the promoters. Carbon nanotube formation and metal particle removal from support were not observed to cause deactivation while amorphous carbon formation was clearly linked to catalyst deactivation, as amorphous carbon could encapsulate nickel, either on the support or at the end of the carbon nanotube. Furthermore, the organometallic grafting technique is an efficient and suitable technique for synthesizing highly dispersed and homogeneous phases which lead to high conversion and high durability for dry reforming of methane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Critical review of functionalized silica sorbent strategies for selective extraction of rare earth elements from acid mine drainage

We report the ubiquitous and growing global reliance on rare earth elements (REEs) for modern technology and the need for reliable domestic sources underscore the rising trend in REE-related research. Adsorption-based methods for REE recovery from liquid waste sources are well-positioned to compete with those of solvent extraction, both because of their expected lower negative environmental impact and simpler process operations. Functionalized silica represents a rising category of low cost and stable sorbents for heavy metal and REE recovery. These materials have collectively achieved high capacity and/or high selective removal of REEs from ideal solutions and synthetic or real coal wastewater and other leachate source. These sorbents are competitive with conventional materials, such as ion exchange resins, activated carbon; and novel polymeric materials like ion-imprinted particles and metal organic frameworks (MOFs). This critical review first presents a data mining analysis for rare earth element recovery publications indexed in Web of science, highlighting changes in REE recovery research foci and confirming the sharply growing interest in functionalized silica sorbents. A detailed examination of sorbent formulation and operation strategies to selectively separate heavy (HREE), middle (MREE), and light (LREE) REEs from the aqueous sources is presented. Selectivity values for sorbents were largely calculated from available figure data and gauged the success of the associated strategies, primarily: (1) silane-grafted ligands, (2) impregnated ligands, and (3) bottom-up ligand/silica hybrids. These were often accompanied by successful co-strategies, especially bite angle control, site saturation, and selective REE elution. Recognizing the need to remove competing fouling metals to achieve purified REE “baskets,” we highlight techniques for eliminating these species from acid mine drainage (AMD) and suggest a novel adsorption-based process for purified REE extraction that could be adapted to different water systems.

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

Atomic-Level Structure of Mesoporous Hexagonal Boron Nitride Determined by High-Resolution Solid-State Multinuclear Magnetic Resonance Spectroscopy and Density Functional Theory Calculations

Mesoporous hexagonal boron nitride (p-BN) has received significant attention over the last decade as a promising candidate for water cleaning/pollutant removal and hydrogen storage applications. In this work, high-resolution solid-state NMR spectroscopy and plane-wave density-functional theory (DFT) calculations are used to obtain an atomic-level description of p-BN. 1 H– 15 N or 1 H– 14 N heteronuclear (HETCOR) correlation experiments recorded with either conventional NMR at room temperature or dynamic nuclear polarization surface-enhanced spectroscopy (DNP-SENS) at ca. 100 K reveal NB 2 H, NBH 2 , NBH 3 + species residing on the edges of BN sheets. Ultra-high field 35.2 T 11 B NMR spectroscopy was used to resolve 11 B NMR signals from BN 3 , BN 2 O x (OH) 1–x (x = 0–1), BNO x (OH) 2–x (x = 0–2), BO x (OH) 3–x (x = 0–3), and BO x (OH) 4–x – (x = 0–4). Importantly, 2D 11 B dipolar double-quantum–single-quantum homonuclear correlation spectra reveal that many pore/defect sites are composed of boron oxide/hydroxide clusters connected to the BN framework through BN 2 O units. 1D and 2D 11 B{ 15 N} HETCOR NMR experiments, in addition to plane-wave DFT calculations of nine different structural models, further confirm the assignment of all NMR signals. The detailed structure determination of the pore and edge/defect sites within p-BN should further enable the rational design and development of next-generation p-BN-based materials. In addition, the techniques outlined here should be applicable to determine structure within other porous and/or boron-based materials.

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

Engineered Nanoporous Frameworks for Adsorption Cooling Applications

The energy demand for traditional vapor-compressed technology for space cooling continues to soar year after year due to global warming and the increasing human population’s need to improve living and working conditions. Thus, there is a growing demand for eco-friendly technologies that use sustainable or waste energy resources. Here this review discusses the properties of various refrigerants used for adsorption cooling applications followed by a brief discussion on the thermodynamic cycle. Next, sorbents traditionally used for cooling are reviewed to emphasize the need for advanced capture materials with superior properties to improve refrigerant sorption. The remainder of the review focus on studies using engineered nanoporous frameworks (ENFs) with various refrigerants for adsorption cooling applications. The effects of the various factors that play a role in ENF–refrigerant pair selection, including pore structure/dimension/shape, morphology, open-metal sites, pore chemistry and possible presence of defects, are reviewed. Next, in-depth insights into the sorbent–refrigerant interaction, and pore filling mechanism gained through a combination of characterization techniques and computational modeling are discussed. Finally, we outline the challenges and opportunities related to using ENFs for adsorption cooling applications and provide our views on the future of this technology.

