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DOE OSTI2022

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

New-to-nature chemistry from old protein machinery: carbene and nitrene transferases

Hemoprotein-catalyzed carbene and nitrene transformations have emerged as powerful tools for constructing complex molecules; they also nicely illustrate how new protein catalysts can emerge, evolve and diversify. These laboratory-invented enzymes exploit the ability of proteins to tame highly reactive carbene and nitrene species and direct their fates with high selectivity. New-to-nature carbene and nitrene transferases catalyze many useful reactions, including some that have no precedent using chemical methods. Here we cover recent advances in this field, including alkyne cyclopropenation, arene cyclopropanation, carbene C–H insertion, intramolecular nitrene C–H insertion, alkene aminohydroxylation, and primary amination. For such transformations, biocatalysts have exceeded the performance of reported small-molecule catalysts in terms of selectivity and catalyst turnovers. Lastly, we offer our thoughts on using these new enzymatic reactions in chemical synthesis, integrating them into biological pathways and chemo-enzymatic cascades, and on their current limitations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. This research assesses the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Validation of Robustness in TCR Design Strategies

Advanced Manufacturing (AM) has been identified as one of key pathways in accelerating deployment of next generation of nuclear power plants. The nuclear industry continues to predominately rely on fabrication techniques and materials that their codes and standards were approved decades ago. This legacy approach in the highly regulated nuclear sector has brought uncertainty to commercial viability of nuclear energy in the U.S. The Transformational Challenge Reactor (TCR), sought to utilize AM methods for fabrication of a High Temperature Gas Reactor (HTGR). The program sought to leverage AM to construct a non-conventional geometry made up of TRISO fuel and hydride moderator in SiC matrix. This project objective was to provide independent analysis of the core configuration with separate set of tools and approaches than utilized by the TCR team in a 2-year time frame. This NEUP project also tackled a question: Can AM be a cost-effective tool in changing the commercial paradigm of nuclear. We investigated this question in-context of a micro HTGR. While respecting the inherit uncertainty in our input parameters, overall we saw that AM has potential in improving economic competitiveness of nuclear energy when employed on multiple fronts (i.e. few percentage cost improvement per component). Given realizing AM requires mass orders and large R&D investment for commercial utilization, Department of Energy should focus its support on few key enabling technologies and support their codes and standards development and qualification cost (including irradiation as applicable) rather than taking a diluted approach.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials

Abstract Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness and damage tolerance of such multilevel materials are conferred through the hierarchical assembly of their multiscale (i.e., atomistic to macroscale) components and architectures. Here, the functionality and mechanisms of biopolymers and bio‐inspired materials at multilength scales are explored and summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, and bio‐inspired composites. Their modeling methods with theoretical basis at multiple lengths and time scales are reviewed for biopolymer applications. Additionally, the exploration of artificial intelligence‐powered methodologies is emphasized to realize improvements in these biopolymers from functionality, biodegradability, and sustainability to their characterization, fabrication process, and superior designs. Ultimately, a promising future for these versatile materials in the manufacturing of advanced materials across wider applications and greater lifecycle impacts is foreseen.

Wang, Xing Quan [Department of Mechanical Engineer↗

DOE OSTI2020

Update on the United States Advanced Ultra-Supercritical Program with Full Scale Component Fabrication to Develop Utility Scale Power Plants with 760°C Steam Temperature

Following the successful completion of a 15-year effort to develop and test materials that would allow coal-fired power plants to be operated at advanced ultra-supercritical (A-USC) steam conditions, a United States-based consortium is presently engaged in Phase 2 of a project which includes an advanced manufacturing effort to complete US-based supply chain development for full commercial scale (800-850 MWe) A-USC components made of nickel-based alloys, components operating at up to 760°C. The project, funded under U.S. Department of Energy Award No. DE-FE0025064, falls under the US DOE overall goal to Develop cost-effective, reliable technologies to improve the efficiency of new and existing coal-fired power plants. A-USC steam cycles have the potential to improve cycle efficiency, reduce fuel costs, and reduce greenhouse gas emissions. Current development and demonstration efforts are focused on enabling the construction of A-USC plants, operating with steam temperatures as high as 1400°F (760°C) and steam pressures up to 5000 psi (35 MPa), which can potentially increase cycle efficiencies to 47% HHV (higher heating value), or approximately 50% LHV (lower heating value), and reduce CO 2 emissions by roughly 25%, compared to today’s U.S. fleet. A-USC technology provides a lower-cost method to reduce CO 2 emissions, compared to CO 2 capture technologies, while retaining a viable coal option for owners of coal generation assets. Among the specific goals of Phase 2 of the ComTest Project are to: validate that components made from advanced nickel-based alloys can operate and perform under A-USC conditions accelerate the development of a U.S.-based supply chain for the full complement of A-USC components close the remaining gaps and reduce the risks for manufacturing components from advanced materials for commercial demonstration fabricate full-scale versions of selected key components made of nickel-based alloys validate a qualified U.S. supply chain, to provide greater cost certainty for components obtain American Society of Mechanical Engineers (ASME) code approval for new materials, components and processes Additional benefit: Manufacturing technology will be applicable to other advanced fossil energy high temperature cycles, and can support increased flexibility of existing power plants The ComTest project is managed by Energy Industries of Ohio, and technically directed by the Electric Power Research Institute, Inc., with General Electric designing the A-USC components. As stated earlier, the current phase of the effort is primarily funded by the U.S. Department of Energy, through the National Energy Technology Laboratory. This presentation outlines the motivation for the project, explains the project’s structure and schedule, and provides some of the technical details on the design of the ComTest components.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Update on the United States Advanced Ultra-Supercritical Program with Full Scale Component Fabrication to Develop Utility Scale Power Plants with 760°C Steam Temperature

