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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Binary Quantum Control Optimization with Uncertain Hamiltonians

Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a discretized binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with two gradient-based algorithms using sum-up-rounding approaches. Furthermore, we discuss the differentiability of the objective function and prove upper bounds of the gaps between the optimal solutions to binary control problems and their continuous relaxations. We conduct numerical simulations on various sized problem instances based on two applications of quantum pulse optimization; we evaluate different strategies to mitigate the impact of uncertainties in quantum systems. In conclusion, we demonstrate that the controls of our stochastic optimization model achieve significantly higher quality and robustness compared with the controls of a deterministic model.

conditional value-at-risk (CVaR)↗
DOE OSTI2022

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE

This report summarizes the neutron imaging techniques and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. The experimental results and technique developments reported herein support the US Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in hightemperature microreactor designs is addressed by means of high-spatial-resolution neutron imaging coupled with custom-built and application-specific furnaces. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. Neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metals hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on the first two in-situ heating measurements of YH x performed over the 2020 and 2021 LANSCE operation cycles. In parallel with in-situ heating measurements, neutron computed tomography scans were performed to visualize hydrogen aggregates in post-heated YH x samples. Analysis and results from these neutron imaging measurements are given along with outlooks and plans for improving future hydrogen diffusion measurements at LANSCE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Bentonite buffer under high temperature: laboratory experiments and coupled process modeling

Abstract. Bentonite buffer in a geological repository will be simultaneously heated from decaying radioactive waste and hydrated from the surrounding host rock, triggering complex and coupled THMC (thermal–hydrological–mechanical–chemical) processes. Understanding the THMC behavior of bentonite-based engineered barrier system (EBS) is key to the evaluation and prediction of its long-term performance. Studies on the THMC process have been focused on conditions under 100 ∘C, as most design concepts impose a thermal limit of 100 ∘C in bentonite. Recently, studies under high-temperature conditions have been conducted to evaluate the possibility of raising the thermal limit and expanding the data/knowledge base to increase the confidence level. In this abstract, we present a series of bench-scale laboratory experiments at high temperatures (up to 200 ∘C) and the corresponding modeling work. Two sets of column tests were conducted, and each set consisted of two test columns: a control column undergoing only hydration (non-heated) and an experiment column experiencing both heating and hydration (heated). During the experiment, frequent X-ray computed tomography (CT) images were collected to provide a 3D visualization of the density distribution and present the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. The two sets of tests differ with respect to several experimental conditions, such as bentonite type, compacted density and water content, water chemistry, and hydration pressure, but the important difference is that the first set used bentonite powder with a dry density of 1.28 g cm−3, whereas the second set used granulated bentonite (mixture of pellets and powder) with a dry density of 1.45–1.5 g cm−3. In both sets of experiments, a comprehensive post-dismantling characterization of bentonite samples was carried out after the column tests had been running for 1.5 years. Comparing non-heated and heated columns, the temperature gradient led to lower degree of homogenization of bentonite after bentonite became fully saturated; comparing the first and second sets, granulated and powdered bentonite exhibited drastically different hydration behavior. A THM model with a 2D axisymmetric grid system was used to interpret the data from the first set of tests. The model considers the combined impact of saturation, fluid pressure, and porosity change due to swelling/compression on the spatiotemporal distribution of bulk density and movement of the thermocouple modules. Observations from the tests help us understand the early perturbation of bentonite buffer under high temperature, and data from these tests improve the calibration of key constitutive hydrological and mechanical models and, therefore, enhance the modeling capability with respect to calculating the long-term evolution of bentonite buffer.

Zheng, Liange↗
DOE OSTI2025

Model Development and Analysis of a High-Fidelity Neutron Transport Sensor: The Quadrupole Detector Concept for Measurement of the Neutron Flux Gradient

