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Organomercury Measurements from Sludge Batch 10 Simulant Studies

Researchers at the Savannah River National Laboratory have recently performed testing to evaluate the Sludge Batch 10 flowsheet using simulated sludge waste. In the course of this testing several samples were taken to determine the concentration of organomercury species resulting from sludge batch processing. These samples were submitted to the Savannah River National Laboratory Sensing and Metrology department quantitation using a mercury analyzer. The signal amplifier used to perform organomercury quantitation in the mercury analyzer experienced a malfunction in the course of analysis, leading to uncertainty of organomercury concentrations observed. Seventy-three organomercury samples from six project submissions to the Savannah River National Laboratory Sensing and Metrology department have been critically reviewed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Influence of Surface Treatment on the Interfacial and Mechanical Performance of Metal-Insert Overmolded Composites

Structural composites with metal inserts are gaining increasing interest enabling light weight design for energy efficiency, provide insulation, corrosion protection without compromising mechanical integrity. Such over molded metal-polymer composites show strong potential in many automotive and aerospace component parts, e.g. car bumper, chassis, door, brackets, fasteners, etc. Despite several advantages, metal-polymer dissimilar interface remains a weak spot in structural design, requiring further investigation to improve interfacial interlocking. The current work focuses on enhancing the metal-polymer adhesion between stainless steel inserts and polymer matrices (e.g. acrylonitrile butadiene styrene resin (ABS), and polyamide 66 (PA66) through surface treatment via chemical etching and mechanical roughening. In the chemical etching process, metal substrates were treated with aqua regia solution for different time intervals to evaluate surface roughness measured by water contact angle and morphology analysis. For mechanical roughening approach, metal inserts were subjected to blasting for surface abrasion. Treated samples were also functionalized with aminosilane coupling agent which showed successful grafting on the surface measured by water contact angle. Metal inserts with micro level surface roughness through etching exhibited significant 21% improvement in adhesion strength compared to untreated inserts; however, the effect of silane treatment on the interfacial strength was found to be highly matrix dependent.

Saha, Subhabrata [ORNL] (ORCID:000000033966064X)↗

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Plasmonic nanorod probes’ journey inside plant cells for in vivo SERS sensing and multimodal imaging

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.

Cupil-Garcia, Vanessa↗

Developing and Implementing the Data Mining Algorithms in RAVEN

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.

97 MATHEMATICS AND COMPUTING↗

Quantifying and Qualifying Alloys Based on Level of Homogenization: A U–10 wt% Mo Alloy Case Study

Homogenization heat treatment is performed to attain uniformity in microstructure of metals and metal alloys, which is helpful to achieve the desired workability and microstructure in final products, and eventually to gain predictable and consistent performance. Fabrication of fuel plates made from a low-enriched uranium alloy with 10 wt% Mo (U-10Mo) involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the fuel performance in the nuclear reactor. To make sure these materials are uniformly homogenized, a statistical method was proposed to quantify and characterize the Mo concentration variation in U-10Mo fuel plates by analyzing the Mo concentration measurement data from scanning electron microscopy–energy dispersive spectroscopy line scans. Statistical tolerance intervals were employed to determine the qualification of U-10Mo fuel plates. We formulate an argument for the minimum number of independent samples required to define fuel plate qualification if no Mo measurement data are available in advance, and demonstrate that the given tolerance interval requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication cost. The statistical strategy developed in this report can be implemented for other applications, especially in the field of material manufacturing to assess qualification requirements and monitor and improve process design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

