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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Kaon physics: a cornerstone for future discoveries

The kaon physics programme, long heralded as a cutting-edge frontier by the European Strategy for Particle Physics, continues to stand at the intersection of discovery and innovation in high-energy physics (HEP). With its unparalleled capacity to explore new physics at the multi-TeV scale, kaon research is poised to unveil phenomena that could reshape our understanding of the Universe. This document highlights the compelling physics case, with emphasis on exciting new opportunities for advancing kaon physics not only in Europe but also on a global stage. As an important player in the future of HEP, the kaon programme promises to drive transformative breakthroughs, inviting exploration at the forefront of scientific discovery.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Energetic particle physics: Chapter 7 of the special issue: on the path to tokamak burning plasma operation

We review the physics of energetic particles (EPs) in magnetically confined burning fusion plasmas with focus on advances since the last update of the ITER Physics Basis (Fasoli et al 2007 Nucl. Fusion 47 S264). Topics include basic EP physics, EP generation, diagnostics of EPs and instabilities, the interaction of EPs and thermal plasma instabilities, EP-driven instabilities, energetic particle modes (EPMs), and turbulence, linear and nonlinear stability and simulation of EP-driven instabilities and EPMs, 3D effects, scenario optimization strategies based on EP phase-space control, EPs in reduced field scenarios in ITER before DT, and the physics of runaway electrons. We describe the simulation and modeling of EPs in fusion plasmas, including instability drive and damping as well as EP transport, with a range of approaches from first-principles to reduced models, including gyrokinetic simulations, kinetic-MHD models, gyrofluid models, reduced models, and semi-analytical approaches.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗
DOE OSTI2024

Anomaly detection in collider physics via factorized observables

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called : factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Proposed Classifications of Remote Operations for Nuclear Reactors Based on Physical and Cybersecurity Considerations

The incorporation of remote operations into reactor operations is a topic of high interest among advanced and small modular reactor (A/SMR) vendors, with some considering it essential to the success of their business models. However, remote operations are a concept novel to the nuclear industry. While various technical aspects of remote operations have been explored, a significant gap remains in understanding the security implications of integrating remote operations into reactor designs, particularly concerning the security requirements for remote-operations facilities and infrastructure. This report aims to address this gap by first defining classes of remote operation based on the extent of remote access to reactor control systems and grounded in the existing regulatory framework with compatible terminology. Secondly, the report outlines the physical and cybersecurity requirements applicable to remote-operations facilities and infrastructure at each defined class. These requirements are based on existing licensing frameworks provided by 10 Code of Federal Regulations (CFR) Part 50 and 10 CFR Part 52, as well as the upcoming A/SMR licensing framework in the proposed Part 53. The assessment focuses specifically on security regulations, such as 10 CFR Part 73, which includes provisions for both cybersecurity (§ 73.54) and physical security (§ 73.55). This report proposes five classes of remote reactor operations. Class 1 involves remote monitoring only, with no control over reactor systems. Class 2 allows for the remote issuance of allowlisted commands to the reactor facility. Class 3 extends control to non-safety-significant, non-safety-related, or not important to safety systems and equipment. Class 4 permits remote control of safety-significant systems. Finally, Class 5 allows remote control of safety-related systems. It is important to note that these classes were defined purely with functionality in mind, without considering the practicality or feasibility of implementation for each class under current or upcoming regulatory guidance. The intention behind this approach is to enable an assessment of which security requirements apply to each class, allowing readers to evaluate the implementation possibilities for their specific use cases. Following the definition of remote-operation classes, the report assesses the specific physical and cybersecurity requirements applicable to the remote-operations facility and infrastructure within each defined class. This includes defining the types and locations of operators that are possible at each class of operation and, based on operator type and location, as well as functionality within each class, outlining the physical and cybersecurity requirements. By detailing the security requirements by class, the report provides readers with the information needed to determine the type of security program they may need to implement for their desired concept of operation. The next contribution of this report was to assess the practicality of implementing each proposed class of remote operations based upon the security requirement assessment. In short, three of the five proposed remote-operation classes were found to possibly have a practical path forward to implementation under the U.S. regulatory framework. Class 1 remote operations are currently in use in the U.S. while Class 2 and 3 remote operations may be logistically possible to implement under the U.S. regulatory framework. The final two Classes, 4 and 5, would likely be logistically difficult, if not infeasible to implement within the current U.S. physical- and cybersecurity regulatory framework. Given the results of the feasibility assessment, an example architecture is proposed for both Class 2, remote allowlisted commands, and Class 3, remote control of non-safety systems as well as security implication assessments of each architecture. These example implementations are not meant to be prescriptive in terms of how Class 2 or Class 3 remote operations should be deployed; instead, they are intended to be informative to stakeholders on how Class 2 or Class 3 could potentially be applied in order to inform their system design. An example architecture for Class 1 remote monitoring was not provided as Class 1 in already in use in U.S. nuclear operations. Example architectures for Class 4 and Class 5 were not provided due to their assessment of being likely infeasible to implement. The final contribution is an assessment of the physical- and cybersecurity implications of introducing autonomous operations into an A/SMR. What was found was that the security implications can be separated into two cases. Autonomous operations supported by SSCs located only at the reactor site, and autonomous operations supported by SSCs outside of the reactor site. For the first case, the introduction of autonomous systems will likely not change the facility’s requirement to comply with existing cyber and physical security regulation