Accurate reconstruction of the neutron flux distribution within a reactor core is essential for safe and efficient reactor operation. Traditional power shape synthesis in Light Water Reactors relies on hundreds of in-core detectors. However, this approach becomes impractical for Advanced Reactors and Microreactors due to limited space and harsh environments. To address this challenge, we propose a data-driven methodology that combines high-fidelity modeling with real-time ex-core sensor measurements, enabling the reconstruction of core power distribution while minimizing the reliance on intrusive in-core instrumentation. This project began in FY24 and achieved two initial milestones: (1) the definition of a three-year development plan for a Digital Twin framework and (2) the development of high-fidelity neutronics models of the Purdue University Reactor One (PUR-1) using both MCNP6 and OpenMC. The PUR-1 reactor, a zero-power facility, was selected due to its suitability for neutronics-focused modeling and the availability of experimental data for validation. Both models were benchmarked using neutron flux measurements obtained from irradiated gold foils, which were strategically placed within the core during a dedicated campaign in July 2024. This report marks the continuation and completion of those foundational tasks. The OpenMC model has been refined (improved geometric accuracy, expanded cross-section libraries, and refined sampling) and validated using additional experimental data. An updated sensor design—based on quadrupole configuration—was designed to measure both ex-core flux and its spatial gradient. These measurements will serve as inputs to a neural network-based reconstruction algorithm. Finally, the methodology was demonstrated on a two-dimensional test case representative of the heterogeneous material composition of the PUR-1 reactor core. A neural network implementation of the Kirchhoff-Helmholtz integral equation was employed to solve the boundary value problem using peripheral sensor measurements. The preliminary results confirm the strong potential of the proposed approach for accurate and minimally invasive neutron flux reconstruction.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Design of an additively manufactured functionally graded material of 316 stainless steel and Ti-6Al-4V with Ni-20Cr, Cr, and V intermediate compositions

This study presents a method for designing a computationally informed gradient pathway to fabricate a functionally graded material (FGM) with terminal alloys of 316 stainless steel (SS316) and Ti-6Al-4V via directed energy deposition additive manufacturing with powder feedstock. In this work, the grading is accomplished through the introduction of intermediate elements and alloys (Ni-20Cr, Cr, and V) to avoid the brittle Fe-Ti intermetallic phases that form in the direct liquid phase joining of Ti-alloys and stainless steels. Using a combination of equilibrium calculations and Scheil-Gulliver simulations, a compositional pathway was designed to avoid deleterious phases. FGM samples were fabricated and experimentally characterized to determine the viability of the pathway. A change in phases from fcc to bcc was predicted to occur within the Ni-20Cr/Cr gradient region, and this was validated through experimental characterization. No detrimental phases (intermetallic, Laves, or σ phases) formed along the gradient path, demonstrating a successful computationally-informed design and fabrication of an FGM from SS316 to Ti-6Al-4V.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Latent space dynamics identification for interface tracking with application to shock-induced pore collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

Gaussian process↗
DOE OSTI2020

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. Here, we have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

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

Simultaneous quantification of uranium( VI ), samarium, nitric acid, and temperature with combined ensemble learning, laser fluorescence, and Raman scattering for real-time monitoring

In this work, laser-induced fluorescence spectroscopy (LIFS), Raman spectroscopy, and a stacked regression ensemble was developed for near real-time quantification of uranium(VI) (1–100 μg mL –1 ), samarium (0–200 μg mL –1 ) and nitric acid (0.1–4 M) with varying temperature (20 °C–45 °C). LIFS applications range from fundamental lab-scale studies to real-time process monitoring at industrial levels, such as nuclear reprocessing applications, provided the phenomena affecting the fluorescence spectrum are accounted for (e.g., absorption, quenching, complexation). Multiple chemometric models were examined and compared to a more traditional multivariate regression approach called partial least squares (PLS). Results obtained on synthetic samples selected using D-optimal experimental design indicated that a stacked regression method, which included ridge regression, random forest, PLS, and an eXtreme gradient boost algorithm, successfully measured uranium(VI) concentrations directly in nitric acid without measuring luminescence lifetimes or standard addition. The top model resulted in percent root-mean-square error of prediction values of 5.2, 1.9, 3.0, and 2.3% for U(VI), Sm 3+ , HNO 3 , and temperature, respectively. The approach may be useful for quantifying fluorescent fission products (e.g., Sm 3+ ) to provide information on burnup of irradiated nuclear fuel. This novel framework reinforces the applicability of LIFS for real-time applications in nuclear fuel cycle applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

