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DOE OSTI2025

Architectural Approaches for Integrating ADMS and DERMS: Challenges, Comparisons, and Real-World Use Cases

The electrical distribution landscape is rapidly transforming due to the proliferation of distributed energy resources (DERs) such as solar panels, wind turbines, battery storage systems, combined heat and power units, and electric vehicles, introducing variability and uncontrollability that traditional grid operators are ill-equipped to manage. This transformation is further accelerated by advancements in Information and Communication Technology infrastructure that connects control centers with end devices, demanding automation and a deeper understanding of new technologies by utility personnel. Advanced grid control techniques using system-level optimization, Artificial Intelligence, and Machine Learning at the enterprise level and distributed level are evolving to address these issues. There is also an opportunity to utilize the enormous data created by these new DER technologies in the grid. Advanced Distribution Management Systems (ADMS) and Distributed Energy Resource Management Systems (DERMS) are critical in addressing these challenges by automating grid operations and enhancing reliability. Given the relatively recent development of ADMS and DERMS, and the still relatively low level of ADMS and DERMS deployment in the industry, there is a notable deficiency in the comprehensive understanding of the challenges and benefits associated with these new technologies, especially with their complementary natures and integration architectures. This paper aims to bridge the knowledge gap in ADMS and DERMS integration, presenting three distinct integration architectures currently available, and discussing the challenges and benefits of each architecture to guide utilities, industry professionals, and researchers in optimizing grid management and decision-making processes for a resilient and efficient energy future.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Forbidden scalar dark matter and dark Higgses

As experimental searches for WIMP dark matter continue to yield null results, models beyond the WIMP paradigm have proliferated in order to elude ever improving observational constraints, among them that of sub-GeV dark matter mediated by a massive vector portal (a dark photon) associated with a new dark U(1) gauge symmetry. It has been previously noted that for a significant range of the parameter space of this class of models, the annihilation of dark matter particles into a pair of dark photons can dominate the freeze-out process even when this process is kinematically forbidden for dark matter at rest — this is known as the “forbidden dark matter” (FDM) regime. Prior studies of this regime, however, assume that any “dark Higgs” associated with breaking the dark U(1) and imparting mass to the dark photon is decoupled from the dark matter and as such plays no role in the freeze-out process. In this paper, we explore the effects of a dark Higgs on sub-GeV dark matter phenomenology in this FDM regime by considering the simplest possible construction in which there exist non-trivial dark matter-dark Higgs couplings: a model with a single complex scalar DM candidate coupled directly to the dark Higgs field. We find that for a wide range of parameter space, the dark Higgs can alter the resulting relic abundance by many orders of magnitude, and that this effect can remain significant even for a small dark matter-dark Higgs coupling constant. Considering measurements from direct detection and measurements of the CMB, we further find that points in this model’s parameter space which recreate the appropriate dark matter relic abundance suffer only mild constraints from other sources at present, but may become accessible in near-future direct detection experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

The one-loop amplitudes for Higgs + 4 partons with full mass effects

We present compact analytic formulae for the one-loop amplitudes for Higgs + 4 parton scattering, $0 \to g g g g h$, $0 \to \bar{q} q gg h$ and $0\to \bar{q} q \bar{q}^\prime q^\prime h$, mediated by a loop of massive coloured quarks. We exploit the correspondence with a theory in which a massive coloured scalar circulates in the loop to avoid a proliferation in the number of terms in the result. In addition, we use momentum twistors and high precision numerical evaluations to simplify the expressions. The analytic results in this paper, in terms of spinor products, allow construction of an efficient numerical program to calculate the amplitude.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Unveiling the Dynamics of KRAS4b on Lipid Model Membranes

Abstract Small GTPase proteins are ubiquitous and responsible for regulating several processes related to cell growth and differentiation. Mutations that stabilize their active state can lead to uncontrolled cell proliferation and cancer. Although these proteins are well characterized at the cellular scale, the molecular mechanisms governing their functions are still poorly understood. In addition, there is limited information about the regulatory function of the cell membrane which supports their activity. Thus, we have studied the dynamics and conformations of the farnesylated KRAS4b in various membrane model systems, ranging from binary fluid mixtures to heterogeneous raft mimics. Our approach combines long time-scale coarse-grained (CG) simulations and Markov state models to dissect the membrane-supported dynamics of KRAS4b. Our simulations reveal that protein dynamics is mainly modulated by the presence of anionic lipids and to some extent by the nucleotide state (activation) of the protein. In addition, our results suggest that both the farnesyl and the polybasic hypervariable region (HVR) are responsible for its preferential partitioning within the liquid-disordered (Ld) domains in membranes, potentially enhancing the formation of membrane-driven signaling platforms. Graphic Abstract

