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DOE OSTI2023

Optimizing Geant4 Hadronic Models

Geant4, the leading detector simulation toolkit used in high energy physics, employs a set of physics models to simulate interactions of particles with matter across a wide range of energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and inclusive characteristics, and use physically motivated parameters. However, they generally aim to cover a broad range of possible simulation tasks and may not always be optimized for a particular process or a given material. The Geant4 collaboration recently made many parameters of the models accessible via a configuration interface. This opens a possibility to fit simulated distributions to the thin target experimental datasets and extract optimal values of the model parameters and the associated uncertainties. Such efforts are currently undertaken by the Geant4 collaboration with the goal of offering alternative sets of model parameters, also known as "tunes", for certain applications. The effort should subsequently lead to more accurate estimates of the systematic errors in physics measurements given the detector simulation role in performing the physics measurements. Results of the study are presented to illustrate how Geant4 model parameters can be optimized through applying fitting techniques, to improve the agreement between the Geant4 and the experimental data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Recovery of Scandium from Bauxite Residue Using Sulfation Roasting-Assisted Water Leaching Process

Here, this study presents a method for recovering scandium from Indonesian bauxite residue (red mud) through a sulfation roasting-assisted water leaching process. Ammonium sulfate, (NH 4 ) 2 SO 4 , was chosen as the sulfate source for its lower melting point, less corrosivity, decreased toxicity, and the potential for ammonia recovery. A modified mixing system was introduced to improve the distribution of (NH 4 ) 2 SO 4 on red mud particles by combining them in water. A series of experiments were conducted to optimize the sulfation roasting process by examining the effects of the (NH 4 ) 2 SO 4 /red mud ratio, temperature, and roasting time. Subsequently, water leaching was optimized while considering the effect of leaching time and liquid-to-solid (L/S) ratio. The leaching of scandium from the roasted red mud showed rapid kinetics, even when using only water. Under the roasting conditions of 1/1 (g/g) (NH 4 ) 2 SO 4 /red mud ratio, 700°C, 60 min roasting time, and the leaching conditions of 65°C, 60 min, and 10 mL/g (L/S), over 90% of scandium was recovered, along with 64.4% Al leaching and below 8% Fe leaching. These results were further corroborated through comprehensive analyses such as x-ray diffraction (XRD) and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). This study offers insights into an alternative, more environmentally friendly method for Sc recovery from red mud, promoting the sustainable use of hazardous waste like red mud.

ammonium sulfate↗

DOE OSTI2021

Impact of Magnetic Field Configuration on Heat Transport in Stellarators and Heliotrons

We assess the magnetic field configuration in modern fusion devices by comparing experiments with the same heating power, between a stellarator and a heliotron. The key role of turbulence is evident in the optimized stellarator, while neoclassical processes largely determine the transport in the heliotron device. Gyrokinetic simulations elucidate the underlying mechanisms promoting stronger ion scale turbulence in the stellarator. Similar plasma performances in these experiments suggests that neoclassical and turbulent transport should both be optimized in next step reactor designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A practical approach to determine minimal quantum gate durations using amplitude-bounded quantum controls

Here, we present an iterative scheme to estimate the minimal duration in which a quantum gate can be realized while satisfying hardware constraints on the control pulse amplitudes. The scheme performs a sequence of unconstrained numerical optimal control cycles that each minimize the gate fidelity for a given gate duration alongside an additional penalty term for the control pulse amplitudes. After each cycle, the gate duration is adjusted based on the inverse of the resulting maximum control pulse amplitudes by re-scaling the dynamics to a new duration where control pulses satisfy the amplitude constraints. Those scaled controls then serve as an initial guess for the next unconstrained optimal control cycle, using the adjusted gate duration. We provide multiple numerical examples that each demonstrate fast convergence of the scheme toward a gate duration that is close to the quantum speed limit, given the control pulse amplitude bound. The proposed technique is agnostic to the underlying system and control Hamiltonian models, as well as the target unitary gate operation, making the time-scaling iteration an easy to implement and practically useful scheme for reducing the durations of quantum gate operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Nanosecond Laser Drying in Wafer Foundries for Significant Energy Efficiency Improvement

