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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Beam Loss Assessment Through Use of Photomultiplier Tubes

Modern accelerators aim to deliver maximal beam current at stable energy with minimal beam loss. Environmental changes, among other factors, can result in increased beam loss and decreased beam throughput, prompting daily retuning of the accelerator. The compact nature of the oldest part of the linear accelerator limits the available beam instrumentation, making beam loss assessment and tuning difficult. Thus, additional devices for beam loss monitoring must be considered. Photomultiplier tube-based beam loss monitors (BLMs) were installed along the Fermilab drift tube Linac to assess beam loss. Due to noise, the data from the installed photomultiplier tubes was difficult to assess. After noise reduction and signal analysis, it was found that the signals produced by the photomultiplier tubes in response to beam loss were consistent for a given configuration and therefore a reasonable measure of beam loss. This project lays groundwork for future work in beam loss assessment using photomultiplier tubes, with the automation of the process developed in this project being the next step in this effort.

Waggoner, Alexander↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Creep ductility limiting mechanisms in an additively manufactured Al-Ce-Ni-Mn-Zr alloy

Tensile creep response and cavitation damage evolution in an additively manufactured Al-7.5Ce-4.5Ni-0.4Mn-0.7Zr (wt%) alloy with peak-aging and overaging treatments were investigated in the 300–400 ºC range. Microstructural heterogeneity and its response to heat treatment and subsequent creep deformation were studied to understand the interplay between cavity formation, creep lifetime and ductility. Increasing the applied stress activated the nucleation of more cavities, an experimental observation that is well described using the vacancy accumulation model. Cavities nucleated prematurely due to localized plasticity in the denuded zones that formed at/near melt-pool or grain boundaries. Microstructure/deformation heterogeneity with consequent evolution of stress triaxiality, especially at lower stresses, causes accelerated cavitation, thus producing low creep ductility (∼ 0.2–2.4 %), compared to (∼12–21 %) ductility of the alloy measured by regular tensile tests at equivalent temperatures. A constrained diffusional cavity growth mechanism with continuous cavity nucleation during creep is established as the dominant mechanism, implying that cavitation involves vacancy diffusion, yet its growth rate is dictated by the minimum creep rate. In conclusion, the ductility-limiting creep and cavitation mechanisms discussed here provide new insight into the creep behavior of 3D-printed metallic alloys.

Additive manufacturing↗
DOE OSTI2024

Five Year Wildfire Risk Reduction Action Plan for the Electric Power Industry

The U.S. DOE and the Electric Power Research Institute convened a wildfire advisory group to bring together knowledge of ignition risks, electric utility needs, the applicability of available technology, and existing technology gaps. The outcome is a five-year action plan, containing recommendations on RD&D projects, that if completed by the year 2030, may accelerate the power industry’s ability to substantially reduce wildfire ignition risks. When reviewing this document please consider it is a snapshot in time for the years 2023 and 2024, could be obsolete by the end of the decade. The following list comprises the titles and topics for the proposed follow-on demonstrations: 1. Hybrid Undergrounding RD&D, 2. Live Downed Conductor Detection RD&D, 3. Fault Energy Reduction RD&D, 4. Advanced Inspection and Response Drone, 5. Fault and PQ Event Signature Repository, 6. Advanced and Intelligent Sensor Nodes, 7. Fire Friendly Asset Coatings and Coverings, and 8. Environmental Monitoring Action Plan.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Radiation Belt Response to Fast Reverse Shock at Geosynchronous Orbit

Fast reverse shocks (FRSs) cause the magnetosphere to expand, by contrast to the well-known compressions caused by the impact of fast forward shocks (FFS). Usually, FFSs are more geoeffective than FRSs, and consequently the inner magnetosphere dynamic responses to both shock types can be quite different. In this study, we investigate for the first time the radiation belt response to an FRS impact using multi-satellite observations and numerical simulations. Spacecraft on the dayside observed decreases in magnetic field strength and energetic (~40–475 keV) particle fluxes. Timing analysis shows that the magnetic field signature propagated from the dayside to the nightside magnetosphere. Particles with different energies vary simultaneously at each spacecraft, implying a non-dispersive particle response to the shock. Spacecraft located at lower L-shells did not record any significant signatures. The observations indicate a local time dependence of the response associated with the shock inclination, with the clearest signatures being observed in the dusk–midnight sector. Simulations underestimate the amplitude of the magnetic field variations observed on the nightside. The observed decreases in the electron intensities result from a combination of radial gradient and adiabatic effects. The radial gradients in the spectral index appear to be the dominant contributor to the observed variations of electrons seen on the dayside (near noon and dusk) and on the nightside (near midnight). This work shows that even an FRS can affect the radiation belts significantly and provides an opportunity to understand their dynamic response to a sudden expansion of the magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Competing mechanisms at vibrated interfaces of density-contrast fluids