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Cohort-based pan-cancer analysis and experimental studies reveal ISG15 gene as a novel biomarker for prognosis and immunotherapy efficacy prediction

Abstract ISG15, an interferon-stimulated ubiquitin-like protein, plays a multifaceted role in tumorigenesis and immune regulation. This study comprehensively evaluates ISG15 as a prognostic biomarker and predictor of immunotherapy response through pan-cancer bioinformatics analysis and experimental validation. By integrating multiomics data from TCGA, GEO, and clinical cohorts, we found that ISG15 is significantly overexpressed in multiple cancers and generally correlates with poor prognosis. Elevated ISG15 expression is associated with increased immune checkpoint gene expression, particularly PD-L1, and immune infiltration, notably M2-like tumor-associated macrophages. Immunohistochemistry and multiplexed immunofluorescence confirmed a strong positive correlation between ISG15, PD-L1, and M2-TAM infiltration in lung and gastric cancer samples. Functional analysis at the single-cell level revealed significant associations between ISG15 and tumor proliferation, angiogenesis, and immune suppression. Immunotherapy cohort analysis demonstrated that tumors with high ISG15 expression responded favorably to PD-L1 inhibitors but exhibited resistance to CTLA-4 blockade, findings further validated in lung cancer patients receiving anti-PD-1 therapy. These results suggest that ISG15 is a promising biomarker for prognosis and immunotherapy response prediction across cancers. Its integration into clinical decision-making may enhance personalized treatment strategies, improve immunotherapy outcomes, and provide new insights into the tumor immune microenvironment, cancer progression, and potential therapeutic targets for future drug development.

Immunology↗

DOE OSTI2023

Scavenging dynamics on Guam and implications for invasive species management

Deployment of mouse carcasses laced with acetaminophen has become a common management tool to control invasive brown tree snakes (Boiga irregularis; BTS) on Guam. Additionally, anticoagulant rodenticides may be used to control invasive rats (Rattus spp.) if their populations increase due to predator release in the wake of BTS eradication. However, there has been little research examining how scavengers on Guam could be incidentally exposed to toxicants by scavenging carcasses of animals that die from these population control strategies. Furthermore, there is a limited understanding of how the proliferation of invasive species on Guam has influenced the composition of the scavenger community. We investigated these topics by examining scavenger consumption of mouse, rat, and BTS carcasses on Guam in both a coastal and upland site during the wet (May–Aug 2016) and dry season (Jan–Apr 2017). We documented carcass consumption by 9 species, which scavenged 48% of carcasses. Interactions between season, habitat, and carcass type influenced probability of scavenging, and appeared to be driven by consumption by the two main scavenger species, BTS and cane toads (Rhinella marina), both of which are invasive on Guam. Baiting programs should consider the potential for toxin exposure to land crabs (Coenobita spp., Birgus latro), native species that scavenged at every combination of carcass type, habitat, and season. Altogether, 60% of scavenging events were attributed to species considered pests that are recent introductions to Guam. Invasive species on Guam are the primary scavengers of small vertebrate carrion, suggesting a substantial role in trophic dynamics that extends beyond predation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Structural characterization and biological activity of an α-glucan from the mollusk Marcia hiantina (Lamarck, 1818)