Surface drying is a critical process in microelectronics wafer fabrication. In order to reduce and eliminate watermarks, many surface drying processes have been studied and used. Most of the wafer foundries currently use drying processes based on Isopropyl Alcohol (IPA), which causes serious safety (fire), health, environmental, and energy efficiency issues. The long-term goal of this project is to develop a fast, effective, chemical-free, and extremely energy efficient wafer drying technology that employs nanosecond laser-induced sub-surface evaporation and explosion. The objectives of this project are to 1) experimentally develop and verify the laser surface drying method, explore and identify how and to what extent various physical parameters influence the drying efficiency and effectiveness; and 2) understand and optimize the underlying thermodynamics in the proposed laser surface drying, including micro/nanoscale sub-surface superheating, evaporation, explosion, and photo/thermochemical bond breaking via atomistic modeling and experimental characterization. Toward these objectives, five tasks have been accomplished successfully, including 1) develop in-lab laser surface drying setup and study the effect of varied laser parameters, 2) use multiscale hybrid-modeling to study and understand the physics of water behavior during drying toward process optimization, 3) investigate the effect of drying conditions on the drying effectiveness, 4) develop a large-scale laser drying scanning system with automatic scanning, and 5) conduct large-scale simulation to study the effect of dopant level and develop strategies for system development toward industry deployment.

42 ENGINEERING↗

DOE OSTI2026

Data for Optimization of Pre-Commercial Enzymes Dosage for a Potential Lignocellulosic Biorefinery

Lignocellulolytic enzymes remain one of the primary cost constraints in second-generation (2G) ethanol biorefineries. Achieving efficient hydrolysis of structural carbohydrates with minimal enzyme dosage, maintaining slurry fermentability for industrially relevant ethanol titers, and maximizing ethanol yield per ton of biomass are among the major challenges in 2G processes. In this study, we optimized the dosages of pre-commercial cellulase (NS22257) and hemicellulase (NS22244) on pilot-scale, hydrothermally pretreated lignocellulosic substrates. Enzyme dosages were evaluated at three levels: 20 mg of cellulase with 7.25 mg of hemicellulase (ED-1), 40 mg with 14.5 mg (ED-2), and 60 mg with 21.75 mg (ED-3). As expected, the highest sugar yields were obtained with ED-3; however, for sweet sorghum, oilcane, and miscanthus, sugar yields from ED-2 and ED-3 were not significantly different (p < 0.05). For example, sweet sorghum produced 123.78 ± 1.54 g L−1 and 125.76 ± 0.46 g L−1 of total sugars (glucose and xylose) with ED-2 and ED-3, respectively. Although energycane exhibited a statistically significant difference between ED-2 and ED-3, the incremental gain with ED-3 was modest, increasing sugar release by only 9.02 g L−1 relative to ED-2. Importantly, ED-1 resulted in sugar yields of 88.88 ± 3.64 to 106.86 ± 1.21 g L−1, sufficient to achieve ethanol titers ≥40 g L−1, the threshold required for industrial relevance. A semi-integrated bioprocess validated this outcome, producing 42.09 ± 2.38 g L−1 ethanol and an estimated yield of 213.38 L of ethanol per dry ton of pretreated biomass, requiring only 20.83 L of cellulase and 6.25 L of hemicellulase per ton. Remarkably, these enzyme dosages were approximately tenfold lower than those reported in prior studies.

Energycane↗

DOE OSTI2024

Surpassing millisecond coherence in on chip superconducting quantum memories by optimizing materials and circuit design

The performance of superconducting quantum circuits for quantum computing has advanced tremendously in recent decades; however, a comprehensive understanding of relaxation mechanisms does not yet exist. In this work, we utilize a multimode approach to characterizing energy losses in superconducting quantum circuits, with the goals of predicting device performance and improving coherence through materials, process, and circuit design optimization. Using this approach, we measure significant reductions in surface and bulk dielectric losses by employing a tantalum-based materials platform and annealed sapphire substrates. With this knowledge we predict the relaxation times of aluminum- and tantalum-based transmon qubits, and find that they are consistent with experimental results. We additionally optimize device geometry to maximize coherence within a coaxial tunnel architecture, and realize on-chip quantum memories with single-photon Ramsey times of 2.0 – 2.7 ms, limited by their energy relaxation times of 1.0 – 1.4 ms. These results demonstrate an advancement towards a more modular and compact coaxial circuit architecture for bosonic qubits with reproducibly high coherence.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Characterization of Mechanical Performance of Composites Fabricated Using Innovative Carbon Fiber Wet Laid Process