A machine learning approach to photometric metallicities of giant stars

ABSTRACT Despite the advances provided by large-scale photometric surveys, stellar features – such as metallicity – generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a neural network approach for estimating the metallicities and distances of red giant stars with 8-band photometry and parallaxes from Gaia EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties in the predictions arising from the range of possible outputs at each input and from the range of models compatible with the training set (through drop-out). A two-stage procedure is adopted where an initial network to estimate photoastrometric parallaxes is trained using a large sample of noisy parallax data from Gaia EDR3 and then a secondary network is trained using spectroscopic metallicities from the APOGEE and LAMOST surveys and an augmented feature space utilizing the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $\pm 0.19\, \mathrm{dex}$. The methodology is applied to stars within the Galactic bar/bulge with particular focus on a sample of 1.69 million objects with Gaia radial velocities. We demonstrate the use and validity of our approach by inspecting both spatial and kinematic gradients with metallicity in the Galactic bar/bulge recovering previous results on the vertical metallicity gradient (−0.528 ± 0.002 dex kpc−1) and the vertex deviation of the bar ($-21.29\pm 2.74\, \mathrm{deg}$).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Spatially correlated incommensurate lattice modulations in an atomically thin high-temperature Bi 2.1 Sr 1.9 CaCu 2.0 O 8+y superconductor

Strong variations in superconducting critical temperatures in different families of the cuprate perovskites, even with similar hole doping in their copper-oxygen planes, suggest the importance of lattice modulation effects. The one-dimensional incommensurate lattice modulation (ILM) of Bi 2 Sr 2 CaCu 2 O 8+y , with the average atomic positions perturbed beyond the unit cell6, offer s an ideal test ground for studying the interplay between superconductivity and the long-range incommensurate lattice fluctuations. Here we report Scanning nano X-ray Diffraction (SnXRD) imaging of incommensurate lattice modulations in Bi 2 Sr 2 CaCu 2 O 8+y Van der Waals heterostructures of thicknesses down to two-unit cells. Using SnXRD, we probe that the long-range and short-range incommensurate lattice modulations in bulk sample surface with spatial resolution below 100 nm. We find that puddle-like domains of ILM of size uniformly evolving with dimensionality. In the 2-unit cell thin sample, it is observed that the wavevectors of the long- and short-range orders become anticorrelated with emerging spatial patterns having a directional gradient. The emerging patterns, originated by tiny tuning of lattice strain, induce static mesoscopic charge density waves. Finally, our findings thus demonstrate that the strain can be used to tune and control the electromagnetic properties of two-dimensional high-temperature superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Study of Ferrocene Diffusion in Toluene/Tween 20/1-Butanol/Water Microemulsions for Redox Flow Battery Applications

Redox flow batteries (RFBs) possess multiple advantages as a flexible energy storage solution. However, RFB researchers are still facing many challenges in finding an appropriate electrolyte. Microemulsions have recently been proposed as a promising alternative RFB electrolyte because of their ability to accommodate organic redox species with fast electron transfer rates that are not soluble in aqueous phase, while still offering the high conductivity of an aqueous salt electrolyte. In this work, we focused on understanding the transport of ferrocene (Fc) in a toluene/Tween 20/1-butanol/water model microemulsion and studied the compositional influence on Fc diffusion. The results show that Fc redistributes among the oil, surfactant, and water microenvironments, and the corresponding diffusion and partition coefficients are quantified. Thus, a tortuous path diffusion model is proposed to describe the mass transport of Fc to an electrode surface. Diffusion coefficients are also obtained by pulsed-field gradient nuclear magnetic resonance (PFG NMR), while the values for Fc diffusion are substantially higher than those from electrochemical measurements, suggesting that they measure samples in different ways. The current contributions from each microenvironment indicate that the Fc permeability is much higher in the oil, even though the electron transfer reaction is likely occurring in the surfactant.

25 ENERGY STORAGE↗
DOE OSTI2026

Leaf Wax Hydrogen Isotopes Reflect Storm Track Position Over Western North America