Marcia hiantina (Mollusca, Bivalvia) (Lamarck, 1818), is an edible clam mainly distributed along the tropical coastal regions. Recent researches have demonstrated that clams can possess compounds, including polysaccharides, with a wide range of biological actions including antioxidant, immunomodulatory and antitumor activities. Here an α-glucan was isolated from M. hiantina by hot water, purified by anion exchange chromatography, and its structure was characterized by a combination of multiple nuclear magnetic resonance (NMR) methods (1D 1 H, 1 H- 1 H COSY, 1 H- 1 H TOCSY, 1 H- 1 H NOESY, 1 H- 13 C HSQC and 1 H- 13 C HSQC-NOESY spectra), gas chromatography-mass spectrometry, and high performance size exclusion chromatography (HPSEC). The analysis from NMR, monosaccharide composition, methylation analyses and HPSEC combined with multi angle light scattering (MALS) of M. hiantina-derived α-glycan confirmed a branched polysaccharide exclusively composed of glucose (Glc), mostly 4-linked in its backbone, branched occasionally at 6-positions, and having a molecular weight of ~ 570 kDa. The mollusk α-glucan was subjected to four cell-based assays: (i) viability of three cell lines (RAW264.7, HaCaT, and HT-29), (ii) activity on lipopolysaccharide (LPS)-induced prostaglandin production in RAW264.7 cells, (iii) inhibitory activities of in H 2 O 2 - and LPS-induced reactive oxygen species (ROS) production in HMC3 cells, and (iv) HaCaT cell proliferation. Furthermore, results have indicated no cytotoxicity, potent inhibition of both H 2 O 2 - and LPS-induced ROS, and potent cell proliferative activity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Estimating building occupancy: a machine learning system for day, night, and episodic events

Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Explosive Byproduct Gas Transport Through Sorptive Geomedia

Current underground nuclear explosion (UNE) detection strategies rely heavily on atmospheric noble gas sampling of radioxenon. However, discriminating nuclear weapons testing programs from civilian sources is difficult due to highly variable atmospheric radioxenon backgrounds and processes affecting subsurface transport of parent radionuclides. Here, we aim to study the transport of gases produced by subsurface explosions as novel stable signatures for underground nuclear explosion (UNE) monitoring. These gases may be produced in large quantities with distinct molecular ratios, which will be impacted by subsurface transport processes. To demonstrate how ratios of gases produced by explosions can change during transport in geomaterials, we conducted laboratory benchtop experiments on the transport of carbon dioxide (CO 2 ) and hydrogen (H 2 ) gases through variably saturated zeolitic tuff, which is abundant at the historic US testing site. We observed that zeolitic tuff sorbs substantial quantities of CO 2 while allowing H 2 to transport more freely, leading to changes in the molecular ratios of the two gases along the transport pathway. Gas uptake in the dry zeolitic tuff core was 72.3% for CO 2 , compared with 53.4% for xenon and 7.6% for H 2 . The presence of 20% water saturation disrupted the CO 2 sorption process, though to a lesser extent than observed for noble gases, with a 36.7% drop in xenon sorption compared with a 21.9% drop for CO 2 . These results represent the first observations of zeolite sorption altering explosive gas ratios during transport through geomedia relevant to nuclear proliferation monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Neutronic Performance of a Compact 10 MWe Nuclear Reactor with Low Enrichment (ThxU1−x)N Fuel

Abstract Recent interest in compact nuclear reactors for applications in space or in remote locations drives innovation in nuclear fuel design, especially non-oxide ceramic nuclear fuels. This work details neutronic modeling designed to support the development of a new nuclear fuel concept based on a mixture of thorium and uranium nitride. A Monte Carlo N-Particle Version 6.2 (MCNP-6) model of a compact 10 MWe reactor design which incorporates (Th x U 1− x )N fuel is presented. In this context, a “compact” reactor is a completely assembled reactor which may be emptied of coolant and transported by specialized commercial vehicle, deployed by a C130J aircraft, or launched into space. Core geometry, reflector barrels, and the heat exchange zones are designed to support reduction of overall reactor volume of core components while maintaining criticality with a fixed total fuel mass of 4500 kg. Dense mixed nitrides of thorium nitride (ThN) additions in uranium nitride (UN) in 5 wt.% increments between $$0.05 \le x \le 0.5$$ 0.05 ≤ x ≤ 0.5 have been considered for calculation of $$k_{\infty }$$ k ∞ and $$k_{{{\text{effective}}}}$$ k effective . ThN additions in UN results in a slight increase in the magnitude of the temperature coefficient of reactivity, which is negative by design. The isotopic distribution of the principal actinide inventory as a function of burnup, time, and initial fuel composition is presented and discussed within the context of the proliferation risk of this core design.