The prevalence of precipitation from polar supercooled clouds

Abstract. Supercooled clouds substantially impact polar surface energy budgets, but large-scale models often underestimate their occurrence, which motivates accurately establishing metrics of basic processes. An analysis of long-term measurements at Utqiaġvik, Alaska, and McMurdo Station, Antarctica, combines lidar-validated use of soundings to identify supercooled cloud layers and colocated ground-based profiling radar measurements to quantify cloud base precipitation. We find that more than 85 % (75 %) of sampled supercooled layers are precipitating over the Arctic (Antarctic) site, with more than 75 % (50 %) precipitating continuously to the surface. Such high frequencies can be reconciled with substantially lesser spaceborne estimates by considering differences in radar hydrometeor detection sensitivity. While ice precipitation into supercooled clouds from aloft is common, we also find that the great majority of supercooled cloud layers without ice falling into them are themselves continuously generating precipitation. Such sustained primary ice formation is consistent with continuous activation of immersion-mode ice-nucleating particles (INPs), suggesting that supercooled cloud formation is a principal gateway to ice formation at temperatures greater than ∼-38 ∘C over polar regions. The prevalence of weak precipitation fluxes is also consistent with supercooled cloud longevity and with well-observed and widely simulated case studies. An analysis of colocated microwave radiometer retrievals suggests that weak precipitation fluxes can be nonetheless consequential to moisture budgets for supercooled clouds owing to small liquid water paths. The results here also demonstrate that the observed abundance of mixed-phase clouds can vary substantially with instrument sensitivity and methodology. Finally, we suggest that these ground-based precipitation rate statistics offer valuable guidance for improving the representation of polar cloud processes in large-scale models.

54 ENVIRONMENTAL SCIENCES↗

Utilizing Commercial Submersible Mixer Pumps for Sludge Removal in Savannah River Site's Tank 26 - 20289

The Savannah River Site (SRS) Liquid Waste System (LWS) safely manages, stores, treats, and dispositions liquid radioactive waste. The LWS consists of 51 underground waste storage tanks (eight of which are operationally closed and filled with grout), waste evaporators, treatment facilities, and solidification facilities, known as the Defense Waste Processing Facility (DWPF) and Saltstone Production Facility (SPF). One of the waste storage tanks, Tank 26, was placed into service in 1980 as an F Tank Farm (FTF) Evaporator Feed Tank. From 1980 to 2013, Tank 26 received F Canyon receipts and dilute supernate that was then transferred to the 242-16F FTF Evaporator for volume reduction. In 2013, the steam tube bundle failed in the FTF Evaporator, and the decision was made not to repair/resume evaporator operation. During operation of Tank 26, solids built up to a level of 2.03 m in the tank with a volume of 1062.9 kL. Due to the evaporator failure and subsequent shutdown, the solids in Tank 26 were selected to feed Sludge Batch 10, which is collected and prepped prior to being sent to DWPF for final disposition. To accomplish solids removal, Commercial Submersible Mixer Pumps (CSMPs) were selected to slurry the solids in preparation to be sent to Tank 51. Four CSMPs were installed in Tank 26 with each one installed in a separate quadrant of Tank 26. This work would mark the first deployment of CSMPs in an SRS waste tank. The CSMPs were developed in response to operational issues from previous mixing pumps and budgetary constraints. The CSMPs use the concept of modifying commercially available equipment for nuclear waste applications. The CSMPs consist of a 230-horsepower submersible mixing pump (manufactured by GPM, Inc.) joined to a mast fabricated at SRS. For this application, Savannah River Remediation (SRR) design services was challenged to provide a mast design that required no steel superstructure support system on top of Tank 26. Also, SRR design services provided a simplified Tank 26 riser interface that did not require extensive machine parts to adjust height of the suction screen of the CSMP within the tank. Other design improvements include no requirements for tank top radiation monitors and no requirements for flushing of the CSMPs during startup/shutdown operations. The CSMPs were operated at an initial height of 2.29 m above the Tank 26 bottom for 10 days. After sludge sounding, the CSMPs were lowered to a height of 1.52 m with no issues due to proper work planning and the simplified riser interface. With the CSMPs lowered to a level of 1.52 meters above the tank bottom, the CSMPs were operated for 12 days and another sounding was performed. The sounding level came back matching the disturbance depth results of the first pump run. Chemistry samples were analyzed while the tank was left to settle. The analysis revealed enough weight percent solids to provide good feed to Sludge Batch 10. Also, the results revealed the supernate within the mixture would provide good salt solution feed to Salt Waste Processing Facility (SWPF) Salt Batch 3. So, the plan was modified to let the disturbed solids settle and decant the salt solution for addition to SWPF Salt Batch 3. Then water will be added back to Tank 26, and the CSMPs run in order to wash the solids to decrease settling time prior to being sent to Tank 51 for Sludge Batch 10. Utilizing CSMPs for waste removal in an SRS Tank has provided a cost-effective means for further waste removal efforts. In addition, the CSMPs are easier to operate by utilizing a robust and simplified design. The CSMPs performed quite well with no process shutdown or delays during operation. As a result, CSMPs are integral to the future of removing radioactive waste from storage tanks at SRS. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗