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

Crystalline Si Surface Passivation with Nafion for Bulk Defects Detection with Electron Paramagnetic Resonance

Here, in monocrystalline Si (c-Si) solar cells, identification and mitigation of bulk defects are crucial to achieving a high photoconversion efficiency. To spectroscopically detect defects in the c-Si bulk, it is desirable to passivate the surface defects. Passivation of the c-Si surface with dielectrics such as Al 2 O 3 and SiNx requires deposition at elevated temperatures, which can influence defects in the bulk. Herein, we report on the passivation of different Czochralski (Cz) Si wafer surfaces by an organic copolymer, Nafion. We test the efficacy of the surface passivation at temperatures ranging from 6 to 473 K to detect bulk defects using electron paramagnetic resonance (EPR) spectroscopy. By comparing with state-of-the-art passivation layers, including Al 2 O 3 and liquid HF/HCl, we found that at room temperature, Nafion can provide comparable passivation of n-type Cz Si with an implied open-circuit voltage (iV oc ) of 713 mV and a recombination current prefactor J o of 5 fA/cm 2 . For p-type Cz Si, we obtained an iV oc of 682 mV with a J o of 22.4 fA/cm 2 . Scanning electron microscopy and photoluminescence reveal that Nafion can also be used to passivate the surface of c-Si solar cell fragments scribed from a solar cell module by using a laser. Consistent with previous studies, analysis of the EPR spectroscopy data confirms that the H-terminated surface is necessary, and fixed negative charge in Nafion is responsible for the field-effect passivation. While the surface passivation quality was maintained for almost 24 h, which is sufficient for spectroscopic measurements, the passivation degraded over longer durations, which can be attributed to surface SiO x growth. These results show that Nafion is a promising room-temperature surface passivation technique to study bulk defects in c-Si.

14 SOLAR ENERGY↗
DOE OSTI2024

Quest for Multifunctionality: Current Progress in the Characterization of Heterometallic Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are a class of porous, crystalline materials that have been systematically developed for a broad range of applications. Incorporation of two or more metals into a single crystalline phase to generate heterometallic MOFs has been shown to lead to synergistic effects, in which the whole is oftentimes greater than the sum of its parts. Because geometric proximity is typically required for metals to function cooperatively, deciphering and controlling metal distributions in heterometallic MOFs is crucial to establish structure–function relationships. However, determination of short- and long-range metal distributions is nontrivial and requires the use of specialized characterization techniques. Advancements in the characterization of metal distributions and interactions at these length scales is key to rapid advancement and rational design of functional heterometallic MOFs. Further, this perspective summarizes the state-of-the-art in the characterization of heterometallic MOFs, with a focus on techniques that allow metal distributions to be better understood. Using complementary analyses, in conjunction with computational methods, is critical as this field moves toward increasingly complex, multifunctional systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Synchrotron X-ray metrology of dopant distribution and oxidation state in high pressure CVD grown TM 2+ :ZnSe optical fibers

High pressure chemical vapor deposition (HPCVD) has shown to be a promising method for producing Cr 2+ :ZnSe and Fe 2+ :ZnSe mid-IR optical fiber lasers. The oxidation state of the dopants and their spatial homogeneity are critical for the characterization of effective fiber laser performance; however, this is challenging for small fiber cores and small doping concentrations. We demonstrate using synchrotron micro X-ray fluorescence (XRF) mapping to study the distribution of the dopants throughout the fiber cross-sections. Furthermore, we study the local chemical environment of these unique fiber structures using micro X-ray near edge absorption spectroscopy (XANES). Our study reveals that transition metal doped ZnSe deposited using HPCVD has nanoscale dopant aggregation although the material is chemically identical to the commercially produced diffusion doped laser crystals, as indicated by the presence +2 oxidation state of the dopants. This work demonstrates the power of synchrotron-based X-ray techniques for use as metrology tools for improving the performance of laser materials in fiber geometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Evaluation of dried blood spot sampling for verification of exposure to chemical threat agents