Recent innovation in production of optimized nonwoven wet laid (WL) carbon fiber (CF) mats raised the question of optimal translation of the performance and isotropy into composites formed through these dry preforms. This work explores the mechanical behavior of composites produced from WL-CF mats in conjunction with the microstructure predicted through Object Oriented Finite Element Analysis (OOF). The mats used for the composites were prepared in two dispersion regimes using 25.4 mm long CF. The mixing regimes discussed in the author’s previous work, are identified as Method 1 for the traditional processing regime and Method 2 for the innovative regime that provided optimal nonwoven WL-CF mats. Composite panels from Method 2 mats showed a normalized tensile strength increase of 52% over those from Method 1 panels. Reproducibility analysis of composites made from Method 2 mats demonstrated a standard deviation of 2% in fiber weight content, 2% in tensile modulus and 9% in tensile strength, while composites made from Method 1 mats demonstrated a standard deviation of 5% in fiber weight content, 5% in tensile modulus and 17% in tensile strength. Systematic study of the microstructure and its analysis through OOF confirmed the isotropy translation of mats produced through method 2 to the composites. This study validated the hypothesis that optimal nonwoven mats lead to a well-balanced composite with optimal performance and that non-optimal nonwoven mats do not pack into a well-balanced composite.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Optimal Energy Storage Schedules for Load Leveling and Ramp Rate Control in Distribution Systems

Continued integration of distributed energy resources (DERs) into the grid, such as solar PVs, at a large-scale, contributes into the famous Duck Curve. New DER management algorithms are therefore deemed necessary to alleviate rapid variations within net load profiles of distribution systems. Herein this paper proposes a process to determine the optimal energy storage schedules for leveling the distribution circuit feederhead net load. A series of sensitivity analyses shows how the proposed method can be used to determine the optimal energy storage schedules with different capacities, state of charge requirements, and net load ramp rate limitations.

25 ENERGY STORAGE↗

DOE OSTI2022

Radically Engineered Modular Air Separation System with Tailored Oxygen Sorbents

The commercial energy sector relies heavily on fossil fuel conversion, and in the process releases a significant amount of CO 2 . A promising technology to utilize fossil fuels with relatively affordable CO 2 capture is gasification. However, it requires a pure oxygen stream. The current state of the art method to produce oxygen is cryogenic air separation, which supercools air to a liquid, and then using distillation columns to separate the components. While this method has been thoroughly studied, it only has a 25% efficiency from a second law standpoint and therefore requires a significant amount of energy (and associated emissions) for oxygen production. This, then, lowers the incentive for carbon capture within a plant, so alternative methods need to be investigated. One potential method, chemical looping air separation (CLAS), is a promising method to replace state of the art oxygen generation technologies. CLAS utilizes a cyclic redox scheme with an oxygen sorbent to create pure oxygen streams. This approach typically utilizes a dual reactor scheme where the oxygen deficient sorbent enters the first reactor and is subjected to high oxygen partial pressures to re-oxidize the sorbent. Then the sorbent is sent to the reducing reactor, where it is subjected to low oxygen partial pressure (steam or vacuum) to releases oxygen. The overarching objective of this project was to discover the principles for rational design and optimization of oxygen sorbents and process design to ensure the process is a viable replacement for cryogenic air separation, especially in the context of modular gasification systems. This was done through development, characterization, testing, and analyses of (a) high temperature mixed composite oxides; (b) low temperature doped perovskite oxides (A1 x A2 1-x B1 y B2 1-y O 3 ); (c) scale up synthesis and testing of the optimized sorbent particles; (d) process design and analyses of the CLAS system in the context of modular gasification applications.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗

DOE OSTI2024

Image processing tools for petabyte-scale light sheet microscopy data

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson–Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Simple and Scalable Process for Nanocellulose Production from Residues and Waste (CRADA Final Report)

This project successfully developed an innovative, cost-effective, and scalable method for converting low-cost agricultural waste feedstocks into nanocellulose—a sustainable material with wide-ranging industrial applications. It addressed the critical challenges of high production costs and limited global supply, which have hindered the widespread adoption of nanocellulose in industry. Through this work, significant advancements were made in optimizing resource use, improving process efficiency, and reducing costs. Notable achievements included a 50% reduction in water usage, 30% lower chemical consumption, and 20% energy savings, all while maintaining high-quality product standards. The technical feasibility of the process was further validated at a 10L scale in collaboration with the Advanced Biofuels and Bioproducts Development Unit (ABPDU), demonstrating scalability and replicability. Key challenges in reducing nanocellulose production costs were addressed by utilizing low-cost biomass feedstocks and implementing low-temperature conversion reactions, leading to lower capital and operating expenses. The project also mitigated financial risk by generating critical data on the feasibility, adaptability, and scalability of the conversion technology, paving the way for its commercial implementation. Public benefits include advancing the circular bioeconomy, reducing environmental impact, fostering job creation, and enabling a shift toward biobased materials as sustainable alternatives to fossil-based products. These outcomes align closely with national goals to reduce greenhouse gas emissions and promote sustainable technological innovation.