A selection function toolbox for subsets of astronomical catalogues

ABSTRACT Large catalogues are ubiquitous throughout astronomy, but most scientific analyses are carried out on smaller samples selected from these catalogues by chosen cuts on catalogued quantities. The selection function of that scientific sample – the probability that a star in the catalogue will satisfy these cuts and so make it into the sample – is thus unique to each scientific analysis. We have created a general framework that can flexibly estimate the selection function of a sample drawn from a catalogue in terms of position, magnitude and colour. Our method is unique in using the binomial likelihood and accounting for correlations in the selection function across position, magnitude, and colour using Gaussian processes and spherical harmonics. We have created a new open-source python package selectionfunctiontoolbox that implements this framework and used it to make three different estimates of the APOGEE DR16 red giant sample selection function, as a subset of 2MASS, with each estimate using an increasing amount of technical knowledge of the APOGEE targeting. In a companion paper, we applied our methodology to derive estimates of the astrometric and spectroscopic selection functions of Gaia EDR3. Our framework will make it trivial for astrophysicists to estimate the selection function that they should be using with the custom sample of stars that they have chosen to answer their scientific question.

79 ASTRONOMY AND ASTROPHYSICS↗

April 2025 Semiannual Composite Salt Waste Processing Facility (SWPF) Decontaminated Salt Solution (DSS) Toxicity Characteristic Leaching Procedure (TCLP) Results

The aqueous waste from the Salt Waste Processing Facility is sampled semiannually for transfers to the Saltstone Production Facility. Salt solution is treated at the Saltstone Production Facility and disposed of in the Saltstone Disposal Facility. Per request of customer, X-TTR-Z-00027, Revision 0, one Saltstone Disposal Facility waste form was prepared at Savannah River National Laboratory using the Salt Waste Processing Facility Decontaminated Salt Solution and Z-area premix material for the April 2025 semiannual Toxicity Characteristic Leaching Procedure sample. The sample contained 60:40 (by weight) of slag and fly ash (referred to as the “Cement-Free grout sample”). Results from the technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request prepared by Savannah River Mission Completion. At 43 days cured, the Saltstone sample was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure. The April 2025 semiannual Cement-Free grout sample met the South Carolina Code of Regulations for Hazardous Waste Management Regulations 61-79.261.24 and 61-79.268.48 requirements for a non-hazardous waste form with respect to the Resource Conservation and Recovery Act metals and Underlying Hazardous Constituents, and met the Saltstone Production Facility Waste Acceptance Criteria that was in effect at the time of the sample collection at the Salt Waste Processing Facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Methodology to Generate the Decay Gamma Source for a Second Target Station Target Wedge

This report details the specifics of applying the Position-Averaged Method methodology for the calculation of the rotating lasagna-style target wedge activation and the resulting decay gamma source terms. The methodology uses MCNP® Code Version 6.2.0 with the RNUCS patch coupled with CINDER2008 from the AARE V1.0 package. The process begins with model preparations that include ensuring impurities are included in the materials in the regions of interest along with segmentation of the geometry in order to capture the decay gamma source gradients. This report also details the physics models and other MCNP®, CINDER2008, and ADVANTG 3.2.0 related options used to calculate the decay gamma source terms. ADVANTG 3.2.0 is used to generate a weight window set to aid in convergence of the neutron fluxes in the initial MCNP® protons-on-target particle transport calculation to calculate the neutron fluxes and spallation products in the regions of interest. The neutron fluxes and spallation products are then used in the CINDER2008 transmutation calculation, and the decay gamma sources are generated by the AARE_GAMMA_SOURCE_SCRIPT as a part of the AARE V1.0 package. These decay gamma sources include the energy distributions as well as the necessary distributions to sample the location of the source using the cell rejection method. The gamma sources that are produced using the methodology may be used for a wide variety of analysis involving the target wedge. This report includes the material specifications, physics options, proton source term description, geometrical configuration, and post-processing details.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Quantifying and Qualifying Alloys Based on Level of Homogenization: A U-10Mo Alloy Case Study

