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DOE OSTI2025

Gravitational Photon Polarization Twist to Probe the Early Universe and the Galactic Center

It is known that gravitational wave backgrounds (GWBs) change the polarization state of photons. This letter explores the possibility of using this effect to detect GWBs. The proposed experiment features a vertically polarized laser pulse traveling through a GWB before reaching a birefringent material which separates photons by polarization. Photons that emerge the birefringent material horizontally polarized are counted as signal photons. A space-based setup of a million km (comparable to the LISA mission) with a millisecond laser pulse is capable to detect the hypothetical pulsars responsible for the excess of gamma rays from the galactic center. Varying the duration of the pulse can reveal a variety of GWBs from the early Universe as well.

Capdevilla, Rodolfo [Fermilab] (ORCID:000000020122↗

DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

The 2175 Å bump features in FeLoBAL quasars: One indicator of MW-like dust in the nuclear region of quasars

To investigate the properties of dust in the nuclear region of quasars, we explored the extinction curves of the iron low-ionisation broad absorption line (FeLoBAL) quasar SDSS J163004.29+311957.6 and its two analogues. The parametrised extinction curves indicated the Milky Way-like 2175 Å bump features in underlying extinction, which are similar to those seen in the Local Group and in a subset of high-redshift star-forming galaxies. Compared to the bump features in the Large Magellanic Cloud (LMC), the detections in this work are much closer to those in the Milky Way (MW). These bump features, as well as those in the high- and low-ionisation broad absorption line (BAL) quasars with 2175 Å bumps, are probably the counterpart of the 2175 Å bump features in the quasar environment. This type of dust grain is generally small, easily disrupted by high-energy photons, and has difficulty surviving in the radiation field of the active galactic nucleus. However, due to the presence of absorption-line outflows, the 2175 Å bump feature in quasars, which should be rare, is seen many times in BAL quasars. The shielding effect of outflow clouds allows the MW-like dust grains to be assembled or extends the survival period in the quasar nuclear region. The process and physical and chemical conditions deserve further observational study and investigation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Human Factors Considerations for Automating Microreactors

The U.S. Nuclear Regulatory Commission (NRC) has interacted with vendors pursuing the commercialization of microreactors ( <10 MW th ). It is envisioned that microreactors could be assembled and fueled in a factory and shipped to a site. Many of the sites are expected to be remote locations requiring off-grid power or in some cases military bases. However, before this new class of nuclear reactor can be fully developed and implemented by designers, an effort must be made to explore the technical issues and provide reasonable assurance to the public regarding health and safety impacts centered on various technical issues. Prior works have examined many of these issues. One issue not yet fully explored is the possible change in role of the operations and support personnel. Due to the passive safety features of microreactors and their low level of nuclear material, the microreactor facilities may automate more functions and rely on inherent safety features more than its predecessor nuclear power plants. In some instances, human operators may not be located onsite and may instead be operating or monitoring the facility from a remote location. Some designs also call for operators to supervise and control multiple microreactors from the control room. This report explores the issues around reduced staffing of microreactors, highlights the historical safety functions associated with human operators, assesses current licensing requirements for appropriateness to varying levels of personnel support, and describes a recommended regulatory approach for reviewing the impact of reduced staff to the operation of microreactors. The report evaluates these issues through an extensive literature survey and Subject Matter Expert interviews. The literature covers research topics related to Human Factors Engineering as well as regulatory guidance given by the Nuclear Regulatory Commission.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Identification of mechanisms driving heterogeneous void growth in ductile aluminum

Void growth plays a central role in ductile fracture, yet the specific mechanisms that control this remain obscure. Classical models, such as those proposed by Rice and Tracey in 1969, are able to capture average rates of void growth, but cannot capture the heterogeneity of individual void growth. Building on recent work, the present study employs laboratory-based diffraction contrast tomography and in-situ x-ray computed tomography to investigate the effect of grain structure and other microstructural factors on void growth in an Al-2219 alloy. Crystal plasticity finite element (CP-FE) modeling is used alongside experimental data to evaluate the contributions of local mechanical states, grain orientation, grain size, and neighboring microstructural features. No strong linear relationships are found with any of the considered descriptors and void growth rate. Potential complex nonlinear relationships are explored with the use of a random forest regression model, which identifies initial void volume, void aspect ratio, local normal stress state, local shear stress state, and local equivalent plastic strain (EQPS) as features that most improve void growth rate predictions. The combination of these analyses suggests that these features should be prioritized to improve models of void growth.

Diffraction contrast tomography (DCT)↗

DOE OSTI2022

Optimized Test Section for Helium Flow Loop Experiment

Phase 3 of this project uses the knowledge and techniques gained from Phase 1 and Phase 2. The first outcome of Phase 3 is a new test section which can be manufactured and tested in the Helium Flow Loop Experiment to verify the results of the optimization study. The second outcome of Phase 3 is analyzing the features of this design and determining its performance under simulated TBM conditions: heat flux, pressure, mass flow rate, one-sided and volumetric heating. This will be accomplished by first using the results from the Phase 1 analyses and then applying the optimization strategy from Phase This phase uses the STAR-CCM+ Design Manager tool to apply the optimization strategy to the Helium Flow Loop Experiment (HFLE). The optimization algorithm used by the Design Manager tool is Simultaneous Hybrid Exploration that is Robust, Progressive, and Adaptive (SHERPA search algorithm). In addition to demonstrating the optimization features, a simple parameter sweep is also included in this phase. This report details the applied single objective optimization.