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Climate impacts of particle physics

The pursuit of particle physics requires a stable and prosperous society. Today, our society is increasingly threatened by global climate change. Human-influenced climate change has already impacted weather patterns, and global warming will only increase unless deep reductions in emissions of CO$_2$ and other greenhouse gases are achieved. Current and future activities in particle physics need to be considered in this context, either on the moral ground that we have a responsibility to leave a habitable planet to future generations, or on the more practical ground that, because of their scale, particle physics projects and activities will be under scrutiny for their impact on the climate. In this white paper for the U.S. Particle Physics Community Planning Exercise ("Snowmass"), we examine several contexts in which the practice of particle physics has impacts on the climate. These include the construction of facilities, the design and operation of particle detectors, the use of large-scale computing, and the research activities of scientists. We offer recommendations on establishing climate-aware practices in particle physics, with the goal of reducing our impact on the climate. We invite members of the community to show their support for a sustainable particle physics field (https://indico.fnal.gov/event/53795/).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Electron scattering and neutrino physics

Abstract A thorough understanding of neutrino–nucleus scattering physics is crucial for the successful execution of the entire US neutrino physics program. Neutrino–nucleus interaction constitutes one of the biggest systematic uncertainties in neutrino experiments—both at intermediate energies affecting long-baseline deep underground neutrino experiment, as well as at low energies affecting coherent scattering neutrino program—and could well be the difference between achieving or missing discovery level precision. To this end, electron–nucleus scattering experiments provide vital information to test, assess and validate different nuclear models and event generators intended to test, assess and validate different nuclear models and event generators intended to be used in neutrino experiments. Similarly, for the low-energy neutrino program revolving around the coherent elastic neutrino–nucleus scattering (CEvNS) physics at stopped pion sources, such as at ORNL, the main source of uncertainty in the evaluation of the CEvNS cross section is driven by the underlying nuclear structure, embedded in the weak form factor, of the target nucleus. To this end, parity-violating electron scattering (PVES) experiments, utilizing polarized electron beams, provide vital model-independent information in determining weak form factors. This information is vital in achieving a percent level precision needed to disentangle new physics signals from the standard model expected CEvNS rate. In this white paper, we highlight connections between electron- and neutrino–nucleus scattering physics at energies ranging from 10 s of MeV to a few GeV, review the status of ongoing and planned electron scattering experiments, identify gaps, and lay out a path forward that benefits the neutrino community. We also highlight the systemic challenges with respect to the divide between the nuclear and high-energy physics communities and funding that presents additional hurdles in mobilizing these connections to the benefit of neutrino programs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

SU(3) Gauge Symmetry: An Experimental Review of Diffractive Physics in e+p, p+p, p+ A, and A+A Collision Systems

This review focuses on diffractive physics, which involves the long-range interactions of strong nuclear force at high energies described by SU(3) gauge symmetry. It is expected that diffractive processes account for nearly 40% of the total cross-section at LHC energies. These processes consist of soft-scale physics where perturbation theory cannot be applied. Although highly successful and often described as a perfect theory, quantum chromodynamics relies heavily on perturbation theory, a model best suited for hard-scale physics. The study of pomerons could help bridge the soft and hard processes and provide a complete description of the theory of the strong interaction across the full momentum spectrum. Here, we will discuss some of the features of diffractive physics, experimental results from SPS, HERA, and the LHC, and where the field could potentially lead. With the recent publication of the odderon discovery in 2021 by the D0 and TOTEM collaborations and the new horizon of physics that lies ahead with the upcoming Electron-Ion Collider at Brookhaven National Laboratory, interest is seemingly piquing in high energy diffractive physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics.

