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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 OSTI2020

Replacing photomultiplier tubes with silicon photomultipliers for nuclear safeguards applications

Photomultiplier tubes (PMTs) have been used for decades as the default light detection technology for scintillator-based radiation monitors. PMTs come with a handful of disadvantages, however, including large volume usage, fragility, high-voltage requirements, and susceptibility to magnetic fields. Arrays of silicon photomultipliers (SiPMs) are a possible alternative to PMTs providing similar performance while offering improvements in the areas listed. We focus on straightforward “drop-in replacement” evaluations by imposing a single channel output of the SiPM signal and a backend electronics data acquisition and analysis routine identical to that used for PMTs. In the realm of nuclear safeguards, the primary performance metric for gamma spectroscopy is detector resolution, and for neutron detection it is pulse shape discrimination (PSD) to separate neutron and gamma signals. In this work we present the results of replacing a PMT with a 2” x 2” SiPM array mounted, in turn, to the same 2” x 2” cylindrical sodium iodide (NaI) crystal. The ultimate comparison for this gamma spectrometry is confirmation of uranium enrichment standards, ranging from depleted uranium to 93% highly enriched uranium (HEU), by analyzing the resulting spectra with both a simple integral scaling in key energy regions of the spectrum, as well as with the NaIGEM software package. In addition to the gamma spectroscopy work, we will present the latest results on comparing the PSD capabilities of a 4” x 4” SiPM array to a 5” PMT. As with the gamma spectroscopy evaluations, the light detectors are mounted in turn to the same liter-scale organic scintillators. This neutron-focused work includes evaluation of prototype SiPM readout boards with a single output signal summed over 256 individual SiPM pixels, and evident tradeoffs between PSD capability and fast response.

42 ENGINEERING↗
DOE OSTI2020

Replacing photomultiplier tubes with silicon photomultipliers for nuclear safeguards applications

Photomultiplier tubes (PMTs) have been used for decades as the default light detection technology for scintillator-based radiation monitors. PMTs come with a handful of disadvantages, however, including large volume usage, fragility, high-voltage requirements, and susceptibility to magnetic fields. Arrays of silicon photomultipliers (SiPMs) are a possible alternative to PMTs providing similar performance while offering improvements in the areas listed. We focus on straightforward “drop-in replacement” evaluations by imposing a single channel output of the SiPM signal and a backend electronics data acquisition and analysis routine identical to that used for PMTs. In the realm of nuclear safeguards, the primary performance metric for gamma spectroscopy is detector resolution, and for neutron detection it is pulse shape discrimination (PSD) to separate neutron and gamma signals. In this work we present the results of replacing a PMT with a 2” x 2” SiPM array mounted, in turn, to the same 2” x 2” cylindrical sodium iodide (NaI) crystal. The ultimate comparison for this gamma spectrometry is confirmation of uranium enrichment standards, ranging from depleted uranium to 93% highly enriched uranium (HEU), by analyzing the resulting spectra with both a simple integral scaling in key energy regions of the spectrum, as well as with the NaIGEM software package. In addition to the gamma spectroscopy work, we will present the latest results on comparing the PSD capabilities of a 4” x 4” SiPM array to a 5” PMT. As with the gamma spectroscopy evaluations, the light detectors are mounted in turn to the same liter-scale organic scintillators. This neutron-focused work includes evaluation of prototype SiPM readout boards with a single output signal summed over 256 individual SiPM pixels, and evident tradeoffs between PSD capability and fast response.