09 BIOMASS FUELS↗

DOE OSTI2023

HFTS-1 (Final Report)

Despite the long history of hydraulic fracturing, the optimal number of fracturing stages during multi-stage fracture stimulation in horizontal wells can be a complex subject. In addition to the increased expense of multistage fracturing in horizontal wells, increasing the number of fracturing stages does not always correlate with a commensurate increase in production. The problem is the application of a uniform fracture stimulation design to all stages with no consideration for geological variations along the wellbore. The result is an inefficient use and costly waste of energy and water. Optimization of the fracturing process requires an understanding of the cause-and-effect relationship between fracturing parameters and local geological properties at a given location along the wellbore. Realizing that the generalized rock mechanics theories and hypotheses are not truly applicable to fractured and laminated shales, quantifiable impacts of a shale’s geomechanical and depositional features are a prerequisite for design and implementation of optimized hydraulic fractures. The overarching goal of this project is to understand and define the relationships of shale geology and fracture dynamics using detailed field data that includes coring of the fracture domain. Finally, resource recovery from shale formations is estimated to be less than fifteen percent. Research proposed in this project will establish the foundations for investigating enhanced recovery techniques for increased resource recovery in existing fracture treated wells. Natural gas as an EOR fluid is in the initial stages of being used broadly in the oil and gas industry and as such, many aspects of the process need to be researched and addressed prior to widespread acceptance. In addition, re-fracturing techniques, particularly using cemented liners also need to be further investigated. This project explores several of these EOR techniques in greater detail, not just at the theoretical level, but through actual field test sites. This report delves into the research program, identified tasks, several studies, observations, results, and conclusions under the Hydraulic Fracturing Test Site (HFTS) project. This test project is subdivided into three distinct phases and each phase is discussed in detail under separate sections. The original test site for Phase 1 as well as the Phase 2 site are in Midland Basin in Texas and operated by Laredo Petroleum. In the first two phases, the program targeted the Wolfcamp formation in Midland Basin. Phase 3 of the study targeted the EagleFord play in South Texas with Devon Energy as the operator. Significant conclusions from this study include: 1) lateral and vertical fracture growth mapping as well as proppant transport behavior, 2) Use of field gas and its applicability for huff n puff EOR injection in the Midland Basin, 3) Development and validation of proppant log, both at core scale as well as in-fill well implementation by analyzing drilling mud return samples, 4) Applicability of liner refracturing including best practices for enhanced unit level productivity. This report will detail all these studies as well as technical results and conclusions from said studies. These include general subsurface characterization work, completion designs as implemented, studies looking at fracture geometry, high resolution microseismic study, advanced crosswell strain mapping studies including drainage characterization, core characterization results, proppant analysis (core and observation wells), geochemistry, etc. to name a few.

02 PETROLEUM↗

DOE OSTI2024

Core Design Optimization of the Westinghouse Lead Fast Reactor

Westinghouse is pursuing an advanced Nuclear Power Plant design based on Lead Fast Reactor (LFR) technology for global commercialization. To achieve an optimal combination of key attributes, such as safety, sustainability, and economic competitiveness, Westinghouse and ANL partnered in developing and applying a formalized core design optimization strategy. An LFR analysis workflow was developed to automate a suite of reactor physics, fuels performance, safety, and economics simulations on a selected LFR concept. The workflow streamlines analysis of a wide range of LFR designs with different dimensions and fuel types to assess their viability and economic performance, significantly reducing human processing time and risks of processing errors. The LFR optimization exercise was defined, resulting in selection of the design constraints (geometric, neutronics, thermo-mechanical, safety, thermal-hydraulics, and economics) and performance metrics researched (minimization of both the fuels LCOE and the first core inventory cost). A total of 14 varied design parameters were considered, including assembly dimensions, coolant temperature, and enrichment distribution throughout the core. The LFR analysis workflow was connected to DAKOTA for sensitivity and optimization analyses. Due to the extremely large size of the potential LFR optimization solution space relative to the computing time required to characterize one LFR solution, a multi-stage optimization approach was proposed to breakdown the problem into several stages with more reasonable sizes. This optimization approach enabled finding various viable core solutions with different cost tradeoffs that were considered by Westinghouse and justify selection of a smaller core with multi-batch 2-year cycle length.

