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DOE OSTI2025

Time‐series multi‐omics analysis of micronutrient stress in Sorghum bicolor reveals iron and zinc crosstalk and regulatory network conservation

Micronutrient stress impacts growth, biomass production, and grain yield in crops. Multi-omics studies are valuable resources in identifying genes for functional studies and trait improvement, such as accumulation of Fe or Zn under deficient or excess conditions for bioenergy or grain agriculture. We conducted transcriptomics and ionomics analyses on Sorghum bicolor BTx623, grown under Fe and Zn limited and excess conditions over a 21-day period. To identify early and late transcriptional response in roots and leaves, 180 RNAseq libraries were sequenced for differential expression and co-expression network analyses. Fe and Zn accumulation was measured using ICP-MS at each time point, and a fluorometer was used to estimate chlorophyll content in leaves. Among the four treatments, Fe limitation and Zn excess resulted in the largest phenotypic effects and transcriptional response in roots and leaves. Several of the reduction (Strategy I) and chelation (Strategy II) strategy genes that improve bioavailability of Fe and Zn in plant roots often used by non-grass and grass species, respectively, were differentially expressed. Gene regulatory network (GRN) analysis of roots revealed enrichment of genes from Fe limiting and Zn excess which strongly connect to homologues of SbFIT, SbPYE, and SbBTS as hub genes. The GRN for leaf responses showed homologues of SbPYE and SbBTS as hubs connecting genes for chloroplast biosynthesis, Fe-S cluster assembly, photosynthesis, and ROS scavenging. Expression analyses suggest sorghum uses Strategy II genes for Fe and Zn uptake, as expected, but can also utilize Strategy I genes, which may be advantageous in variable moisture environments. We found strong overlap between Fe and Zn responsive GRNs, indicative of micronutrient crosstalk. We also found conservation of root and leaf GRNs, and known homologous genes suggest strong constraints on homeostasis networks in plants. These data will provide a resource for functional genetics to enhance micronutrient transport in sorghum, and opportunities to conduct further comparative GRN analysis across diverse crops species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A Data Processing Pipeline for Socio-Technical Network Analysis [Slides]

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.

critical clearing time↗

DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗

DOE OSTI2020

The impact of the third O2 addition reaction network on ignition delay times of neo-pentane.

Abstract not provided.

Hansen, Nils NMN↗

DOE OSTI2024

Lessons Learned for Responsible Use of Cloud in the Cirrus Project, Following the CrowdStrike Outage Event

A disruption in CrowdStrike’s Falcon cybersecurity platform on July 19th, 2024, caused worldwide chaos. This event highlights the imperative need for cloud security measures for networks that are critically reliant on cloud technology. This incident negatively impacted air travel, government networks, and critical infrastructure sectors such as hospitals and financial institutions. While no electric utilities had a physical impact, and few had an IT impact, there were issues created by loss of cloud services, and other interrelated industries. For utilities and energy distribution organizations, understanding and mitigating these risks is essential. The Cirrus tool offers a strategic solution engineered to weave cloud integration seamlessly into the fabric of operational management, thereby enhancing resilience and streamlining efficiency in the face of digital challenges.

25 ENERGY STORAGE↗

DOE OSTI2022

Effect of network connectivity on behavior of synthetic Broborg hillfort glasses

There is wide industrial interest in developing robust models of long-term (>100 years) glass durability. Archeological glass analogs, glasses of similar composition, and alteration conditions to those being tested for durability can be used to evaluate and inform such models. Two such analog glasses from a 1500-year-old vitrified hillfort near Uppsala, Sweden have previously been identified as potential analogs for low concentration Fe-bearing aluminosilicate nuclear waste glasses. However, open questions remain regarding the melting environment from which these historic glasses were formed and the effect of these conditions on their chemical durability. A key factor to answering the previous melting and durability questions is the redox state of Fe in the starting and final materials. Past work has shown that the melting conditions of a glass-forming melt may influence the redox ratio value (Fe +3 /ΣFe), a measure of a glass's redox state, and both melting conditions and the redox ratio may influence the glass alteration behavior. Synthetic analogs of the hillfort glasses have been produced using either fully oxidized or reduced Fe precursors to address this question. In this study, the melting behavior, glass transition temperature, oxidation state, network structure, and chemical durability of these synthesized glass analogs is presented. Resulting data suggests that the degree of network connectivity as impacted by the oxidation state of iron impacted the behavior of the glass-forming melt but in this case does not affect the chemical durability of the final glass. Glasses with a lower degree of melt connectivity were found to have a lower viscosity, resulting in a lower glass transition temperature and softening temperature, as well as in a lower temperature of foam onset and temperature of foam maximum. This lower degree of network connectivity most likely played a more significant role in accelerating the conversion of batch chemicals into glass than the presence of water vapor in the furnace's atmosphere. Future work will focus on using the results from this work with outcomes from other aspects of this project to evaluate long-term glass alteration models.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Effect of Fatty Acid Polyunsaturation on Synthesis and Properties of Emulsion Polymers Based on Plant Oil-Based Acrylic Monomers