Parker, S. S. (ORCID:000000018597440X)↗

DOE OSTI2022

Do agrivoltaics improve public support for solar? A survey on perceptions, preferences, and priorities

Abstract Agrivoltaic systems integrate agricultural production with solar photovoltaic electricity generation. Given the proven technical, economic, and environmental co-benefits provided by agrivoltaic systems, increased proliferation is anticipated, which necessitates accounting for the nuances of community resistance to solar development on farmland and identifying pathways for mitigation. Minimizing siting conflict and addressing agricultural communities’ concerns will be key in continued deployment of agrivoltaics, as localized acceptance of solar is a critical determinant of project success. This survey study assessed if public support for solar development increases when energy and agricultural production are combined in an agrivoltaic system. Results show that 81.8% of respondents would be more likely to support solar development in their community if it integrated agricultural production. This increase in support for solar given the agrivoltaic approach highlights a development strategy that can improve local social acceptance and the deployment rate of solar. Survey respondents prefer agrivoltaic projects that a) are designed to provide economic opportunities for farmers and the local community b) are not located on public property c) do not threaten local interests and d) ensure fair distribution of economic benefits. Proactively identifying what the public perceives as opportunities and concerns related to agrivoltaic development can help improve the design, business model, and siting of systems in the U.S.

Pascaris, Alexis S. (ORCID:0000000253806927)↗

DOE OSTI2024

Advancing quantitative models of soil microbiology, ecology, and biochemistry

Rapid advances in soil biology are increasingly reflected in mathematical models. From molecular to global scales, models contribute to fundamental understanding and prediction of critical soil processes, such as carbon sequestration. Recent models explicitly incorporate microbial control over soil carbon and nutrient cycling, an approach that offers greater biological realism but raises new challenges in model parameterization and stability. Although they have proliferated at community and ecosystem scales, microbial-explicit models have yet to be incorporated into most Earth system models. Applying soil models across scales requires integration with data to support parameterization and validation using approaches such as Bayesian data assimilation. In conclusion, to catalyze future progress, we recommend breaking down barriers between modeling and empirical disciplines while broadening access to the computational infrastructure that supports soil biological modeling.

Xie, Wally↗

DOE OSTI2021

Plentiful electricity turns wholesale prices negative

In 2020, average wholesale electricity prices in the United States fell to $21/MWh, their lowest level since the beginning of the 21st century. Low natural gas prices and the proliferation of low marginal cost resources like wind and solar had already established a trend toward lower wholesale prices, and this trend was exacerbated by declining electricity demand due to the Covid-19 pandemic in 2020. Negative real-time hourly wholesale prices occurred in about 4% of all hours and wholesale market nodes across the United States, but these were not distributed evenly. Regional clusters emerged, for example, in the Permian Basin in western Texas, and in Kansas and western Oklahoma in the Southwest Power Pool (SPP), negative prices accounted for more than 25% of all hours. Negative electricity prices result either from local congestion of the transmission system leading supply to exceed demand locally or due to system-wide oversupply. Looking at the latter condition in SPP, we find that all major generator types contribute to this excess supply, because of limited ramping flexibility or self-scheduled out-of-market unit commitments. Additional monetary production incentives such as renewable energy credits or tax credits also enable negative bids; indeed, negative prices predominantly occur when demand levels are low and wind production levels are high. Frequent negative prices can inform the value of additional renewable energy investments at specific locations, the need for transmission and storage development, and opportunities load growth or adaptation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Experimental validation of the mechanistic scale-up methodology of gas–solid spouted beds using radioactive particle tracking (RPT)

The very high-temperature reactors (VHTRs) are highly ranked among candidates of Generation IV of nuclear reactors due to their high efficiency, safety, the resistance to proliferation, and reliability. The VHTRs are preferentially fueled by Tristructural-isometric (TRISO) coated fuel particles which has fuel kernels of fissile material coated by four coating layers: a porous buffer pyrolysis carbon layer (buffer PyC), an inner dense pyrolysis carbon layer (IPyC), a silicon carbide layer (SiC) and an outer dense pyrolysis carbon layer (OPyC). The heart of the operation and safety of the VHTRs significantly depends on the reliability of the coating layers of TRISO particles to retain metallic and gaseous fission products within the particles. The technique used for coating TRISO particles are gas-solids spouted beds via chemical vapor deposition (CVD). Fabrication of high-quality low-defect TRISO fuel particles fuel at larger scale spouted beds is required to support the commercialization of the VHTRs. In this work, our new developed mechanistic scale-up methodology of gas-solids spouted beds based on matching the radial profile of gas-holdup has been demonstrated and validated using radioactive particle tracking (RPT). Two spouted beds of small and larger scales were used in the study. Three sets of conditions were carried out which include the conditions of the reference case in the large scale, conditions that provide similar gas holdup radial profile to that of the reference case and conditions that provided dissimilar gas holdup radial in the small-scale spouted beds. The results confirm the validation of the scale-up methodology in terms of the dimensionless values of the spout diameter, cumulative probability distribution of the solids particles penetration into the spout, fraction of cycle time in each region of the bed, the radial profiles of the dimensionless values of the root-mean-square particle velocities and solids eddy diffusivity. Finally, the results further advance the knowledge and understanding of the gas-solids spouted beds provide deeper insight into their solids dynamics and presenting important benchmarking data for validating computational fluid dynamics codes and models. At last, procedures are established for the implementation of the new scale-up methodology.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Fractonic gauge theory of smectics