Proxy records of past climates have yielded powerful insights into regional hydroclimate dynamics. Novel insights may be gained by reconstructing spatiotemporal changes in precipitation isotopologues in past climate states that cannot be gleaned from individual site‐level studies. In this paper, we ask whether latitudinal gradients in the stable hydrogen isotopic composition of precipitation, as inferred from sedimentary leaf wax biomarkers, reflect aspects of large‐scale climate conditions in western North America (WNA). Modern coretop samples from offshore WNA show that leaf wax hydrogen isotopes broadly track the hydrogen isotopic composition of rainfall between 20 and 40N, but with an offset corresponding to a relatively constant “apparent fractionation” value. Poleward of 40N, the leaf wax signal may be complicated by fluvial transport of leaf waxes from the continental interior. Leaf wax‐inferred precipitation hydrogen isotopes show a shift to more negative values between 30 and 40N. We use modern observational data to show that this latitudinal range marks a major transition between the subtropical arid zone and the location of the midlatitude storm tracks over the northeast Pacific. Nudged water isotope‐enabled models capture the location of the arid to mesic climate transition with fidelity. This modern data set suggests that reconstructions of the latitudinal gradient of precipitation hydrogen isotopes can constrain the sensitivity of the storm tracks to shifts in climatic boundary conditions in past climate states. This approach can yield novel insights into past, present, and future hydroclimate variability in this arid region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Controlled current-rate AC flash sintering of uranium dioxide

Uranium dioxide (UO2) pellets with controlled microstructures were densified up to 93.4% of their theoretical density in less than 25 minutes at a furnace temperature of 873 K, utilizing controlled current-rate alternating current (AC) flash sintering (FS). Using this AC-FS method it was possible to control the sintering rate and thermal gradients, resulting in dense pellets with no appreciable hourglassing and good mechanical integrity. Moreover, the apparent sintering activation energy for FS and for conventionally sintered samples was estimated to be 108 kJ mol -1 and 380 kJ mol -1 , respectively using the master sintering curve method. The apparent activation energy for FS was remarkably close to those reported in the literature for spark plasma sintering of UO 2 . Furthermore, both these field assisted sintering methods utilize fast heating rates and electrical effects that are likely enhancing the grain boundary diffusion mechanism. Finally controlled current-rate AC-FS has been demonstrated as a technological advancement, capable of producing ceramic nuclear fuels in a fraction of the conventional processing time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Copper migration and surface oxidation of CuxBi2Se3 in ambient pressure environments

Abstract Chemical modifications such as intercalation can be used to modify surface properties or to further functionalize the surface states of topological insulators (TIs). Using ambient pressure x-ray photoelectron spectroscopy, we report copper migration in C u x B i 2 S e 3 , which occurs on a timescale of hours to days after initial surface cleaving. The increase in near-surface copper proceeds along with the oxidation of the sample surface and large changes in the selenium content. These complex changes are further modeled with core-level spectroscopy simulations, which suggest a composition gradient near the surface which develops with oxygen exposure. Our results shed light on a new phenomenon that must be considered for intercalated TIs—and intercalated materials in general—that surface chemical composition can change when specimens are exposed to ambient conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of post-weld heat treatment on microstructure and mechanical properties of rotary inertia friction welded dissimilar 422-4140 martensitic steel piston joints

The study investigated how post‐weld heat treatment (PWHT) temperature affects the microstructure and localized deformation/fracture during bend testing of rotary inertia friction welds (RIFW) between AISI 422 stainless steel and AISI 4140 steel. RIFW produced a fully martensitic interface with approximately 550 HV hardness in both the thermo-mechanically affected (TMAZ) and heat‐affected zones (HAZ). Due to differences in temper resistance, the 4140 TMAZ/HAZ softened progressively under PWHT temperatures from 525 °C to 700 °C, while the 422 TMAZ unexpectedly maintained about 550 HV up to 600 °C before significantly softening at temperatures ≥625 °C. This asymmetric softening generated steep hardness gradients across the interface at temperatures ≤600 °C. Furthermore, carbon migration across the interface was minimal up to 600 °C, moderate at 625 °C, and by 700 °C produced a carbide‐rich eutectoid layer in the 422 TMAZ alongside a carbon‐depleted soft ferrite layer in the 4140 TMAZ. Strain during bending was PWHT‐dependent, concentrating on the 4140 side; in as-welded joints, the high hardness led to deformation and crack initiation in the base metals, whereas in PWHT samples, cracking initiated in the softened 4140 TMAZ near the interface. The intermediate PWHT temperature of 625 °C offered the best balance of limited carbon diffusion across the interface, relatively low peak weld hardness and minimized hardness gradients across the interface, more homogenous deformation, and good bend test performance.