42 ENGINEERING↗

DOE OSTI2023

Bio-Inspired Catalysts Featuring Earth Abundant Metals and Secondary Coordination Sphere Interactions for the Reduction of Oxyanions

The scientifically challenging problem of catalytically reducing oxyanions was explored and presented great opportunities in terms of the environment, economics and energy self-sufficiency. Oxyanions are pervasive as they are found in many areas of technology, all forms of life, in minerals, and as synthetic materials. Given their ubiquity, the contamination of inorganic oxyanions in drinking water is a national problem as 26 states and Puerto Rico have reported high concentrations of these harmful pollutants. The goals achieved by this research was to catalytically reduce oxyanions utilizing sustainable earth abundant catalysts featuring bio-inspired ligands. This worked allowed us to gain fundamental insights into what dictates the reduction/reactivity for these anions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Structure-performance relationships in lignin-based transesterification vitrimers: The role of lignin structural features

Lignin has been hailed as an ideal renewable alternative for petrochemical-based prepolymers in material synthesis for a sustainable and circular economy, due to its abundant aromatic network and high carbon content. However, the properties and performance of lignin-derived macromolecules are strongly influenced by the lignin itself. While numerous studies have explored the impact of lignin content on the thermomechanical performance of lignin-based vitrimers, literature on how the inherent structural features of lignin affect these properties is scanty. In this study, hardwood organosolv lignin was fractionated in ethyl acetate, ethanol, and acetone to obtain lignin fractions with varying structural characteristics. These fractions were then modified through carboxylation and crosslinked with epoxidized soybean oil (ESO) at a hydroxyl to epoxy group ratio of 1:1 to produce lignin-based transesterification vitrimers (LVs). The thermal properties (i.e. glass transition temperature and thermal stability), tensile strength, storage modulus, and stress relaxation behavior of the LVs were studied and carefully related to the structural features of lignin. The results revealed a positive relationship between strong hydroxyl content in modified lignin and the tensile strength (5.10–9.71 MPa), storage modulus (1099.4 – 1372.8 MPa), crosslinking density, and stress relaxation of the LVs. Additionally, both the storage modulus and tensile strength exhibited a positive relationship with the ratio of rigid linkages in modified lignin, while lignin molecular weight was found to significantly impact the thermal properties of LVs (i.e Tg and thermal stability). This study not only highlights the valorization of lignin in vitrimer synthesis but also provide insights for designing lignin-based materials with tailored properties for specific applications.

Bio-based polymer↗

DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Performance on HPC Platforms Is Possible Without C++

Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. In conclusion, we describe some of the presentations and discussion from the mini symposium in this article.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Nicotine-Inspired, De Novo-Designed SARS-CoV-2 Main Protease Inhibitors Reveal Unique Chemistry for Covalently Conjugating Both Cysteine and Histidine Residues in the Catalytic Dyad

Anecdotal reports about smokers with low SARS-CoV-2 infection rates prompted a search for nicotine and its pyrolysis products as SARS-CoV-2 main protease (M Pro ) inhibitors. From this search, 3-vinylpyridine was discovered as a weak binder for the M Pro S1 subsite and was used subsequently as a de novo starting point for covalent inhibitor design that quickly yielded a highly potent inhibitor, SR-A-174, with an IC 50 value of 60 nM. Representing a novel class of M Pro inhibitors, SR-A-174 features an N,N -diaryl-α,α-dichloroacetamide scaffold that facilitated rapid exploration of alternative covalent warheads and various N-substituents, leading to the identification of multiple inhibitors with potent antiviral activity. Eight such M Pro inhibitor structures were determined, all demonstrating covalent binding to catalytic Cys145 of M Pro . In six determined structures, binding is dominated by the covalent bond plus van der Waals contacts, which contrasts with the extensive hydrogen bond networks formed with peptidomimetic inhibitors such as nirmatrelvir. Strikingly, two N,N -diaryl-α,α-dichloroacetamide inhibitors exhibit an unprecedented dual covalent modification mode of the catalytic dyad, forming bonds to both Cys145 and His41 with a concomitant loss of both chlorides and displacing the inhibitors from the S1 subsite. This dyad-targeting reactivity suggests a novel route for bioconjugation of both cysteine and histidine.