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

CEFAS Poster for Advanced Manufacturing Workshop 3/26/24

Electric-field assisted sintering (EFAS) is an advanced manufacturing method for the consolidation and processing of ceramic and metal materials. It is a particularly excellent candidate for efficient materials processing as it has greater than 90% energy cost savings and improved CO2 emissions compared to traditional sintering in a furnace. This is due to direct rapid heating of the materials and molds leveraging an electrification phenomenon called Joule heating where electrical current is passed through the sample/mold assemblies causing direct heating. The technique does have limitations though as it is traditionally a batch-process and suffers from scale up difficulties. To overcome this, researchers at Idaho National Laboratory changed the form factor of traditional EFAS instruments to develop and construct a continuous rolling electric-field assisted sintering (CEFAS) device.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full-Waveform Inversion

Concrete is a vital material in construction—especially in the nuclear industry, where it is used in critical structures such as containment vessels. Over time, concrete can degrade due to harsh operational and environmental conditions, necessitating that its elastic properties be accurately evaluated to ensure structural integrity and safety. Traditional nondestructive evaluation methods such as ultrasound-based techniques often rely on simplifying assumptions that may not hold true for concrete. This paper presents an advanced ultrasound-based method that uses elastic full-waveform inversion (EFWI) to create detailed images of concrete’s mechanical properties. By accurately modeling wave behaviors such as scattering and reflection, we aim to overcome the limitations of conventional ultrasonic-based methods. In this work, the imaging problem involved reconstructing the various elastic properties of a heterogenous concrete block with three steel rebars embedded in it. The ultrasonic measurements were synthetically generated from multiple sources and receivers, and the reconstruction process was performed using a gradient-based optimization algorithm. Our approach leveraged EFWI to reconstruct high-resolution images of the pressure wave speed, shear wave speed, and density. Multiple misfit functions—including L2-norm, cross-correlation (CC), and L1-norm—combined with total variation (TV) regularization and parameter constraints using a Sigmoid function—were explored for the reconstruction. The results demonstrated that using the L1-norm misfit function in conjunction with TV regularization and Sigmoid constraints significantly improved the reconstruction quality in comparison to traditional methods. This approach provided clearer images with fewer artifacts and better captured background heterogeneity. Our findings highlight that, when properly designed, EFWI carries great potential for providing comprehensive, more accurate, and more reliable assessments of concrete conditions, as is crucial for the maintenance and safety of nuclear power plant structures.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Laser Doppler vibrometry for piezoelectric coefficient ($d_{33}$) measurements in irradiated aluminum nitride