4140 low-alloy steel↗
DOE OSTI2024

Integrating Experiments and Well Logs to Predict Caney Shale Static Mechanical Properties During Production with Supervised Machine Learning

Caney shale is one of the emerging oil reservoirs in Oklahoma. Understanding the impact of effective stress on its mechanical properties is critical for predicting hydraulic fracture geometry and overall hydrocarbon production. The objective of our study is to evaluate the impact of effective stress on the dynamic Young’s modulus using ultrasonic velocity measurements for Caney shale samples. A triaxial cell was utilized to measure ultrasonic (P-wave and S-wave) velocities for ten downhole Caney shale samples under various effective stresses to indirectly assess the impact of pore pressure change. The dynamic Young’s moduli estimated from these measurements were integrated with available conventional well logs (excluding sonic logs) and triaxial test results from Benge et al. (2021) to predict the static Young’s modulus using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that the estimated dynamic Young’s moduli from ultrasonic measurements were higher than the corresponding static Young’s modulus of cores from the same vertical well at similar depths. With increasing effective stress, the dynamic Young’s modulus increased for all samples. The estimated dynamic-to-static correction factor tended to be higher in zones of high neutron porosity (PHIN) and low density compared to other zones. Finally, SHapley Additive exPlanations (SHAP) for RF and XGBoost models identified depth, gamma ray (GR), and PHIN as key features for predicting the static Young’s modulus. This study enhances our understanding of the dynamic and static Young’s moduli for the Caney shale interval, as a function of effective stress and conventional well logs. The findings from this study can improve predictions of production throughout the well's lifespan by offering insights into the mechanical property degradation resulting from pore pressure depletion.

Kholy, Sherif M.↗
DOE OSTI2024

High energy electron diffraction instrument with tunable camera length

Ultrafast electron diffraction (UED) stands as a powerful technique for real-time observation of structural dynamics at the atomic level. In recent years, the use of MeV electrons from radio frequency guns has been widely adopted to take advantage of the relativistic suppression of the space charge effects that otherwise limit the temporal resolution of the technique. Nevertheless, there is not a clear choice for the optimal energy for a UED instrument. Scaling to beam energies higher than a few MeV does pose significant technical challenges, mainly related to the inherent increase in diffraction camera length associated with the smaller Bragg angles. In this study, we report a solution by using a compact post-sample magnetic optical system to magnify the diffraction pattern from a crystal Au sample illuminated by an 8.2 MeV electron beam. Our method employs, as one of the lenses of the optical system, a triplet of compact, high field gradients (>500 T/m), small-gap (3.5 mm) Halbach permanent magnet quadrupoles. Shifting the relative position of the quadrupoles, we demonstrate tuning the magnification by more than a factor of two, a 6× improvement in camera length, and reciprocal space resolution better than 0.1 Å –1 in agreement with beam transport simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Mercury Speciation via Diffusive Gradients Thin-Films Technology

Objectives: Develop and test various diffusive gradient in thin films (DGT) samplers for mercury - SRNL is developing reactive DGTS (rDGTs). Test chemistry to differentiate methylmercury from total and/or inorganic mercury in environmental samples. Fabricate and test rDGT samplers for deployment. Deploy the rDGTs in variable settings. Mercury is a persistent-bioaccumulative-toxic environmental pollutant. Mercury exists in different species such as elemental, inorganic, and methyl. Mercury speciation determine mercury behavior and toxicity. For example, methylmercury strongly accumulates in biota such as fish resulting in potential human health impacts. DGTs are innovative samplers to measure water concentration by diffusion and capture. Types of Hg DGTs used: Total Hg, Inorganic Hg, and Methyl Hg. Deployment Sites: Upper Three Runs, Lower Three Runs, Steele Creek, East Fork Poplar Creek (SRNL Sites), Horizons Creek, NOA Creek, Bruners Creek (ORNL Sites). DGT concentration estimates closely matched alternative measurements for soluble mercury at each Oak Ridge site - DGTs did not respond to particulate mercury. DGTs provide a representative measure for biota uptake since they are left out longer which allows for an average exposure concentration. DMA-80 provided an efficient and quick analysis. Variability in site location made a difference in Hg species. DGT preparations and sampling wasn't as quick and simple as microcolumn technology. Copper reagent degraded agarose gel - more work and cleaner data are needed to assess speciation in rDGTs. Future Work: Use cross-linked polyacrylamide for the collection layer for a stronger plastic. Make a copper diffusion layer without agarose. Test copper separation in lab using realistic stream conditions such as high organic carbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