Abstract Purpose Exposure to chemical threat agents (CTAs), including nerve agents, the vesicating agent sulfur mustard, and opioids, remains a significant threat to warfighter and civilian populations. Definitive analytical methods to verify exposure to CTAs require shipping refrigerated or frozen biomedical samples to reference laboratories for analysis. Logistical and financial burdens arise as the transport of biomedical samples is subject to strict restrictions and complex packaging, which, if done incorrectly, can lead to sample deterioration. The use of dried blood spot (DBS) sampling could provide operational improvements for collecting, storing, and shipping important forensic samples. Therefore, this effort focuses on developing DBS techniques with Mitra® 30-µL volumetric absorptive microsampling (VAMS®) devices for use in CTA exposure verification. Methods VAMS® devices were loaded and dried with human whole blood that was exposed to the metabolites pinacolyl methylphosphonic acid (PMPA), ethyl methylphosphonic acid (EMPA), 1,1’sulfonylbis[2-(methylsulfinyl)ethane] (SBMSE), norfentanyl, norcarfentanil, norsufentanil, and norlofentanil. Following extraction from the VAMS® devices, metabolites were detected using liquid chromatography-tandem mass spectrometry (LC–MS/MS). The methods were validated for performance by assessing sensitivity, precision, accuracy, and recovery. Results These methods were sensitive to 1 ng/mL for SBMSE, 0.5 ng/mL for PMPA, EMPA, and norfentanyl; 0.1 ng/mL for norlofentanil, and 0.05 ng/mL for norsufentanil and norcarfentanil. All methods met acceptable precision and accuracy criteria with favorable recovery. Conclusions These results demonstrated the utility of VAMS® in stabilizing human whole blood and show promise as an improved collection method for verification of exposure to various CTAs.

Toxicology↗
DOE OSTI2023

Real-time insight into the multistage mechanism of nanoparticle exsolution from a perovskite host surface

In exsolution, nanoparticles form by emerging from oxide hosts by application of redox driving forces, leading to transformative advances in stability, activity, and efficiency over deposition techniques, and resulting in a wide range of new opportunities for catalytic, energy and net-zero-related technologies. However, the mechanism of exsolved nanoparticle nucleation and perovskite structural evolution, has, to date, remained unclear. Herein, we shed light on this elusive process by following in real time Ir nanoparticle emergence from a SrTiO 3 host oxide lattice, using in situ high-resolution electron microscopy in combination with computational simulations and machine learning analytics. We show that nucleation occurs via atom clustering, in tandem with host evolution, revealing the participation of surface defects and host lattice restructuring in trapping Ir atoms to initiate nanoparticle formation and growth. These insights provide a theoretical platform and practical recommendations to further the development of highly functional and broadly applicable exsolvable materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Effect of Plutonium on Uranium Oxide Microstructural Fingerprint

The analysis of particulates from environmental sampling is routinely performed for nuclear forensics applications. In order to increase the tools available for nuclear forensics applications, capability development materials (CDMs), or reference particulates, are necessary for developing and benchmarking new analytical methods. Historically, these CDM particulates have been produced with highly controlled and characterized isotopic and size parameters for applications in developing and benchmarking particle sizers and mass spectrometry analytical methods [1- 4]. However, the development of CDMs with highly characterized particle morphology and phase of the particles is vital for aiding in the development and benchmarking of particle analytical methods for those parameters. In many cases, key properties such as crystallographic phase, morphology, and microstructure, may be correlated to processing history of environmental sampling particulates [5]. To that end, this work investigates determining the structural fingerprint of produced Pu-doped uranium oxide CDMs using transmission electron microscopy (TEM). The particulates, one of which shown in Figure 1, were synthesized to Fig. 1. Single particulate of uranium oxide fabricated using the THESEUS technique. develop capabilities for environmental sampling investigations. These particles are monodisperse at diameter of 1 μm and a range of plutonium concentrations from 0- 1,000 ppm. Given their small diameter, TEM analysis was ideal for studying the particulate structural fingerprint in detail. Previous investigations confirmed the external homogeneity of the particulates at different plutonium concentrations, however this analysis focuses on determining the grain structure, phase distribution, and porosity of the particulates.

Mayer, Jack [Univ. of Florida, Gainesville, FL (Un↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2020

Facile and scalable dry surface doping technique to enhance the electrochemical performance of LiNi 0.64 Mn 0.2 Co 0.16 O 2 cathode materials

Lithium nickel manganese cobalt oxide (NMC) is one of the dominant cathode materials in lithium-ion batteries. In this study a simple, efficient and scalable surface doping technique is successfully demonstrated, which can be readily used in mass production of cathode materials. For the first time neodymium oxide (Nd 2 O 3 ) has been employed as the surface doping agent. The Nd-doped NMC shows greatly improved cycling and rate performance, and the enhanced cycling stability has been demonstrated in full pouch cells, with a 17.5% increase in capacity retention after 300 cycles. Fewer cracks have been observed in the doped NMC after cycling, and in situ X-ray diffraction reveals the suppressed lattice collapse by Nd doping. Greatly suppressed surface phase change has been confirmed by HR-TEM and EELS. The result suggests great promise in using this dry doping technique to enhance the electrochemical performance of NMC cathodes.