Engineering - Instrumentation related to nuclear s↗
DOE OSTI2023

Diversifying and perennializing plants in agroecosystems alters retention of new C and N from crop residues

Abstract Managing soils to retain new plant inputs is key to moving toward a sustainable and regenerative agriculture. Management practices, like diversifying and perennializing agroecosystems, may affect the decomposer organisms that regulate how new residue is converted to persistent soil organic matter. Here we tested whether 12 years of diversifying/perennializing plants in agroecosystems through extended rotations or grassland restoration would decrease losses of new plant residue inputs and, thus, increase retention of carbon (C) and nitrogen (N) in soil. We tracked dual‐labeled ( 13 C and 15 N), isotopically enriched wheat ( Triticum aestivum ) residue in situ for 2 years as it decomposed in three agroecosystems: maize–soybean (CS) rotation, maize–soybean–wheat plus red clover and cereal rye cover crops (CSW2), and spring fallow management with regeneration of natural grassland species (seven to 10 species; SF). We measured losses of wheat residue (C wheat and N wheat ) in leached soil solution and greenhouse gas fluxes, as well as how much was recovered in microbial biomass and bulk soil at 5‐cm increments down to 20 cm. CSW2 and SF both had unique, significant effects on residue decomposition and retention dynamics that were clear only when using nuanced metrics that able to tease apart subtle differences. For example, SF retained a greater portion of C wheat in 0–5 cm surface soils (155%, p = 0.035) and narrowed the C wheat to N wheat ratio ( p < 0.030) compared to CS. CSW2 increased an index of carbon‐retention efficiency, C wheat retained in the mesocosm divided by total measured, from 0.18 to 0.27 (49%, p = 0.001), compared to CS. Overall, we found that diversifying and extending the duration of living plants in agroecosystems can lead to greater retention of new residue inputs in subtle ways that require further investigation to fully understand.

McDaniel, Marshall D.↗
DOE OSTI2020

Accurate and complete genomes from metagenomes

Genomes are an integral component of the biological information about an organism; thus, the more complete the genome, the more informative it is. Historically, bacterial and archaeal genomes were reconstructed from pure (monoclonal) cultures, and the first reported sequences were manually curated to completion. However, the bottleneck imposed by the requirement for isolates precluded genomic insights for the vast majority of microbial life. Shotgun sequencing of microbial communities, referred to initially as community genomics and subsequently as genome-resolved metagenomics, can circumvent this limitation by obtaining metagenome-assembled genomes (MAGs); but gaps, local assembly errors, chimeras, and contamination by fragments from other genomes limit the value of these genomes. Here, we discuss genome curation to improve and, in some cases, achieve complete (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been generated, although notably some are from very complex systems such as soil and sediment. Through analysis of about 7000 published complete bacterial isolate genomes, we verify the value of cumulative GC skew in combination with other metrics to establish bacterial genome sequence accuracy. The analysis of cumulative GC skew identified potential misassemblies in some reference genomes of isolated bacteria and the repeat sequences that likely gave rise to them. We discuss methods that could be implemented in bioinformatic approaches for curation to ensure that metabolic and evolutionary analyses can be based on very high-quality genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data Release Report for the Source Physics Experiment Phase II: Dry Alluvium Geology Experiments (DAG-1 through DAG-4), Nevada National Security Site