The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. These new methods will become increasingly important in the coming years as the volume of survey data continues to increase, and as the degeneracy between predictions of different physical models grows. There are several perspectives on the divergences between the values of cosmological parameters, such as the model-independent probes in the late Universe and model-dependent measurements in the early Universe, which we cover at length. The White Paper closes with a number of recommendations for the community to focus on for the upcoming decade of observational cosmology, statistical data analysis, and fundamental physics developments.

Dienes, Keith [Univ. of Arizona, Tucson, AZ (Unite↗
DOE OSTI2021

Physical protection regulates microbial thermal responses to chronic soil warming

Climate warming can affect the temperature sensitivity of microbial activity and growth efficiency, possibly explained by changes to microbially unavailable carbon (C) protected within in soil aggregates. Here, we assessed physical protection by crushing macroaggregates (250–2000 μm) and microaggregates (<250 μm) isolated from mineral soils exposed to 27 years of experimental warming (+5 °C). We hypothesized that removal of physical protection would increase microbial activity and reduce C use efficiency (CUE). We found that crushing increased microbial respiration and biomass turnover rate, but did not affect CUE. We also hypothesized that long-term warming would reduce the effect of physical protection on microbial activity, and that physical protection would attenuate microbial temperature sensitivity in heated compared to control soils. We found that long-term warming was associated with a smaller effect of physical protection for microbial respiration but with a larger effect for biomass turnover rate in macroaggregates. Physical protection reduced the temperature sensitivity of respiration but enhanced the temperature sensitivity of microbial biomass turnover rate in heated compared to control soils. Our work shows that long-term warming has contrasting effects on how microbial respiration, biomass turnover rate, and their thermal responses are mediated by physical protection within soil aggregates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CO 2 rock physics modeling for reliable monitoring of geologic carbon storage

Monitoring, verification, and accounting (MVA) are crucial to ensure safe and long-term geologic carbon storage. Seismic monitoring is a key MVA technique that utilizes seismic data to infer elastic properties of CO 2 -saturated rocks. Reliable accounting of CO 2 in subsurface storage reservoirs and potential leakage zones requires an accurate rock physics model. However, the widely used CO 2 rock physics model based on the conventional Biot-Gassmann equation can substantially underestimate the influence of CO 2 saturation on seismic waves, leading to inaccurate accounting. We develop an accurate CO 2 rock physics model by accounting for both effects of the stress dependence of seismic velocities in porous rocks and CO 2 weakening on the rock framework. We validate our CO 2 rock physics model using the Kimberlina-1.2 model (a previously proposed geologic carbon storage site in California) and create time-lapse elastic property models with our new rock physics method. We compare the results with those obtained using the conventional Biot-Gassmann equation. Our innovative approach produces larger changes in elastic properties than the Biot-Gassmann results. Using our CO 2 rock physics model can replicate shear-wave speed reductions observed in the laboratory. Our rock physics model enhances the accuracy of time-lapse elastic-wave modeling and enables reliable CO 2 accounting using seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2021

Roadmap on emerging concepts in the physical biology of bacterial biofilms: from surface sensing to community formation

Abstract Bacterial biofilms are communities of bacteria that exist as aggregates that can adhere to surfaces or be free-standing. This complex, social mode of cellular organization is fundamental to the physiology of microbes and often exhibits surprising behavior. Bacterial biofilms are more than the sum of their parts: single-cell behavior has a complex relation to collective community behavior, in a manner perhaps cognate to the complex relation between atomic physics and condensed matter physics. Biofilm microbiology is a relatively young field by biology standards, but it has already attracted intense attention from physicists. Sometimes, this attention takes the form of seeing biofilms as inspiration for new physics. In this roadmap, we highlight the work of those who have taken the opposite strategy: we highlight the work of physicists and physical scientists who use physics to engage fundamental concepts in bacterial biofilm microbiology, including adhesion, sensing, motility, signaling, memory, energy flow, community formation and cooperativity. These contributions are juxtaposed with microbiologists who have made recent important discoveries on bacterial biofilms using state-of-the-art physical methods. The contributions to this roadmap exemplify how well physics and biology can be combined to achieve a new synthesis, rather than just a division of labor.