Fluid-fluid interfacial instability and subsequent fluid mixing are ubiquitous in nature and engineering. The hydrodynamic instability of fluid interfaces has long centered on the pressure gradient-driven long-wavelength Rayleigh-Taylor instability and the resonance-induced short-wavelength Faraday instability. However, neither instability alone can explain the dynamics when both mechanisms are present. We identify a previously unseen multi-modal instability emerging from their coexistence. When the denser fluid is polydimethylsiloxane, the mixed region at a high density contrast (Atwood number = 0.9) spans a vibration amplitude range approximately twice the gravitational acceleration. Using Floquet stability analysis, we show how vibrations govern transitions between the RT and Faraday instabilities, leading to contention between these instabilities rather than resonant enhancement. Here, the initial transient growth is represented by the exponential modal growth of the most unstable Floquet exponent, along with its accompanying periodic behavior. Direct numerical simulations validate these findings and track interface breakup into the multiscale and nonlinear regimes. Specifically, we show that growing RT modes nonlinearly suppresses Faraday responses even when the initial growth rate of the Faraday instability is 3.63 times that of RT, so a bidirectional competition hinders their sustained coexistence.

Direct numerical simulations↗
DOE OSTI2022

Designing Cladded Components for High Temperature Nuclear Service: Part II—Design Rules

The challenge of using existing ASME Section III, Division 5, class A metallic materials for the construction of structural components of advanced reactors with corrosive coolants could be mitigated by allowing designers to use cladding to protect the base material from corrosion. However, the existing Section III, Division 5 rules provide no guidance on the evaluation of strain accumulation and creep-fatigue damage in cladded components. The availability of design rules for cladded components that do not require long-term clad material testing could promote the application of the cladding approach to accelerate the deployment schedule of these advanced reactor systems. To avoid long-term properties for the clad materials Part I of this work proposes two approximate design analysis methods for two types of clad materials—soft clads that creep much faster and have lower yield stress than the class A base material, and hard clads that creep much slower and have higher yield stress than the class A base material. The proposed analysis methods approximate the response of a soft clad by treating it as perfectly compliant and of a hard clad by treating it as linear elastic. Based on these approximate design analysis strategies this Part II develops a complete set of design rules for class A components cladded with either soft or hard clad materials. In conclusion, Part II discusses the reasoning behind the proposed design rules and uses example finite element analyses of representative reactor components to illustrate the use of these design methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2021

Prototype tests of the electromagnetic particle injector-2 for fast time response disruption mitigation in tokamaks

Predicting and controlling disruptions is an important and urgent issue for ITER. Some disruptions with a short warning time may be unavoidable. For these cases, a fast time response disruption mitigation method is essential. Experimental tests on a prototype system of a novel, rapid time-response disruption mitigation system being developed for tokamak-based reactors and as a backup option for ITER, referred to as the electromagnetic particle injector (EPI), have been able to verify the primary advantages of the concept. These are its ability to meet short warning time scales of <10 ms while attaining the projected high velocities for deep radiative payload penetration in reactor-scale plasmas. The EPI relies on an electromagnetic propulsion system. A metallic sabot is accelerated electromagnetically to the required velocities (>1 km s -1 ) within 2 ms, at which point it releases a radiative payload consisting of a shell pellet or well-defined microspheres. Initial experimental tests from the prototype system show attainment of over 600 m s -1 in about 1 ms. Finally, essential aspects of payload separation from the sabot and sabot capture have also been demonstrated at 200 m s -1 , and the method can be extended to over 2 km s -1 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Energy Leaders: The Catalyst for Strategic Energy Management