The Dry Alluvium Geology (DAG) project was Phase II of the Source Physics Experiment and consisted of a series of four chemical explosive tests conducted in the same source hole on the Nevada National Security Site. This hole is located at 37.1146°N and -116.0693°W, with a surface elevation of 1,285.2 meters (m) (4,216.5 feet [ft]) above sea level. The first test (DAG-1) was conducted on July 20, 2018, at 16:51:52.67838 Coordinated Universal Time (UTC). The explosive source for DAG-1 was nitromethane initiated by a small plastic-bonded explosive (PBX) charge, detonated at the depth of 385.0 m (1,263.2 ft) below ground surface. DAG-1 had a trinitrotoluene (TNT) equivalent yield of 0.908 metric tons (2,002 pounds [lbs]). DAG-2 was conducted on December 19, 2018, at 18:45:56.92115 UTC. This test was the largest in the series, with a TNT equivalent yield of 50.997 metric tons (112,429 lbs). The explosive source for DAG-2 was nitromethane initiated by a small PBX charge, detonated at the depth of 299.8 m (983.6 ft) below ground surface. DAG-3 was conducted on April 27, 2019, at 15:49:01.84183 UTC. The explosive source for this test was nitromethane initiated by a small PBX charge, detonated at the depth of 149.9 m (492.0 ft) below ground surface. DAG-3 had a TNT equivalent yield of 0.908 metric tons (2,002 lbs). The final DAG test (DAG-4) was conducted on June 22, 2019, at 21:06:19.87632 UTC. The explosive source for DAG-4 was nitromethane initiated by a small PBX charge, detonated at the depth of 51.6 m (169.3 ft) below ground surface. DAG-4 had a TNT equivalent yield of 10.357 metric tons (22,833 lbs). The four tests were recorded by an extensive set of instrumentation that included sensors both at near-field (less than 200 m) and far-field (200 m or greater) distances. The near-field instruments consisted of three-component (3C) accelerometers installed at various depths ranging from 51.6 to 385 m (169.3 to 1,263.1 ft) below ground surface in boreholes positioned around the source hole, and arrays of single-component and 3C accelerometers on the surface. The far-field network comprised a variety of seismic and acoustic sensors, including short-period geophones, broadband seismometers, and 3C accelerometers at distances of 200 m to 400 kilometers. In addition, the DAG-2, DAG-3, and DAG-4 explosions were recorded by a temporary array of 496 geophones arranged in a densely spaced grid pattern known as “Large N.” This report coincides with the release of these data for analysts and organizations that are not participants in this program. This report describes the four DAG tests and the various types of near-field, far-field, and other data that are available. Assembled data sets are accessible through: Incorporated Research Institutions for Seismology, Data Management Center 1408 NE 45th Street, Suite 201, Seattle, Washington 98105 USA. www.iris.washington.edu

58 GEOSCIENCES↗
DOE OSTI2022

The Critical Role of Process Analysis in Chemical Recycling and Upcycling of Waste Plastics

There is an urgent need for new technologies to enable circularity for synthetic polymers, spurred by the accumulation of waste plastics in landfills and the environment and the contributions of plastics manufacturing to climate change. Chemical recycling is a promising means to convert waste plastics into molecular intermediates that can be remanufactured into new products. Given the growing interest in the development of new chemical recycling approaches, it is critical to evaluate the economics, energy use, greenhouse gas emissions, and other life cycle inventory metrics for emerging processes, relative to the incumbent, linear manufacturing practices employed today. Here we offer specific definitions for classes of chemical recycling and upcycling and describe general process concepts for the chemical recycling of mixed plastics waste. We present a framework for techno-economic analysis and life cycle assessment for both closed- and open-loop chemical recycling. Rigorous application of these process analysis tools will be required to enable impactful solutions for the plastics waste problem.

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

Risk-Averse Bi-Level Stochastic Network Interdiction Model for Cyber-Security Risk Management

Security of cyber networks is crucial; recent severe cyber-attacks have had a devastating effect on many large organizations. The attack graph, which maps the potential attack paths of a cyber network, is a popular tool for analyzing cyber system vulnerability. In this study, we propose a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse, resource constrained cyber network defender to optimally deploy security countermeasures that protect against attackers with an uncertain budget. This risk- averse conditional-value-at-risk (CVaR) model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop a customized constraint and column generation algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Numerical experiments demonstrate that the acceleration techniques enable the solution of relatively large problems within a reasonable amount of time: applying all the acceleration techniques also reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, computational results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

Bhuiyan, Tanveer H.↗
DOE OSTI2022

Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions

Abstract. In this study, we develop a watershed zonation approach for characterizing watershed organization and functions in a tractable manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale heterogeneity of key bedrock-through-canopy properties and for quantifying the co-variability of these properties representing coupled ecohydrological and biogeochemical interactions, (2) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having the unique distributions of these properties relative to neighboring parcels, and (3) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought and solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesize airborne remote sensing data (lidar, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that (1) we can define the scale of hillslopes at which the hillslope-averaged metrics can capture the majority of the overall variability in key properties (such as elevation, net potential annual radiation, and peak snow-water equivalent – SWE), (2) elevation and aspect are independent controls on plant and snow signatures, (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are significantly correlated with surface topography and plan species distribution, and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, we show that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be applicable to other sites and generally useful for guiding the selection of hillslope-experiment locations and informing model parameterization.