Sensors used for experiments in advanced reactors must survive in harsh environments. Few material systems can be used to construct sensors viable for extreme conditions. Aluminum nitride (AlN) is one such material because it has high thermal stability and radiation resistance and sustains good piezoelectric and dielectric properties at high temperatures. In this work, the piezoelectric coefficient $d_{33}$ of the AlN single-crystal with thermal and irradiation damage was investigated using an indirect method with a laser Doppler vibrometer (LDV). Surface electrodes were deposited on the AlN samples, and the vibration response of the samples to an applied voltage was monitored using the LDV as a function of the excitation frequency. The $d_{33}$ estimation was based on the excitation voltage and the thickness-mode displacement extracted from the LDV measurements. Further, six AlN substrates were irradiated with 8 MeV Al 2+ at three fluences (10 15 , 10 16 , and 10 17 ions/cm 2 ) and two temperatures (300 °C and 500 °C). The $d_{33}$ for the six irradiated samples and one pristine sample were measured, and the measurement uncertainty was estimated based on five repeated tests. All samples were also measured by a commercial piezometer for comparison. The experimental results demonstrate that the piezoelectric coefficients obtained by LDV were about 0.8–1.16 pm/V lower than those obtained by the piezometer. With the compensation of the clamping effect, the corrected LDV values are similar to the piezometer results. Both show similar trends in all samples, which validates the feasibility of the proposed method for $d_{33}$ measurement. Based on the LDV results, the irradiated samples show a 12%–22% decrease in $d_{33}$ compared with the pristine samples. The samples irradiated under the same fluence at a higher temperature (500 °C) demonstrated a lower $d_{33}$ than those at 300 °C. The effect of the retro-reflective tape, sample temperature, and sample size on the $d_{33}$ measurement were also studied.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Parallel-in-Time Multigrid Methods for Hyperbolic Problems, with a Focus on the Shallow Water Equations

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations, which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic problems of programmatic interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic problems on future architectures. The difficulty lies in the fact that hyperbolic problems are well-known to be difficult for parallel-in-time methods, with the most common parallel-in-time method, Parareal, diverging in many cases. Here, the chosen methodology for scalably solving these hyperbolic space-time equations is multigrid-reductionin-time (MGRIT), because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. To further research in this area, this project will explore a model hyperbolic problem (the shallow water equations) in the context of recent advances (e.g., by Wingate and Haut) in constructing improved coarse time-grid time-propagators by using the slow asymptotic structure of the equations. In particular, we will investigate if such coarse time-propagators offer significant advantages in an MGRIT setting, and explore any broader insights gained into parallel-in-time for hyperbolic problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Benchmarking second and third-generation sequencing platforms for microbial metagenomics

Shotgun metagenomic sequencing is a common approach for studying the taxonomic diversity and metabolic potential of complex microbial communities. Current methods primarily use second generation short read sequencing, yet advances in third generation long read technologies provide opportunities to overcome some of the limitations of short read sequencing. Here, we compared seven platforms, encompassing second generation sequencers (Illumina HiSeq 300, MGI DNBSEQ-G400 and DNBSEQ-T7, ThermoFisher Ion GeneStudio S5 and Ion Proton P1) and third generation sequencers (Oxford Nanopore Technologies MinION R9 and Pacific Biosciences Sequel II). We constructed three uneven synthetic microbial communities composed of up to 87 genomic microbial strains DNAs per mock, spanning 29 bacterial and archaeal phyla, and representing the most complex and diverse synthetic communities used for sequencing technology comparisons. Our results demonstrate that third generation sequencing have advantages over second generation platforms in analyzing complex microbial communities, but require careful sequencing library preparation for optimal quantitative metagenomic analysis. Our sequencing data also provides a valuable resource for testing and benchmarking bioinformatics software for metagenomics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

The physical chemistry of solar fuels catalysis

The demonstration of effective light-driven electrochemical water splitting at the surface of a TiO 2 electrode in the seminal 1972 Honda–Fujishima publication spurred global research efforts to understand this effect and design advanced systems for solar H 2 generation from water as a carbon-free and sustainable fuel. In the 50+ years since that discovery, research investigating approaches toward the catalytic generation of the so-called “solar fuels,” i.e., not only H 2 from water but also CO 2 reduction products and NH 3 from N 2 , has expanded dramatically in scope and scale. The development of new experimental and computational methods and construction and commissioning of large-scale facilities have paved the way for important insights into the machinery of natural photosynthesis, leading to a greater understanding of the complexity of light-to-chemical energy conversion with an exquisite temporal and spatial resolution. Finally, these studies have provided the foundation for biologically inspired molecular and hybrid designs with the goal to promote the photochemical steps critical to solar fuels generation while avoiding unproductive or unnecessary pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Dynamic Construction, Perception, and Remodeling of Plant Cell Walls

Plant cell walls are dynamic structures that are synthesized by plants to provide durable coverings for the delicate cells they encase. They are made of polysaccharides, proteins, and other biomolecules and have evolved to withstand large amounts of physical force and to resist external attack by herbivores and pathogens but can in many cases expand, contract, and undergo controlled degradation and reconstruction to facilitate developmental transitions and regulate plant physiology and reproduction. Recent advances in genetics, microscopy, biochemistry, structural biology, and physical characterization methods have revealed a diverse set of mechanisms by which plant cells dynamically monitor and regulate the composition and architecture of their cell walls, but much remains to be discovered about how the nanoscale assembly of these remarkable structures underpins the majestic forms and vital ecological functions achieved by plants.