Wong, Gerard C. L. (ORCID:0000000308936383)↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2023

Physical, Chemical, and Mineralogical Characterizations of MSWI Ash Product and Recommendations for Downstream Processing

The primary objectives of this project are to (1) systematically characterize MSWI ash, and (2) based on characterization findings, design preliminary flowsheets for downstream processing. To achieve these objectives, a total of ten tasks were completed, including sample collection, physical separation tests, liberation tests, synthetic MSWI ash preparation, elemental composition analysis, sequential chemical extraction, mineralogical characterization, pozzolanic activity characterization, thermal stability characterization, processing flowsheet design, TEA and T2M, and project performance reporting. Many useful findings and conclusions were obtained from the exhaustive efforts of this project from several different aspects, including: a) Valuable Metals in MSWI Ash: MSWI ash contains a diverse array of valuable metals. Based on potential recoverable values, the most valuable metals present in MSWI ash include Fe, Ti, Mn, Cu, Zn, V, Co, Ni, Sr, Sn, Ag, Mo, and Sc. Some of these metals have been identified as critical minerals by DOE and DOI, suggesting that MSWI is a promising feedstock for critical mineral recovery. Noticeable graphical and seasonable variations in the valuable metal content of MSWI ash were observed. Nevertheless, it was challenging to discern any clear, definitive patterns for conclusions from those observations. Compared with bottom ash, fly ash contains more volatile metals, such as Zn and Sn, but less nonvolatile metals, such as Fe, Mn, Cu, Zn, Co, and Ni. Mineralogical analyses showed that MSWI ash contains a substantial amount of calcium minerals, such as portlandite, lime, gypsum, and calcite. In addition, it was found that different types of valuable metals often exist in the same particles. b) Physical Separation of MSWI Ash: Both dry sieving and wet sieving were performed on MSWI ash. A notable disparity in the size distribution of the same material was observed when using the two different sieving methods. The disparity is due to the agglomeration of small particles. For the valuable metals investigated, no significant enrichment in a specific size fraction was observed, suggesting that it is challenging to preconcentrate the valuable metals through size fractionation. Due to the presence of ferromagnetic materials, such as Fe, most of the materials reported to the magnetic products obtained by dry magnetic separation. However, the enrichment effect is minimal due to the existence of particle agglomerates. Density separation at a cut-off density of 2.7 SG or higher led to noticeable enrichment of selected valuable metals, particularly Ti. The unburned carbon present in MSWI ash was effectively removed by flotation using diesel as the collector. A novel reagent scheme, Na2S plus cationic collectors, that can efficiently beneficiate nonferrous metals plus Co was developed. c) Liberation Tests: The particle size of MSWI ash was effectively reduced by grinding, and as a result, the encapsulated valuable metal particles (if any) were liberated to a certain degree. However, particle size reductions did not noticeably enhance the beneficiation performance using the physical separation methods, primarily due to the inefficiency of these methods in processing fine particles and/or a possibility that insufficient liberation is not a limiting factor for achieving satisfactory physical separation performance. Valuable metals were classified into water leachable, ion-exchangeable, acid soluble, reducible, oxidable, and insoluble forms. It was found that the distributions in the different categories, i.e., the occurrence modes of the valuable metals, were not affected by the particle size. d) Leaching Characteristics of Metals from MSWI Ash: Most of the valuable metals were extracted from the fly ash samples when using 1 M HCl or HNO3 as the lixiviant. The leaching reaction is a very fast process, which can reach equilibrium within the first 5 min. The releasing of Co, Ni and Ag are sensitive to leaching temperature, a higher recovery value could be obtained when using relatively higher leaching temperatures. The leachability of the valuable metals present in MSWI bottom ash is relatively lower than that of fly ash. Leaching recoveries increased with elevations in the acid concentration. Relatively high leaching recoveries were obtained for REEs, Mn, Co, Ni, Cu, and Zn using 1 M HCl or HNO3 as the lixiviant. Elevations in the reaction temperature noticeably increased the leachability of the valuable metals, whereas the leachability was barely influenced by oxidizing and reducing agents. Similar to fly ash, leaching valuable metals from bottom ash is a rapid process, with most of the leaching reaction completed within the first 5 minutes. e) Combusted iPhones: The original structure of iPhones was remained after treating at 400 ºC and 600 ºC, while after being treated at 800℃, the screen bent, and the back cover of iPhone melted. Increasing the combustion temperature to 1000℃, the screen scattered, and most of the components turned into ashes. Combustion enhanced the leachability of REEs, while the leachability of the other valuable metals, except for Zn, was barely affected. Most of the REEs present in the original iPhones occurred as oxidizable forms. With elevations in the combustion temperature up to 600 ºC, the oxidizable REEs were transformed to acid soluble forms. However, further elevations in temperature resulted in decreases in the acid soluble fraction and corresponding increases in the reducible and oxidizable forms. Additionally, combustion temperature also significantly altered the occurrence modes of other metals present in the iPhones. f) Synthetic MSWI Ash: It was found that in the absence of hydrogen peroxide, all the elements except for Si were leached to certain degrees. It is noteworthy that approximately 80% of Zn was leached with 1.2 M HCl. When hydrogen peroxide was added to the reaction system, noticeable increases in the leaching recovery of Fe, Mn, Co, Ni, and Cu were observed. The leaching recovery of Al and Si was barely affected by adding hydrogen peroxide. These results suggested that the majority of Zn in the synthetic MSWI ash existed as metal oxide, a portion of Fe, Mn, Co, Ni, and Cu existed as metal oxide, and Al and Si are associated with glasses which are difficult to leach. Additionally, the remaining Fe, Mn, Co, Ni, and Cu in the metallic form were efficiently oxidized in the presence of hydrogen peroxide. g) Pozzolanic Activity and Thermal Stability of MSWI Ash: MSWI fly ash has higher pozzolanic activity compared to the bottom ash sample, which indicates that the fly ash sample consumed more portlandite because of its smaller particle size as reactivity fundamentally relates to reaction surface area. However, after the recovery of valuable elements, the pozzolanic activity of both the valuable elements fraction and the less valuable elements-rich products decreased significantly, which means that the valuable elements recovery lowers the Ca(OH)2 consumption, thus leading to the low activity of SCM. h) Flowsheet Design for Metal Recovery from MSWI Ash: Based on the results of the comprehensive physical separation and acid leaching tests, circuits that enable the beneficiation of the valuable metals were developed. In these circuits, the valuable metals are recovered into nonferrous, ferrous, and other valuable metal concentrates, which are processed separately in the acid leaching step. The subsequent separation and purification steps are simplified due to the physical beneficiation step. In addition, the overall recovery cost is reduced since physical beneficiation is much cheaper compared with chemical processing. Using different technologies, such as selective precipitation and solvent extraction, a comprehensive hydrometallurgical circuit was designed, and compounds of Cu, Zn, Mn, Co, and Ni with a purity close to or even higher than 95% were successfully generated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Canada-US Blended Cyber-Physical Security Exercise (Final Report)