SARS-CoV-2↗

DOE OSTI2023

Continuous symmetry breaking in a two-dimensional Rydberg array

Spontaneous symmetry breaking underlies much of our classification of phases of matter and their associated transitions. The nature of the underlying symmetry being broken determines many of the qualitative properties of the phase; this is illustrated by the case of discrete versus continuous symmetry breaking. Indeed, in contrast to the discrete case, the breaking of a continuous symmetry leads to the emergence of gapless Goldstone modes controlling, for instance, the thermodynamic stability of the ordered phase. Here, we realize a two-dimensional dipolar XY model that shows a continuous spin-rotational symmetry using a programmable Rydberg quantum simulator. We demonstrate the adiabatic preparation of correlated low-temperature states of both the XY ferromagnet and the XY antiferromagnet. In the ferromagnetic case, we characterize the presence of a long-range XY order, a feature prohibited in the absence of long-range dipolar interaction. In conclusion, our exploration of the many-body physics of XY interactions complements recent works using the Rydberg-blockade mechanism to realize Ising-type interactions showing discrete spin rotation symmetry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Holography: Quantum Mechanical Aspects of Black Holes (Final Report)

Current theories of fundamental physics are based on quantum field theory, unifying quantum mechanics and special relativity. A similar unification of quantum field theory with gravity has been elusive. String theory is a consistent model of quantum gravity and it is important to identify general lessons that can be applied to the real world. The main technique that emerged from such studies has been the gravitational path integral. During the period of this grant, the PI has explored the implication of the gravitational path integral in novel setups to uncover new features of rotating black holes in four dimensions, as well as increasing our understanding of some specific black holes in string theory.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Catalytic Difunctionalization of Cyclic Dienes: Direct Entry to Novel ROMP Monomers

We developed a catalytic platform to convert simple hydrocarbon feedstocks into valuable, tunable materials by leveraging nickel-catalyzed difunctionalization of cyclic dienes to access a novel class of cyclic alkene monomers. These monomers undergo ring-opening metathesis polymerization (ROMP) to yield sequence-controlled polymers with defined stereochemistry. Through mechanistic studies and catalyst optimization, we established a scalable, gram-level synthesis for selective diarylation, and expanded the reaction scope to include arylalkylation through rationally tuning the organoboron coupling partner. The resulting polymers were systematically studied to understand how steric, electronic, and stereochemical features influence polymerization behavior and bulk material properties. Functionalized derivatives bearing sulfonated groups were explored as proton-exchange membranes, and chemical recycling pathways were developed to recover monomers from the final materials. This work bridges small-molecule catalysis and macromolecular design, enabling access to tunable, recyclable polymers from abundant hydrocarbon starting materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Deuterium trapping mechanisms in reduced activation ferritic martensitic steels and their correlation with mechanical strengthening

Development of high-strength materials often involves introduction of additional strengthening microstructures that also serve as tritium trapping sites. Such additions in fusion material development could degrade the fuel efficiency in fusion reactors and raise radiological concerns. The contribution of individual microstructure features in hydrogen trapping must be evaluated to ensure fuel efficiency and radiological safety. This study explores the mechanistic origins of deuterium trapping in reduced-activation ferritic–martensitic steels and its correlation to mechanical strengthening. A series of model alloys and engineering steels were fabricated and subjected to different heat treatments to control deuterium trapping site density. Deuterium retention was evaluated using D 2 gas charging and thermal desorption spectroscopy, focusing on the role of grain boundary, dislocation, M 23 C 6 precipitates, and TiC precipitates. Multiscale microstructure characterization and synchrotron X-ray diffraction were performed to characterize microstructure, which was correlated to the deuterium retention property. Results show that TiC precipitates exhibit the highest deuterium trapping capacity, followed by M 23 C 6 precipitates. Dislocation and grain boundary demonstrate the lowest and similar efficiencies. Furthermore, the relationship of trapping quantity and mechanical strengthening of these microstructure features was quantified, demonstrating that TiC precipitates offer highest deuterium trapping per unit of mechanical strengthening.

Retention↗

DOE OSTI2024

Enhancement of vibrationally assisted energy transfer by proximity to exceptional points, probed by fluorescence-detected vibrational spectroscopy

Emulation of energy transfer processes in natural systems on quantum platforms can further our understanding of complex dynamics in nature. One notable example is the demonstration of vibrationally assisted energy transfer (VAET) on a trapped-ion quantum emulator, which offers insights for the energetics of light harvesting. In this work, we expand the study of VAET simulation with trapped ions to a non-Hermitian quantum system comprising a 𝒫⁢𝑇-symmetric chromophore dimer weakly coupled to a vibrational mode. We first characterize exceptional points (EPs) and non-Hermitian features of the excitation energy transfer processes in the absence of the vibration, finding a degenerate pair of second-order EPs. Exploring the non-Hermitian dynamics of the whole system including vibrations, we find that energy transfer accompanied by absorption of phonons from a vibrational mode can be significantly enhanced near such a degenerate EP. Our calculations reveal a unique spectral feature accompanying the coalescing of eigenstates and eigenenergies that provides a unique approach to probe the degenerate EP by fluorescence-detected vibrational spectroscopy. Enhancement of the VAET process near the EP is found to be due to maximal favorability of phonon absorption at the degenerate EP, enabling multiple simultaneous excitations. Our work on improving VAET processes in non-Hermitian quantum systems paves the way for leveraging non-Hermiticity in quantum dynamics related to excitation energy transfer.

Non-Hermitian systems↗