biofuels (including algae and biomass), bio-inspir↗

DOE OSTI2023

Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection

Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Gas Stopper Developments for Improved Purity and Intensity of Low-Energy, Rare Isotope Ion Beams (Final Technical Report)

This final technical report summarizes the work of the Michigan State University (MSU) team supported by grant # DE-SC0021423 awarded by the Office of Nuclear Physics, Department of Energy. Objectives: The successful fulfillment of the FRIB science mission hinges on ensuring the availability of fast, stopped, and reaccelerated beams consisting of rare isotopes. This project's research and development focus was dedicated to supporting the advancement and creation of a cutting-edge linear gas stopper. The primary aim is to efficiently convert the high-intensity fast beams of rare isotopes provided by FRIB into high-quality, low-energy beams. These beams are essential for conducting stopped beam experiments or for subsequent reacceleration. The overarching goal is to advance technology, aiming to increase the beam rate capability of the linear gas stopper for medium-to-heavy-mass rare isotopes by more than tenfold compared to the currently most effective gas stopper in operation, and to improve the purity of the delivered beams. Project Description: The existing technology employed in gas stopping devices designed for low-energy, rare-isotope beams presents limitations in both the purity of extracted beams and the intensities of injected beams. These limitations are incompatible with the requirements of the recently commissioned rare isotope beam facility, FRIB. Our research and development efforts, aligned with the previously outlined objectives, focused on addressing the most critical aspects for enhancing beam-rate capability and purity. Specifically, advanced particle-in-cell simulations were developed and integrated into a simulation pipeline to explore the efficacy of multi-layer RF carpets on increasing ion transport efficiency with high incoming beam rates that generate space charge fields which can limit it. We also explored the possibility of using a collision-induced-dissociation (CID) gas cell to break up molecular contaminant ions that are generated during the stopping process. A prototype CID gas cell was constructed and tested with beams from an offline ion source, validating the concept with the successful demonstration of breaking of molecular ions. The outcome of this research enabled the formulation a conceptual design for a next-generation linear gas stopping device specifically tailored for FRIB. This device is envisioned to deliver rare-isotope-ion beams at a rate of 10 8 particles per second or higher, accompanied by advancements in purity. Methods employed: This project leverages advancements in technologies initially designed for the Advanced Cryogenic Gas Stopper (ACGS), the current state-of-the-art linear gas stopper, through the use of new simulations and beam purification via collision-induced-dissociation. The methods include: 1. Development of a prototype low-energy, low-pressure CID gas stopper. This prototype features a thin, approximately 20 nm, Si 3 N 4 entrance window designed for dissociating stable and rare-isotope molecular ions. The goal is to enhance beam purification and overall efficiency. 2. Creation of Particle-in-cell (PIC) simulations to assess the advantages of multi-layer RF carpets and multi-point extraction for ion transport efficiency. These simulations rely on the 3DCylPIC package, specifically designed for studying devices of this nature. The goal is to quantify and mitigate ion transport losses due to space charge generated in the stopping process of large numbers of ions. 3. Perform ion transport simulations across an RF carpet using an 8-phase travelling wave and evaluate its performance. Compared to the 4-phase RF carpets used in ACGS, the 8-phase carpets will double the wavelength of the generated traveling wave allowing for larger maximum RF amplitudes that could result in improved ion transport efficiency for high-intensity incoming beams when large space charge fields are present. Impact: Tackling the primary challenges associated with transforming high-energy projectile fragment beams into low-energy beams—specifically, addressing efficiency and purity—holds significant promise for advancing FRIB science. This advancement will particularly impact precision mass measurements, laser spectroscopy of short-lived nuclei, and studies in astrophysics and nuclear reactions using reaccelerated beams. These domains play a crucial role in addressing key questions outlined in the 2023 NSAC long-range plan, spanning nuclear structure, nuclear astrophysics, and fundamental symmetries. Additionally, they contribute to addressing 10 out of the 17 benchmarks identified by the NRC RISAC. The development of a next-generation gas stopping device capable of delivering low-energy, rare-isotope beams at a rate of 10 8 particles per second, or more, with high purity holds the potential to unlock experiments that would otherwise be unfeasible. Furthermore, it is expected to reduce the time required for experiments at FRIB, thereby maximizing scientific output. The research and development activities performed as part of this project bolstered essential competencies at FRIB in beam physics and ion source technologies, provided valuable training opportunities for junior scientists.

43 PARTICLE ACCELERATORS↗