Motivated by striped correlated quantum matter, and the recently developed duality between elasticity of a two-dimensional (2D) crystal and a gauge theory, we derive a dual coupled U(1) vector gauge theory for a two-dimensional (2D) quantum smectic, where the disclination is mapped onto the fractonic charge, that we demonstrate can only move transversely to smectic layers. This smectic gauge theory dual also emerges from a gauge dual of a quantum crystal through a Higgs transition corresponding to a single flavor of its dipole condensation, an anisotropic quantum melting via dislocation proliferation. A condensation of the second flavor of dislocations a corresponds to another Higgs transition describing the smectic-to-nematic melting. We also utilize the electrostatic limit of this duality to formulate a melting of a 2D classical smectic in terms of a higher derivative sine–Gordon model, demonstrating its instability to a nematic at any nonzero temperature. Generalizing this classical duality to a 3D smectic, gives formulation of a 3D nematic-to-smectic transition in terms of an anisotropic Abelian-Higgs model.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗

DOE OSTI2023

A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces

Historically, surface complexation model (SCM) constants and distribution coefficients (K d ) have been employed to quantify mineral-based retardation effects controlling the fate of metals in subsurface geologic systems. Our recent SCM development workflow, based on the Lawrence Livermore National Laboratory Surface Complexation/Ion Exchange (L-SCIE) database, illustrated a community FAIR data approach to SCM development by predicting uranium(VI)-quartz adsorption for a large number of literature-mined data. Here, we present an alternative hybrid machine learning (ML) approach that shows promise in achieving equivalent high-quality predictions compared to traditional surface complexation models. At its core, the hybrid random forest (RF) ML approach is motivated by the proliferation of incongruent SCMs in the literature that limit their applicability in reactive transport models. Our hybrid ML approach implements PHREEQC-based aqueous speciation calculations; values from these simulations are automatically used as input features for a random forest (RF) algorithm to quantify adsorption and avoid SCM modeling constraints entirely. Named the LLNL Speciation Updated Random Forest (L-SURF) model, this hybrid approach is shown to have applicability to U(VI) sorption cases driven by both ion-exchange and surface complexation, as is shown for quartz and montmorillonite cases. The approach can be applied to reactive transport modeling and may provide an alternative to the costly development of self-consistent SCM reaction databases.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2025

Direct root contact among neighboring plants influences activity of soil extracellular enzymes

Composition and diversity of vegetation systems can influence soil microbial activity and extracellular enzyme (EE) dynamics, which are crucial for soil carbon (C) accrual and nutrient cycling. Yet, the impact of plant interactions and competition on EE activities remains a notable knowledge gap. This study examines how direct root contact and neighboring plant identity affect the activity and spatial distribution of four key soil EEs: β-glucosidase (BGlu), chitinase, acid phosphatase (AcidP), and alkaline phosphatase (AlkP). Using three-compartment rhizoboxes with switchgrass (Panicum virgatum L.) grown alongside bush clover (Lespedeza capitata Michx.), and black-eyed Susan (Rudbeckia hirta L.), we assessed enzyme activities using zymography under conditions that either allowed or restricted direct root contact by root barriers. Results show that root proliferation and species interactions significantly influenced EE activity. While BGlu and AcidP activities were strongly correlated with root biomass, AlkP activity was consistently higher in the absence of root barriers, indicating a pronounced microbial response to plant interactions via direct/close root contacts. Additionally, soil phosphorus availability modulated enzyme activity, with higher phosphatase activities in low-P soils. Furthermore, these findings highlight the importance of root-root interactions and plant species composition in shaping soil biochemical processes.

enyzme activity↗