58 GEOSCIENCES↗
DOE OSTI2020

Techno-Economic Analysis: Best Practices and Assessment Tools

A team at Sandia National Laboratories (SNL) recognized the growing need to maintain and organize the internal community of Techno - Economic Assessment analysts at the lab . To meet this need, an internal core team identified a working group of experienced, new, and future analysts to: 1) document TEA best practices; 2) identify existing resources at Sandia and elsewhere; and 3) identify gaps in our existing capabilities . Sandia has a long history of using techno - economic analyses to evaluate various technologies , including consideration of system resilience . Expanding our TEA capabilities will provide a rigorous basis for evaluating science, engineering and technology - oriented projects, allowing Sandia programs to quantify the impact of targeted research and development (R&D), and improving Sandia's competitiveness for external funding options . Developing this working group reaffirms the successful use of TEA and related techniques when evaluating the impact of R&D investments, proposed work, and internal approaches to leverage deep technical and robust, business - oriented insights . The main findings of this effort demonstrated the high - impact TEA has on future cost, adoption for applications and impact metric forecasting insights via key past exemplar applied techniques in a broad technology application space . Recommendations from this effort include maintaining and growing the best practices approaches when applying TEA, appreciating the tools (and their limits) from other national laboratories and the academic community, and finally a recognition that more proposals and R&D investment decision s locally at Sandia , and more broadly in the research community from funding agencies , require TEA approaches to justify and support well thought - out project planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

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

High-resolution imaging of Hg/Se aggregates in the brain of small Indian mongoose, a wild terrestrial species: insights into intracellular Hg detoxification

Human activities result in the emission of 2000 metric tons of mercury compounds annually. Mercury (Hg) biomagnification has been characterized in marine mammals and predatory fish; however, little is known about mercury accumulation in brains of wild terrestrial species. Elevated Hg content, of 1.27 μg/g wet wt.—found in the brain of wild small Indian mongoose, prompted us to use synchrotron X-ray fluorescence imaging for simultaneous, quantitative mapping of biologically relevant and neurotoxic elements with high spatial resolution. X-ray fluorescence combined with immunohistochemistry revealed ~0.5–1.9 micron Hg-rich aggregates in cells of the choroid plexus and astrocytes of the subventricular wall in the mongoose brain. Hg content within aggregates correlated with selenium. Hg aggregates did not co-localize with lysosomes. The low Hg density inside aggregates indicated diffuse Hg binding to a Se-containing biomolecule, rather than much denser HgSe nanoparticles proposed to form in other species. Our data show the susceptibility of the small Indian mongoose population to Hg pollution and highlight the vulnerability of the brain as an organ targeted by mercury. Data also provide evidence on the adaptation in the form of a Se-based detoxification mechanism sequestering Hg into intracellular aggregates.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Low-temperature oxidation pathways are critical to thermal incineration of PFAS-laden materials

With growing desire to destroy per- and poly-fluoroalkyl substances (PFAS) now known to be detrimental to human health, a sound understanding of fluorocarbon combustion chemistry is important to efficient thermal destruction within incinerators. While most fluorocarbon combustion models and the sets of reactions contained within them were originally developed for the high temperatures encountered in flame suppression applications, they have often been used to assess PFAS destruction in incinerators, which emphasize a lower range of temperatures. We present results that demonstrate that low-temperature fluorocarbon oxidation pathways—not yet known to play a role in fluorocarbon combustion—impact key incinerator performance metrics, including: PFAS surrogate mole fractions, products of incomplete destruction, and waste destruction efficiencies. The results further point to the utility of NO as a potential additive. The present results show the influence of these pathways for CF 3 O 2 , for which some data are available, but analogous pathways would also occur for other fluoroalkylperoxy radicals, for which little is known. Furthermore, the results demonstrate the need for future work to identify and characterize low-temperature pathways more generally, consider such pathways in kinetic model development, and experimentally probe intermediate temperature conditions to better understand, design, and control thermal destruction technologies for improved PFAS management.