Homogenization heat treatment is performed to attain uniformity in microstructure which is helpful to achieve the desired workability and microstructure in final products and, eventually, to gain predictive and consistent performance. Fabrication of low-enriched uranium alloys with 10 wt% molybdenum (U-10Mo) fuel plates involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the performance in the nuclear reactor. Here, to ensure uniform homogenization, a statistical method is proposed to quantify and characterize the molybdenum concentration variation in U-10Mo fuel plates by analyzing the molybdenum concentration measurement data from scanning electron microscopy energy dispersive spectroscopy line-scan. Statistical tolerance intervals (TI) are employed to determine the qualification of the U-10Mo fuel plate. We formulate an argument for the minimum number of independent samples to define fuel plate qualification if no molybdenum measurement data are available in advance and demonstrate that the given TI requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication costs. The statistical strategy developed in this paper can be implemented for other applications especially in the field of material manufacturing to assess qualification requirements and monitor and improve the process design.

36 MATERIALS SCIENCE↗

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Gupta, Ayush↗

Dynamic compressive and tensile strengths of spark plasma sintered alumina

Fully dense submicron grain size alumina samples were manufactured from alumina nano-powder using Spark Plasma Sintering and tested in two kinds of VISAR-instrumented planar impact tests. In the first kind, samples were loaded by 1-mm tungsten impactors, accelerated to a velocity of about 1 km/s. These tests were aimed at studying the Hugoniot elastic limit (HEL) of Spark Plasma Sintering (SPS)-processed alumina and the decay, with propagation distance, of the elastic precursor wave. In the tests of the second kind, alumina samples of 3-mm thickness were loaded by 1-mm copper impactors accelerated to 100–1000 m/s. These tests were aimed at studying the dynamic tensile (spall) strength of the alumina specimens. The tensile fracture of the un-alloyed alumina shows a monotonic decline of the spall strength with the amplitude of the loading stress pulse. Analysis of the decay of the elastic precursor wave allowed determining the rate of the irreversible (inelastic) strains in the SPS-processed alumina at the initial stages of the shock-induced inelastic deformation and to clarify the mechanisms responsible for the deformation. The 1-% addition of Cr{sub 2}O{sub 3} decreases the HEL of the SPS-processed alumina by 5-% and its spall strength by 50% but barely affects its static properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

