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DOE OSTI2013

Rapid Radiochemical Analyses in Support of Fukushima Nuclear Accident - 13196

There is an increasing need to develop faster analytical methods for emergency response, including emergency soil and air filter samples [1, 2]. The Savannah River National Laboratory (SRNL) performed analyses on samples received from Japan in April, 2011 as part of a U.S. Department of Energy effort to provide assistance to the government of Japan, following the nuclear event at Fukushima Daiichi, resulting from the earthquake and tsunami on March 11, 2011. Of particular concern was whether it was safe to plant rice in certain areas (prefectures) near Fukushima. The primary objectives of the sample collection, sample analysis, and data assessment teams were to evaluate personnel exposure hazards, identify the nuclear power plant radiological source term and plume deposition, and assist the government of Japan in assessing any environmental and agricultural impacts associated with the nuclear event. SRNL analyzed approximately 250 samples and reported approximately 500 analytical method determinations. Samples included soil from farmland surrounding the Fukushima reactors and air monitoring samples of national interest, including those collected at the U.S. Embassy and American military bases. Samples were analyzed for a wide range of radionuclides, including strontium-89, strontium-90, gamma-emitting radionuclides, and plutonium, uranium, americium and curium isotopes. Technical aspects of the rapid soil and air filter analyses will be described. The extent of radiostrontium contamination was a significant concern. For {sup 89,90}Sr analyses on soil samples, a rapid fusion technique using 1.5 gram soil aliquots to enable a Minimum Detectable Activity (MDA) of <1 pCi {sup 89,90}Sr /g of soil was employed. This sequential technique has been published recently by this laboratory for actinides and radiostrontium in soil and vegetation [3, 4]. It consists of a rapid sodium hydroxide fusion, pre-concentration steps using iron hydroxide and calcium fluoride precipitations, followed by Sr-Resin separation and gas flow proportional counting. To achieve a lower detection limit for analysis of some of the Japanese soil samples, a 10 gram aliquot of soil was taken, acid-leached and processed with similar preconcentration chemistry. The MDA using this approach was ∼0.03 pCi/g (1.1 mBq/g)/, which is less than the 0.05-0.10 pCi/g {sup 90}Sr levels found in soil as a result of global fallout. The chemical yields observed for the Japanese soil samples was typically 75-80% and the laboratory control sample (LCS) and matrix spike (MS) results looked very good for this work Individual QC results were well within the ± 25% acceptable range and the average of these results does not show significant bias. Additional data for a radiostrontium in soil method for 50 gram samples will also be presented, which appears to be a significant step forward based on looking at the current literature, with higher chemical yields for even larger sample aliquots and lower MDA [5, 6, 7] Hou et al surveyed a wide range of separation methods for Pu in waters and environmental solid samples [8]. While there are many actinide methods in the scientific literature, few would be considered rapid due to the tedious and time-consuming steps involved. For actinide analyses in soil, a new rapid method for the determination of actinide isotopes in soil samples using both alpha spectrometry and inductively-coupled plasma mass spectrometry was employed. The new rapid soil method utilizes an acid leaching method, iron/titanium hydroxide precipitation, a lanthanum fluoride soil matrix removal step, and a rapid column separation process with TEVA Resin. The large soil matrix is removed easily and rapidly using these two simple precipitations with high chemical recoveries and effective removal of interferences. [9, 10] Vacuum box technology and rapid flow rates were used to reduce analytical time. Challenges associated with the mineral content in the volcanic soil will be discussed. Air filter samples were reported within twenty-four (24) hours of receipt using rapid techniques published previously. [11] The rapid reporting of high quality analytical data arranged through the U.S. Department of Energy Consequence Management Home Team was critical to allow the government of Japan to readily evaluate radiological impacts from the nuclear reactor incident to both personnel and the environment. SRNL employed unique rapid methods capability for radionuclides to support Japan that can also be applied to environmental, bioassay and waste management samples. New rapid radiochemical techniques for radionuclides in soil and other environmental matrices as well as some of the unique challenges associated with this work will be presented that can be used for application to environmental monitoring, environmental remediation, decommissioning and decontamination activities. (authors)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy

The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Multivariate analysis of gamma spectra to characterize used nuclear fuel

Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.