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

Single Gaussian process method for arbitrary tokamak regimes with a statistical analysis

Abstract Gaussian process regression is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student’s t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Matérn kernel for the prior probability, and Student’s t-distribution for the likelihood is shown to give the best results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Determination of nuclear PDFs using Markov chain Monte Carlo methods

Global QCD analyses of nuclear parton distribution functions (nPDFs) have traditionally relied on the Hessian method for uncertainty estimation. However, the inherent Gaussian approximation and reliance on local curvature often prove insufficient for nPDF fits, which are frequently characterized by limited data constraints and non-Gaussian likelihoods. In this paper, we present the first nPDF determination based on Markov Chain Monte Carlo (MCMC) techniques, implemented within the nCTEQ framework using an adaptive Metropolis-Hastings algorithm. The MCMC approach enables a direct mapping of the posterior distribution and reveals a highly nontrivial parameter-space structure, including multiple modes and pronounced non-Gaussian behavior, particularly for the valence PDFs. We perform the first single-nucleus global analysis of lead PDFs using exclusively lead data and compare it to a multi-nuclei fit employing a standard analytic A dependence. The inclusion of lighter nuclei reduces quark uncertainties and modifies the shape of the lead PDFs, while leaving the gluon distribution largely unaffected. A complementary Hessian analysis exposes systematic limitations of the Gaussian approximation. Our results demonstrate that MCMC methods provide a more reliable framework for uncertainty quantification in nPDF determinations.

Derakhshanian, N. [Institute of Nuclear Physics Po↗
DOE OSTI2022

Harmonic Generation and Inverse Cascade in the z-Pinch Driven, Preseeded Multimode, Magneto-Rayleigh-Taylor Instability

We report the magneto-Rayleigh-Taylor instability (MRTI) plays an essential role in astrophysical systems and in magneto-inertial fusion, where it is known to be an important degradation mechanism of confinement and target performance. In this Letter, we show for the first time experimental evidence of mode mixing and the onset of an inverse-cascade process resulting from the nonlinear coupling of two discrete preseeded axial modes (400- and 550 - μm wavelengths) on an Al liner that is magnetically imploded using the 20-MA, 100-ns rise-time Z Machine at Sandia National Laboratories. Four radiographs captured the temporal evolution of the MRTI. We introduce a novel unfold technique to analyze the experimental radiographs and compare the results to simulations and to a weakly nonlinear model. We find good quantitative agreement with simulations using the radiation magnetohydrodynamics code hydra. Spectral analysis of the MRTI time evolution obtained from the simulations shows evidence of harmonic generation, mode coupling, and the onset of an inverse-cascade process. The experiments provide a benchmark for future work on the MRTI and motivate the development of new analytical theories to better understand this instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Third-integer Resonant Extraction Regulation System for Mu2e

A third-integer resonant slow extraction system is being developed for Fermilab's Delivery Ring to deliver protons to the upcoming Mu2e experiment. The timescale of the extraction (or spill) duration is 43 milliseconds, which is extremely short and unprecedented. Additionally, the experiment's strict and challenging requirements on the quality of the spill at this time scale has led to the development of a new Spill Regulation System (SRS) design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content suppressor. Contributions to the first two components of the SRS, i.e., Slow Regulation and Fast Regulation subsystems, will be presented in which new adaptive learning algorithm schemes for the slow regulation of the spill -- validated using particle tracking simulations -- shall be described. In addition to these novel methods for the enhancement of the spill regulation system, results of employing Machine Learning in enhancing the performance of the resonant extraction are also presented. At the forefront of applying ML techniques to solve non-linear accelerator control problems, this work includes optimizing the PID gains as well as the replacement of the traditional PID controller using Recurrent Neural Networks and Gated Recurrent Unit (GRU) ML models to achieve efficiencies greater than a PID controller. Cutting-edge on-going Reinforcement Learning efforts, including an actor-critic family of learning algorithms, to regulate the spill rate will be reviewed, as well as present analytical calculations pertaining the transit time of particles in a third-integer resonant extraction. Detailed numerical investigations and validations of such calculations, the model of which could be exported and reliably used in future analytical modeling of any resonant extraction, are discussed.

43 PARTICLE ACCELERATORS↗