The Canada-US Blended Cyber-Physical Exercise was a successful, first of its kind, multiorganization and multi-laboratory exercise that culminated years of complex system development and planning. The project aimed to answer three driving research questions, (1) How do cyberattacks support malicious acts leading to theft or sabotage [at a nuclear site]? (2) What are aspects of an effective combined cyber-physical response? (3) How to evaluate effectiveness of that response? Which derived the following primary objectives, 1. The May 2023 Cyber-Physical Exercise shall present a cyber-attack scenario that supports malicious acts leading to theft or sabotage. 2. The May 2023 Cyber-Physical Exercise shall define aspects of an effective combined cyber-physical response. 3. Analysis of the May 2023 Cyber-Physical Exercise shall evaluate the effectiveness of the incident response against pre-established exercise evaluation criteria. 4. Analysis of the May 2023 Cyber-Physical Exercise shall assess the effectiveness of the evaluation criteria itself. 5. Exercises shall be performed in a real-life environment. The team believes these objectives were met, and the evidence will be presented in this report. Due to the novelty of the exercise, there were several lessons learned that will be presented in this report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Assessment of Current MACCS Capabilities for Modeling Atmospheric Physical and Chemical Transformations

The physical and chemical transformation during atmospheric transport of radionuclides released into the environment has the possibility of impacting consequence modeling results. Accordingly, this report identifies physical and chemical transformations that may occur following release of chemically reactive radioactive species, how those transformations may affect modeling of consequences of release to the atmosphere and identifies current capabilities – in both MACCS and other state-of-practice atmospheric transport and dispersions models– to model those transformations. It was found that the inclusion of physical and chemical transformations is currently very limited in current state-of-practice codes for atmospheric dispersion of radionuclides. State-of-practice atmospheric dispersion codes appear to be typically limited to simulating either physical-chemical transformations or radioactive transformations, but not both. A state-of-practice atmospheric dispersion code capable of performing parallel physical, chemical, and radioactive transformation was not identified. A few atmospheric dispersion codes capable of modeling physical and chemical transport of specific species such as tritium or uranium hexafluoride were identified. Consequently, there is currently no information available that clearly suggests updates to the MACCS code are needed to bring it up to state-of-practice. However, investigations concluded that the MACCS computational framework can currently accommodate multiple physical/chemical forms in one simulation. Additionally, with some major assumptions, the computational framework in MACCS can accommodate parallel physical-chemical and radioactive transformations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