Multivariate Analysis for Characterization of Used↗

DOE OSTI2021

Controlled Pyrolysis: A Robust Scalable Composite Recycling Technology

The reinforced composites industry is facing significant challenges in handling the scrap composite material from automobile manufacturing, the wind turbine industry, and others. The fibers in the material, whether they be carbon, glass, or other materials have commercial value if they can be recovered successfully. Successfully means the fibers are clean with no sizing or other binders and have adequate strength and physical properties that would allow them to be economically reprocessed into valuable product. The composites recycling project was an industry-collaborative effort to develop a composite recycling technology using controlled pyrolysis. Through the recycling of scrap and end-of-life (EOL) cured composite materials, this pilot study was intended to create a business case by realizing a cost-effective means for recycling EOL and production scrap composite materials, ultimately reducing the volume of composite materials destined for landfill. The project was led by the Institute for Advanced Composites Manufacturing Innovation (IACMI), the American Composites Manufacturers Association (ACMA), Oak Ridge National Laboratory (ORNL), Continental Structural Plastics (CSP) a Teijin Group Company, CHZ Technologies, and A. Schulman with support from Owens Corning, John Deere, General Electric (GE), Ashland LLC, and Plastics Europe (CEFIC). The team studied and tested CHZ Technologies’ controlled pyrolysis system, known as the Thermolyzer TM , which operates on a scalable basis to convert organic polymer materials into a clean synthesis gas and char containing the recoverable carbon and glass fiber reinforcement. The recoverable energy contained in the input polymers creates the synthesis gas that can be used to provide heat to the Thermolyzer TM primary reactor in a sustainable manner. That is, once the Thermolyzer TM is started with a small amount of external natural gas, the synthesis gas that is created from the polymers will continue to operate the burners so long as feedstock is supplied. The reinforcing fiber materials remaining in the solid phase char were separated and cleaned for re-use in other polymer systems based on the retained properties of the fibers. The study created reports (attached in the appendix) on the Mass and Energy Balances, syngas analytics, VOC assessment, yield analysis and other analytics necessary for a Techno-Economic Analysis (TEA) to quantify the economic impact of the recovery and sustainable re-use of the carbon and glass fibers. The process consisted of 4 steps: Selection of 4 samples of cured composite waste materials from project partners interested in materials recycling and recovering the reinforcing fibers for best case re-use. The materials included glass fiber (GF) polyester/vinyl ester automotive SMC from CSP, GF epoxy balsa/PVC foam wind blades from GE, carbon fiber (CF) epoxy wind blade laminated spar caps from GE, and GF/CF epoxy hybrid assembly from John Deere. Processing the waste composite samples into 1-2” shreds. Packaging the shredded composites into bulk sacks on international shipping pallets for shipment to KUG in Forst (Lausitz), Germany. Pyrolysis of the shredded composites under controlled conditions designed for each polymer system. Collecting samples of the gas and char for analysis. Shipping the char containing the CF/GF back to the US for the next steps of testing the fibers and developing protocols for sustainable re-use of the fibers in composite applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Quantitative Morphological Characterization of Carbide Inclusions in Uranium Metal

Uranium carbides (UCs) are prevalent inclusions in U metal that form during melting operations from interactions with crucible walls and the casting chamber atmosphere. Although UCs have been studied extensively since the beginning of U metal foundry operations, there are still unknowns regarding the effects of thermomechanical processing on their sizes and morphology. Here, we present the results of a series of controlled cooling experiments with molten uranium to elucidate the effect of cooling rate on inclusion morphology in a-U. Samples were melted using a vacuum induction melter and manually cooled at rates of 2.5, 1.7, 1.1, 0.8, 0.3 (± 1%) K/s from ~1600 K to <700 K in under 1 hour. Subsequent scanning electron microscopy (SEM) was performed on cross-sections of the samples, revealing a complex mixture of UC morphologies that are indicative of diffusion and growth influenced by the thermal processing of the U matrix. Image analysis using the Morphological Analysis of Materials (MAMA) software showed that UC sizes generally grew larger with slower cooling rates, and the two slowest cooling rates noticeably impacted the inclusion circularity and ellipse aspect ratio. These results indicate that UC morphology is sensitive to short cooling rates (<1 hour) and could therefore be controlled in the production of metallic nuclear fuels. Additionally, inclusion speciation and morphologies could potentially provide forensic clues about processing history of unknown metal samples. Understanding the driving forces involved in UC morphology evolution is beneficial for evaluating metal fuels for next generation nuclear reactors and for identifying signatures for nuclear forensics.