This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, its effective implementation requires strategic leadership and a structured approach. This research analyzes data collected from 120 participants representing 71 companies attending the Energy Bootcamp events organized by the U.S. Department of Energy’s (DOE) Better Plants program. The collected data focused on the state of SEM implementation, the presence and responsibilities of energy leaders, and the formation and function of energy teams. The findings reveal a significant gap between the perceived importance of SEM and its actual adoption, highlighting the need for strong leadership to drive behavioral changes by championing energy efficiency initiatives. Results indicate that effective energy leaders possess a diverse skill set, including the ability to secure top management buy-in, foster a culture of energy consciousness, and collaborate across departments. This study emphasizes the importance of empowering energy leaders with clearly defined roles and responsibilities as well as the authority to build and lead cross-functional energy teams. Furthermore, integrating energy management into existing organizational structures and leveraging readily available resources are identified as key factors for successful implementation. This research underscores how dedicated leadership and effective SEM practices help achieve industrial energy efficiency goals, providing practical insights for organizations seeking to improve performance and contribute to a resilient future.

energy leader↗
DOE OSTI2022

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology – A Proof-of-Concept Model Development for Remdesivir

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. Here, the disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Intricate structure of the plasma Rayleigh–Taylor instability in shock tubes

Spikes and bubbles grow on unstable interfaces that are accelerated in high-energy-density conditions. If a shock propagates ahead of the interface, the plasma can be heated to extreme conditions where conduction and radiation fluxes influence the hydrodynamics. For example, a National Ignition Facility experiment found reduced single-mode nonlinear mixed-width growth in conditions scaled from a supernova explosion. We present high-resolution two-dimensional radiation hydrodynamic simulations with the Flash code that quantitatively reproduce the experiment. Radiative fluxes are primarily responsible for ablating the spike and removing the mushroom caps. The ablated plasma increases the mixed mass and forms a low-density halo with spikes forming in both directions. This is considerably more complex than classical instability. The halo is sensitive to ablative physics, so radiographing it may aid in the verification of energy transport modeling. Although ablation changes the spike shape, it has little effect on the overall mixed width for these parameters. This is because ablation enhances the bubble velocity but it has the opposite effect on the spike. The radiation transport instead suppresses the growth via increasing the shocked foam density, thus decreasing the Atwood number. Furthermore, a terminal velocity model including the rarefaction expansion agrees with the experimental mixed-width growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Understanding PV Polymer Backsheet Degradation through X-Ray Scattering

Understanding how photovoltaic (PV) module backsheet polymers age and degrade in response to environmental stresses is important for designing polymers that can maintain their structural integrity after decades of outdoor use. X-ray scattering is a powerful technique for exploring backsheet polymer structure at the angstrom- (wide-angle, WAXS) and nanometer- (small-angle, SAXS) length-scales. We present the use of SAXS and WAXS to study pristine and aged polymer backsheets. The structural insight from these techniques can be used to compare the degradation induced by accelerated testing with the degradation seen in field-aged PV materials.

14 SOLAR ENERGY↗
DOE OSTI2022

Experimental Verification of Pulse Shaping in Elastic Metamaterials Under Impact Excitation

Here, we present experimental verification of pulse shaping in elastic metamaterials together with a procedure to design, fabricate, and verify metamaterial pulse shapers under impact excitation. The split Hopkinson pressure bar (SHPB) test, a fundamental dynamic test introduced more than 70 years ago, often incorporates pulse shaping as a means to alter a stress wave, providing the primary motivation for the presented study. Elastic metamaterials hold promise for enhancing conventional pulse shaping abilities and improving capabilities of the SHPB test. We first design the pulse shaper by numerically optimizing its response using finite element analysis. The pulse shaper consists of repeated unit cells based on a combination of a phononic crystal and a local resonator. Then, we fabricate and test pulse shaper candidates to validate the procedural efficacy. An iterative element corrects inaccuracies in input force and material properties and allows convergence on an appropriate pulse shaper. We carry out this procedure by designing pulse shapers fabricated from 3D-printed polylactic acid (PLA) to achieve an extended dwell acceleration pulse shape. In experimental impact tests, the procedure results in rise, dwell, and fall behaviors comparable to that predicted, effectively confirming the efficacy of the presented procedure and verifying the performance of metamaterial-based pulse shapers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