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

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A single-component water-lean post-combustion CO 2 capture solvent with exceptionally low operational heat and total costs of capture – comprehensive experimental and theoretical evaluation

A comprehensive evaluation of a recently developed water-lean amine-based solvent, namely N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA), has been performed to analyze its post-combustion CO 2 capture performance. This evaluation comprises (1) fundamental characterization of the solvent–CO 2 interaction using vapor–liquid equilibria, kinetics and viscosity measurements; (2) process characterization of the CO 2 capture performance as measured in a laboratory scale continuous flow system and via Aspen Plus® simulation using a flue gas simulant; as well as (3) a full techno economic analysis of the capture process at industrial scale with corresponding projections of critical metrics. This paper summarizes the many parts of this comprehensive evaluation and shows how the various parts come together to empower validated conclusions about its process performance. Notably, it is projected that this solvent can operate at a regeneration heat rate of 2.0 GJ per tonne CO 2 for post-combustion capture, and at a total cost of capture of $50.6/tonne CO 2 . Overall, with further process optimization significant reductions in the capture cost are predicted.

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

Improving Conversion of Captured CO 2 on Mixed-Oxide Dual-Function Materials by Low-Concentration Metal Promotion

Reactive carbon capture (RCC) is a promising nascent approach to harness the untapped resource of waste CO 2 to produce fuels and platform chemicals. However, process economics relies on the design of dual-function materials (DFMs) that exhibit maximal conversion of captured CO 2 and high selectivity toward valuable carbon products (e.g., CO). Herein, we report the use of low-concentration (ca. 0.5 wt %) metal promotors (Pt and Au) to improve the conversion of captured CO 2 on a Zn-Al mixed-oxide DFM by >30%, while also more than doubling per-cycle CO yield due to enhanced retention of captured CO 2 and H 2 activation. In situ DRIFTS of bound CO 2 on the DFMs showed distinctions in surface carbonate formation for the Pt- and Au-modified materials that were correlated with product evolution during the reactive desorption step of RCC. Particularly, Pt afforded superior H 2 activation but formed irreversibly-bound bicarbonates, compromising overall productivity, while the Na/Au/ZA DFM predominantly adsorbed CO 2 on Na sites, resulting in polydentate carbonates that are retained until higher temperatures before conversion to CO. In conclusion, these results indicate that adding a low concentration of oxidation-resistant noble metals, especially Au, is an effective strategy for improving key performance metrics of Zn-Al mixed-oxide DFMs to further advance the development of scalable RCC technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Thermodynamics–Structure–Dynamics Correlations and Nonuniversal Effects in the Elastically Collective Activated Hopping Theory of Glass-Forming Liquids