Athon, Matthew T.↗

DOE OSTI2015

HepSim: A Repository with Predictions for High-Energy Physics Experiments

A file repository for calculations of cross sections and kinematic distributions using Monte Carlo generators for high-energy collisions is discussed. The repository is used to facilitate effective preservation and archiving of data from theoretical calculations and for comparisons with experimental data. The HepSim data library is publicly accessible and includes a number of Monte Carlo event samples with Standard Model predictions for current and future experiments. The HepSim project includes a software package to automate the process of downloading and viewing online Monte Carlo event samples. Data streaming over a network for end-user analysis is discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

Analysis of tank 7 surface supernatant sample (FTF-7-15-26) in support of corrosion control program

This report provides the results of analyses on Savannah River Site Tank 7 surface supernatant liquid sample in support of the Corrosion Control Program (CCP). The measured nitrate, nitrite and free-hydroxide concentrations for the Tank 7 surface sample averaged, 3.74E-01 ± 1.88E-03, 4.17E-01 ± 9.01E-03 and 0.602 ± 0.005 M, respectively. The Tank 7 surface cesium-137, sodium and silicon concentrations were, respectively, 3.99E+08, ± 3.25E+06 dpm/mL, 2.78 M and <3.10 mg/L. The measured aluminum concentration in the Tank 7 surface sample averaged 0.11 M.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Survey of DAKOTA's V&V Capabilities in the Simulation of Residual Stresses in a Simple Composite Structure

Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses can result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, a process modeling methodology was developed and implemented into Sandia National Laboratories' SIERRA/SolidMechanics code. This method can be used to predict the process-induced stresses in any composite structure, regardless of material composition or geometric complexity. However, to develop confidence in these predictions, they must be rigorously validated. Specifically, sensitivity studies should be completed to define which model parameters are critical to the residual stress predictions. Then, the uncertainty associated with those critical parameters should be quantified and processed through the model to develop stress-state predictions encompassing the most important sources of physical variability. Numerous sensitivity analysis and uncertainty quantification methods exist, each offering specific strengths and weaknesses. Therefore, the objective of this study is to compare the performance of several accepted sensitivity analysis and uncertainty quantification methods during the manufacturing process simulation of a composite structure. The examined methods include simple sampling techniques as well as more sophisticated surrogate approaches. The computational costs are assessed for each of the examined methods, and the results of the study indicate that the surrogate approaches are the most computationally efficient validation methods and are ideal for future residual stress investigations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Analytical Action Level Calculator in Turbo FRMAC (FY2020 Close-Out) [Slides]

Objectives: Automate the labor-intensive process of generating Analytical Action Levels (AALs) in Turbo FRMAC to shorten the timeline for planning sampling campaigns and sample analysis during a response. Make the tool output results in a format that is easily imported to RadResponder as a Mixture for use in Analysis Request Forms. Deliver training to EPA on using this new tool in Turbo FRMAC (Delayed due to COVID.

42 ENGINEERING↗

DOE OSTI2024

SPRUCE Phospholipid Fatty Acid (PLFA) Abundances, August 2021-June 2022

This data set provides the results for phospholipid fatty acid analysis (PLFA) of peat samples collected from ambient plots in the SPRUCE experiment site, with experimental plot samples still being processed. The samples used to generate this data set were collected on 2021-08-23 and 2022-06-21. This data set includes abundances for groups of lipids indicating total biomass, fungi, Gram-positive bacteria, Gram-negative bacteria, actinomycetes, and anaerobic bacteria. This data set contains one file in comma separate (*.csv) format. Samples: On each sampling date, cores extending to 200 cm were collected and subsampled at 10 cm intervals from 0 and 100 cm and 25 cm intervals below 100 cm depth in Plot 7 and 21. During the lipid extraction procedure, samples were pooled into the following depth increments in order to obtain sufficient material to achieve adequate lipid yield: 0-10, 10-20, 20-30, 30-50, 50-100, and 100-150, and 150-200 cm.

actinomycetes↗

DOE OSTI2024

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Transcriptomics (PB-DP3)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Sample data was acquired using a Illumina HiSeq sequencer system and processed for RNA sequencing (RNA-Seq) expression analysis. Transcriptomic differential expression analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism. Processed RNA-Seq datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed RNA-seq results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Microfluidic electrochemical device and process for chemical imaging and electrochemical analysis at the electrode-liquid interface in-situ