MP1 Intermediate Characterization Report Summary

Great effort worldwide has been invested in replacing high enriched uranium (HEU) fuel with low enriched uranium (LEU) fuel in nuclear plants. This effort aims in safeguarding nuclear material and promoting the proliferation resistance of current reactors. The United States Department of Energy (DOE), National Nuclear Security Administration's (NNSA) Materials Management and Minimization Reactor Conversion program is actively working to convert civilian research and test reactors from the use of HEU to LEU fuel. To date, DOE NNSA has converted or verified the shutdown of various domestic reactors and also reactors worldwide. The task to develop and test a new LEU fuel while maintaining current performances has been assigned to the High-Performance Research Reactor (USHPRR) program. U alloyed with Mo achieves the goal of high uranium density necessary for the conversion of the high performance research reactors, and has also demonstrated good performance in pile [1]. The alloying process of U with Mo stabilizes the body-centered cubic (bcc) gamma phase (?-U) at room temperature. Without this alloying property enhancement, this phase is only stable at temperatures only above 770 °C. Retaining this isotropic gamma phase within the U-Mo alloy provides swelling and oxidation resistance and desired mechanical properties [2]. Generally 10% w. has been considered sufficient to achieve stabilization. However, local phenomena within the alloy can affect the local Mo content and lead to gamma phase decomposition. Because of this, it has been suggested that controlling gamma phase decomposition is most critical during processing [2-3]. The work detailed in this document is part of the HPPR program and focuses on the characterization of fresh fuel (a.k.a. as fabricated, before reactor testing) in collaboration with other laboratories including PNNL (Pacific Northwest National Laboratory), LANL (Los Alamos National Laboratory) and BWXT (Babcock and Wilcox Technologies). This testing is part of a 4 way validation for which similar samples from master plates were taken and distributed to each laboratory to independently analyze utilizing a standardized set of measurement and analysis procedures. When complete this work will supply a “toolbox” of information for alloying and fabrication parameters with its direct effects on microstructure quantified. Various parameters have been analyzed in this characterization effort (including grain size, quality of the coating layer, composition of the samples, carbide content etc.), based on the direction of the working group in ref.[4]. The final aim of this work is to increase the understanding of the impact of processing conditions on the final fuel microstructure, using fabrication processes that meet “commercial viability” requirements [4]. The strength of this campaign rests on the extent of the samples studied and the variation of the manufacture parameters which will permit to achieve the required understanding. Thus permitting to use these experiments to down-select test samples used to evaluate irradiation performance [4] to be tested in various locations of the Idaho National Laboratory (INL) Advanced Test Reactor (ATR).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Role of atomistic structure in the stochastic nature of conductivity in substoichiometric tantalum pentoxide

First-principles calculations of electrical conductivity (σ{sub o}) are revisited to determine the atomistic origin of its stochasticity in a distribution generated from sampling 14 ab-initio molecular dynamics configurations from 10 independently quenched models (n = 140) of substoichiometric amorphous Ta{sub 2}O{sub 5}, where each structure contains a neutral O monovacancy (V{sub O}{sup 0}). Structural analysis revealed a distinct minimum Ta-Ta separation (dimer/trimer) corresponding to each V{sub O}{sup 0} location. Bader charge decomposition using a commonality analysis approach based on the σ{sub o} distribution extremes revealed nanostructural signatures indicating that both the magnitude and distribution of cationic charge on the Ta subnetwork have a profound influence on σ{sub o}. Furthermore, visualization of local defect structures and their electron densities reinforces these conclusions and suggests σ{sub o} in the amorphous oxide is best suppressed by a highly charged, compact Ta cation shell that effectively screens and minimizes localized V{sub O}{sup 0} interaction with the a-Ta{sub 2}O{sub 5} network; conversely, delocalization of V{sub O}{sup 0} corresponds to metallic character and high σ{sub o}. The random network of a-Ta{sub 2}O{sub 5} provides countless variations of an ionic configuration scaffold in which small perturbations affect the electronic charge distribution and result in a fixed-stoichiometry distribution of σ{sub o}; consequently, precisely controlled and highly repeatable oxide fabrication processes are likely paramount for advancement of resistive memory technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Direct conversion of wet algae to crude biodiesel under supercritical ethanol conditions

This paper presents a single-step, environmentally friendly approach for the direct conversion of wet algae to crude biodiesel under supercritical ethanol conditions. Ethanol was used for the simultaneous extraction and transesterification of lipids in algae to produce fatty acid ethyl esters at supercritical conditions. In this work the effects of process parameters dry algae to ethanol (wt./vol.) ratio (1:6-1:15), reaction temperature (245-270 C), and reaction time (2-30 min.) on the yield of fatty acid ethyl esters (FAEE) were studied. 67% conversion was achieved at 265 C and 20 min of reaction time. The calorific value of a purified biodiesel sample produced at optimum conditions was measured to be 43 MJ/kg, which is higher than that of fatty acid methyl esters produced from the same biomass. The purified fatty acid ethyl esters were analyzed using GC-MS and FTIR. TGA analysis of algal biomass and purified FAEE was presented along with TEM images of the biomass captured before and after supercritical ethanol transesterification. This green conversion process has the potential to provide an energy-efficient and economical route for the production of renewable biodiesel production.

09 BIOMASS FUELS↗