CESER I-Corps (Final Report)

Energy I-Corps is a U.S. Department of Energy (DOE) sponsored entrepreneurial training program for laboratory researchers aimed at accelerating the commercialization of lab-developed technologies. The program, developed and managed by DOE's Office of Technology Commercialization (OTC) in partnership with the National Laboratory of the Rockies (NLR), uses a customized curriculum built on the Lean Launch Methodology and delivers a rigorous 10- to 12-week training program to selected laboratory-based teams. The Energy I-Corps team at NLR worked with the DOE's OTC and Office of Cybersecurity, Energy Security, and Emergency Response (CESER) to develop and implement commercialization programming for awardees of the CESER Cybersecurity for Distributed Energy Resources Research, Development, and Demonstration Research Call, funded under Provision 40125(b) of the Infrastructure Investment and Jobs Act (IIJA). 40125(b) Projects were overseen by a technical monitor at the National Energy Technology Laboratory (NETL). The CESER Energy I-Corps Program is considered an Energy I-Corps "lite" or "mini" program, with a lighter lift to accommodate scheduling and funding per participant. This final report describes the CESER I-Corps program and provides recommendations on behalf of OTC to CESER to consider if CESER were to run an I-Corps program in future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Stressing the importance of plant specialized metabolites: omics-based approaches for discovering specialized metabolism in plant stress responses

Plants produce a diverse range of specialized metabolites that play pivotal roles in mediating environmental interactions and stress adaptation. These unique chemical compounds also hold significant agricultural, medicinal, and industrial values. Despite the expanding knowledge of their functions in plant stress interactions, understanding the intricate biosynthetic pathways of these natural products remains challenging due to gene and pathway redundancy, multifunctionality of proteins, and the activity of enzymes with broad substrate specificity. In the past decade, substantial progress in genomics, transcriptomics, metabolomics, and proteomics has made the exploration of plant specialized metabolism more feasible than ever before. Notably, recent advances in integrative multi-omics and computational approaches, along with other technologies, are accelerating the discovery of plant specialized metabolism. In this review, we present a summary of the recent progress in the discovery of plant stress-related specialized metabolites. Emphasis is placed on the application of advanced omics-based approaches and other techniques in studying plant stress-related specialized metabolism. Additionally, we discuss the high-throughput methods for gene functional characterization. These advances hold great promise for harnessing the potential of specialized metabolites to enhance plant stress resilience in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Low-energy calibration of SuperCDMS HVeV cryogenic silicon calorimeters using Compton steps

Cryogenic calorimeters for low-mass dark matter searches have achieved sub-eV energy resolutions, driving advances in both low-energy calibration techniques and our understanding of detector physics. The energy deposition spectrum of gamma rays scattering off target materials exhibits step-like features, known as Compton steps, near the binding energies of atomic electrons. Here, we demonstrate a successful use of Compton steps for sub-keV calibration of cryogenic silicon calorimeters, utilizing four SuperCDMS High-Voltage eV-resolution detectors operated with 0 V bias across the crystal. This new calibration at 0 V is compared with the established high-voltage calibration using optical photons. The comparison indicates that the detector response at 0 V is about 30% weaker than expected, highlighting challenges in detector response modeling for low-mass dark matter searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Inference of response functions with the help of machine-learning algorithms

Response functions are a key quantity to describe the near-equilibrium dynamics of strongly interacting many-body systems. Recent techniques that attempt to overcome the challenges of calculating these ab initio have employed expansions in terms of orthogonal polynomials. We employ a neural network prediction algorithm to reconstruct a response function 𝑆⁡(𝜔) defined over a range in frequencies 𝜔. Here, we represent the calculated response function as a truncated Chebyshev series whose coefficients can be optimized to reduce the representation error. We compare the quality of response functions obtained using coefficients calculated using a neural network (NN) algorithm with those computed using the Gaussian integral transform (GIT) method. In the regime where only a small number of terms in the Chebyshev series are retained, we find that the NN scheme outperforms the GIT method.

Kurkcuoglu, Doga Murat [Fermi National Accelerator↗