A microfluidic electrochemical device and process are detailed that provide chemical imaging and electrochemical analysis under vacuum at the surface of the electrode-sample or electrode-liquid interface in-situ. The electrochemical device allows investigation of various surface layers including diffuse layers at selected depths populated with, e.g., adsorbed molecules in which chemical transformation in electrolyte solutions occurs.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Microfluidic electrochemical device and process for chemical imaging and electrochemical analysis at the electrode-liquid interface in-situ

42 ENGINEERING↗

DOE OSTI2021

Case Study: Systems Analysis of Y-12 Waste Sample Collection

The Environmental Management Organization has established a 90-Day Goal for characterizing, packaging, and dispositioning all waste after it has been declared. If adequate process knowledge is not available, a waste sample must be collected and analyzed. The objective of this case study is to reduce the cycle time for waste sample collection from 14.5 calendar days to 10 calendar days 95% of the time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

SO3/H2SO4 continuous real-time sensor demonstration at a power plant

We present results from field testing of a new, innovative sensor developed to measure SO3/H2SO4 continuously and in real time at a coal-fired power plant. The system utilizes the sensitivity, specificity, and real-time capabilities of mid-infrared (Mid-IR) laser-based sensor technology, along with a heated, close-coupled cell mounted directly to a power plant duct. Measurements were made by the laser sensor over a 2-day period and compared to results from the accepted method, EPA 8A, which requires a 30-minute collection, labor intensive processing, and off-site analysis at a lab. In contrast to the condensation method, the laser sensor continuously samples flue gas and reports measurements of the concentration of SO3/H2SO4, SO2 ever second with unattended operation. This initial demonstration proved the sensor concept and paves the way for its use for optimizing sorbent injection used to neutralize SO3/H2SO4. Optimized sorbent injection will enable significant cost savings associated with efforts to mitigate the presence and effects of SO3/H2SO4 such as “Blue Plume”, air heater fouling, and duct corrosion. In particular, the real-time, actionable information will enable better control of additive injection in flex conditions and variable fuels.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2013

Analysis Of 2h-Evaporator Scale Wall [HTF-13-82] And Pot Bottom [HTF-13-77] Samples

Savannah River Remediation (SRR) is planning to remove a buildup of sodium aluminosilicate scale from the 2H-evaporator pot by loading and soaking the pot with heated 1.5 M nitric acid solution. Sampling and analysis of the scale material has been performed so that uranium and plutonium isotopic analysis can be input into a Nuclear Criticality Safety Assessment (NCSA) for scale removal by chemical cleaning. Historically, since the operation of the Defense Waste Processing Facility (DWPF), silicon in the DWPF recycle stream combines with aluminum in the typical tank farm supernate to form sodium aluminosilicate scale mineral deposits in the 2Hevaporator pot and gravity drain line. The 2H-evaporator scale samples analyzed by Savannah River National Laboratory (SRNL) came from the bottom cone sections of the 2H-evaporator pot [Sample HTF-13-77] and the wall 2H-evaporator [sample HTF-13-82]. X-ray diffraction analysis (XRD) confirmed that both the 2H-evaporator pot scale and the wall samples consist of nitrated cancrinite (a crystalline sodium aluminosilicate solid) and clarkeite (a uranium oxy-hydroxide mineral). On “as received” basis, the bottom pot section scale sample contained an average of 2.59E+00 ± 1.40E-01 wt % total uranium with a U-235 enrichment of 6.12E-01 ± 1.48E-02 %, while the wall sample contained an average of 4.03E+00 ± 9.79E-01 wt % total uranium with a U-235 enrichment of 6.03E-01% ± 1.66E-02 wt %. The bottom pot section scale sample analyses results for Pu-238, Pu-239, and Pu-241 are 3.16E- 05 ± 5.40E-06 wt %, 3.28E-04 ± 1.45E-05 wt %, and <8.80E-07 wt %, respectively. The evaporator wall scale samples analysis values for Pu-238, Pu-239, and Pu-241 averages 3.74E-05 ± 6.01E-06 wt %, 4.38E-04 ± 5.08E-05 wt %, and <1.38E-06 wt %, respectively. The Pu-241 analyses results, as presented, are upper limit values. These results are provided so that SRR can calculate the equivalent uranium-235 concentrations for the NCSA. Results confirm that the uranium contained in the scale remains depleted with respect to natural uranium. SRNL did not calculate an equivalent U-235 enrichment, which takes into account other fissionable isotopes U-233, Pu-239 and Pu-241. The applicable method for calculation of equivalent U-235 will be determined in the NCSA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