Stauff, Nicolas E.↗

DOE OSTI2021

System Configuration Evaluation for Process Settling of Hanford Waste Solid Particles

Direct Feed High-Level Waste (DFHLW) is a potential flowsheet operations approach to initiating high-level waste (HLW) vitrification prior to completion of the Hanford Waste Treatment and Immobilization Plant (WTP) Pretreatment Facility. A settle/decant process has been proposed to concentrate solids prior to delivery to the WTP HLW Facility during DFHLW operations, wherein the solids in a settled layer would be remixed with the supernatant liquid remaining after decanting operations to provide the feed at required solids concentrations. Settling would be used in lieu of purpose-built filtration or other solids separation equipment. Pacific Northwest National Laboratory (PNNL) is providing baseline technical support to the Washington River Protection Solutions (WRPS) Flowsheet Integration group. To support planning for DFHLW, WRPS previously requested that PNNL evaluate the current data set available to predict the time needed for HLW solids to settle and the solids concentration and strength of that settled layer, to identify gaps in the understanding and predictive capability of HLW solids waste settling times, and to provide scoping estimates of the potential settling times. Eight technical gaps were identified for predicting settling times and characteristics of the formed sediment layers. In addition to the data gaps, an overarching observation was made that there is significant variation in behavior of settling rate and settled layer data. The settling time required to concentrate solids via a settle/decant process was determined from the limited data to have a difference of potentially more than a factor of 5,000 in the estimated settling times, varying from 0.2 to 1,060 days for example depending on process vessel depth and final sediment solids concentration. In contrast, successful processes of liquid-forward output streams resulting from in-tank settling and decanting forward liquid have been reported for operations conducted at the Hanford Site. The purpose of this current report is to further support DFHLW planning by evaluating double-shell tank (DST) and alternate vessel equipment and operational configurations to enable optimization of the settle/decant process to concentrate solids. Hanford waste processing behavior specific to liquid feed availability following a slurry transfer in a DST is summarized, including process stream characteristics and process equipment configurations. The performance of DST process equipment configurations is evaluated for possible improvements using computational fluid dynamics (CFD) and simple analytical models. Potential new vessel design(s) specific to enabling effective settle/decant processes, and cursory summary of other separate and inline solids separations processes, are also provided. The CFD results indicated that improvement in outflow solids concentration was promoted by a reduction in the slurry flow rate, angling the distributor nozzles downward, and lifting the transfer pump. The solid-liquid analysis evaluating particle trajectory confirmed that the potential for particle ingestion (in the transfer pump) was decreased with increased radial separation between the inlet and outlet (transfer pump inlet), decreased inlet flow, and decreased liquid density and viscosity for a neutrally buoyant inlet flow. An assessment was also made of the potential for inflow configuration changes to result in the discrete mounding or piling of solids within the tank. Based on the characterization of the settled waste to date, HLW sediments will be unlikely to sustain a substantial angle of repose to facilitate significant variations in the elevation of the settled solids.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Properties of Nb x Ti (1–x) N thin films deposited on 300 mm silicon wafers for upscaling superconducting digital circuits

Scaling superconducting digital circuits requires fundamental changes in the current material set and fabrication process. The transition to 300 mm wafers and the implementation of advanced lithography are instrumental in facilitating mature CMOS processes, ensuring uniformity, and optimizing the yield. Here, this study explores the properties of Nb x Ti (1–x) N films fabricated by magnetron DC sputtering on 300 mm Si wafers. As a promising alternative to traditional Nb in device manufacturing, Nb x Ti (1–x) N offers numerous advantages, including enhanced stability and scalability to smaller dimensions, in both processing and design. As a ternary material, Nb x Ti (1–x) N allows engineering material parameters by changing deposition conditions. The engineered properties can be used to modulate device parameters through the stack and mitigate failure modes. We report characterization of Nb x Ti (1–x) N films at less than 2% thickness variability, 2.4% T c variability and 3% composition variability. Film resistivity (140–375 Ωcm) shows a strong correlation with the film oxygen content, while the critical temperature T c (4.6 K–14.1 K) is strongly affected by film stoichiometry and its microstructure has only a moderate effect on modifying T c . Our results offer insights about the interplay between film stoichiometry, film microstructure and critical temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