We employ the microscopic Elastically Collective Nonlinear Langevin Equation (ECNLE) theory of activated dynamics in combination with crystal-avoiding simulations to study four inter-related questions for metastable monodisperse hard sphere fluids. The first is how significantly improved integral equation theory structural input (Modified-Verlet (MV) closure) changes the dynamical predictions of ECNLE theory. The main consequence is a modest enhancement of the importance of the collective elastic barrier relative to its local cage contribution, which increases the alpha relaxation time and fragility relative to prior results based on the Percus–Yevick closure. Second, ECNLE-MV theory predictions for the alpha time and self-diffusion constant in the metastable regime are quantitatively compared to our new simulations. The small adjustment of a numerical prefactor that enters the collective elastic barrier leads to quantitative agreement over three decades. Third, using the more accurate MV structural input, ECNLE theory is shown to predict thermodynamics–structure–dynamics “correlations” based on various long and short wavelength scalar properties all related to static two-point collective density fluctuations. The logarithm of the alpha relaxation time scales as a power law with these scalar metrics with an exponent that is significantly lower in the less dense noncooperative activated regime compared to the very dense highly cooperative regime. However, the discovered correlation of activated relaxation with a thermodynamic property (dimensionless compressibility) is not causal in ECNLE theory, but rather reflects a strong connection between the local structural quantities that quantify kinetic constraints in the theory with the amplitude of long wavelength density fluctuations. Fourth, the consequences of chemically specific nonuniversalities associated with the onset condition and relative importance of collective elasticity are studied. In conclusion, the predicted thermodynamics–structure–dynamics correlations are found to be robust, albeit with nontrivial shifts of the onset condition.

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

Elemental Analysis of Bio-Oils by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Laboratory Analytical Procedure (LAP), Issue Date: May 13, 2022

Concentrations of inorganic elements is a key quality metric for bio-oils as certain elements impact upgrading processes and product quality. Unless reduced or removed during production and processing, alkali and alkaline metals native to lignocellulosic biomass can carry over into bio-oils contributing to ash content and degraded catalyst performance during upgrading to hydrocarbon fuels or chemical products. Non-metallic elements such as sulfur and phosphorus can also negatively impact upgrading catalysts and product quality. Inductively coupled plasma optical emission spectroscopy (ICP-OES) can be used to measure inorganic elements of interest in bio-oils. This procedure covers the preparation and analysis of fast pyrolysis (FP) and catalytic fast pyrolysis (CFP) bio-oils. The concentrations of these elements can indicate potential bio-oil quality from the perspective of deoxygenation processes. The implications of elemental composition will depend on process parameters such as upgrading catalyst sensitivities. This Laboratory Analytical Procedure (LAP) covers two methods for quantification of inorganic elements by ICP-OES: Procedure A uses microwave assisted digestion with concentrated nitric acid, and Procedure B is an organic ICP-OES method utilizing a diglyme solvent.

09 BIOMASS FUELS↗
DOE OSTI2020

SDU7 Interior Liner Testing & Evaluation

The Saltstone Disposal Unit 7 (SDU7) project requested the Savannah River National Laboratory (SRNL), Materials Science & Engineering (MS&E) organization to evaluate an alternative bromobutyl liner and adhesives for potential use in SDU7 based on applicable ASTM testing standards. SRNL performed similar testing for the liner system used in SDU6. Bonded and non-bonded samples of the alternative liner (Blair Rubber Marflex™ RCHB60HT) were subjected to specific ASTM tests after immersion in two Saltstone leachate simulants, designated S1 and S2, for 1000 hours at 60 °C. Immersion exposures were performed at the Savannah River Ecology Laboratory (SREL). Post-immersion testing involved mechanical property and hardness testing of base material, lap-shear testing of bonded samples and rubber-to-concrete paver interrogation. The liner exhibited an approximate 40% drop in tensile strength and 25% drop in elongation at failure from baseline values after immersion, though final values are comparable to previous SDU6 liner values and are within liner manufacturer property ranges. Overall lap-shear strength behavior of the four adhesives showed a similar pattern, with short-term reduction and subsequent increase in peak load values. However, ranking of adhesives varied with the metric used for comparison. Two of the four adhesives tested (Normac 900 and REMA SC 4000) showed overall better behavior, collectively considering immersion performance, lap-shear data and bonded paver interrogation. Lap-shear samples bonded with the REMA adhesive showed the best combination of final retained bond strength, 6-week average bond strength and bond failure mode. Only one paver (REMA SC4000) was noted to have no defects after immersion. This document details the testing performed and provides conclusions and recommendations. The testing suggests that SDU liner performance is highly dependent upon seam integrity, which collectively depends on a combination of adhesive properties, installation workmanship and inspection/quality control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