This study demonstrated that polymerization behavior of plant oil-based acrylic monomers (POBMs) synthesized in one-step transesterification reaction from naturally rich in oleic acid olive, canola, and high-oleic soybean oils is associated with a varying mass fraction of polyunsaturated fatty acid fragments (linoleic (C18:2) and linolenic (C18:3) acid esters) in plant oil. Using miniemulsion polymerization, a range of stable copolymer latexes was synthesized from 60 wt.% of each POBM and styrene to determine the impact of POBM chemical composition (polyunsaturation) on thermal and mechanical properties of the resulted polymeric materials. The unique composition of each plant oil serves as an experimental tool to determine the effect of polyunsaturated fatty acid fragments on POBM polymerization behavior and thermomechanical properties of crosslinked films made from POBM-based latexes. The obtained results show that increasing polyunsaturation in the copolymers results in an enhanced crosslink density of the latex polymer network which essentially impacts the mechanical properties of the films (both Young’s modulus and toughness). Maximum toughness was observed for crosslinked latex films made from 50 wt.% of each POBM in the monomer feed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Optimal Network Topology for Node-Breaker Representations With AC Power Flow Constraints

It has been demonstrated that network topology optimization (NTO) may change the topology of power system networks, and consequently, provide additional flexibility to reduce network congestion and violations. Most NTO problems are formulated based on the bus-branch model in which it is challenging to represent a realistic picture of all substation configurations. In this paper, we explore advantages of substation reconfiguration modeling based on node-breaker representations for NTO problem with full nonlinear alternating current power flow. It also proposes a tailored solution algorithm to solve this nonconvex mixed-integer nonlinear programming through the outer approximation method. The proposed solution approach iterates between a mixed-integer linear programming and a nonlinear subproblem. Additional enhancements to further accelerate the iteration process are illustrated. Numerical case studies demonstrate the relative economic and operational impact of optimal network topology with node-breaker representations.

42 ENGINEERING↗

DOE OSTI2023

Datasets used in manuscript, 'High-Throughput Chromosomal Confirmation Capture (Hi-C) Metagenome Sequencing Reveals Moisture Impact on Soil Phage-Host Interactions'

Soil moisture shifts have largely unknown impacts on soil virus-host interactions. Here, we applied high-throughput chromosomal confirmation capture (Hi-C) metagenomics to link phage with their hosts in soils under wet and dry conditions. Bulk metagenomes and metatranscriptomes were analyzed from the same soil incubations. Host-associated phage diversity and the number of viruses per host increased following soil desiccation. Under wet conditions, the viral enrichment and host abundances were significantly negatively correlated, but the transcriptional activities of the phage were higher. Together, these results suggest that there was a general transition of phage from lytic to lysogenic during drying and that viral infection was higher under dry conditions. Soil desiccation also caused shifts in phage hosts and some of these were central in microbial co-occurrence networks, highlighting the impact of soil phages on microbiome structure. This study provides the first empirical evidence of phage-mediated bacterial dynamics during soil desiccation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Transient cycling of nitrogen, organic carbon and oxygen within the free-flowing Columbia River corridor: Linking exposure time dependent biogeochemical reactions to river stage fluctuations (Final project report)

The objective of this project was to develop next-generation techniques for representing the transformations of complex reaction networks like those impacting transient river-corridors, then apply them to develop upscaling tools. The project contributed significantly to the development of Lagrangian “mass transfer particle tracking” (MTPT) tools that are the first numerical framework that explicitly separates mixing and spreading processes; a crucial distinction for accurate representations of reaction rates across scales. The MTPT approach uses an operator splitting scheme where physical transport processes are simulated using classical random walk methods and each particle is treated as a “container” that carries with it any number of chemical species. Mixing between particles is simulated using a colocation-probability based mass transfer kernel and reactions are evaluated on each particle after mixing. The method is stable, accurate, and also allows the explicit modeling of residence time distributions of the individual containers of mass. The MTPT scheme allowed us to explore several research questions related to reactive transport in river corridors and complex reaction networks. We found that it is possible to quantify the exposure time distributions (ETDs) of reactants and that this does suggest some pathways to upscaling. However, comparable approximations can be obtained using simplified (reduced dimensionality) MTPT simulations in less time with similar uncertainty, so development of extensive ETD-based methods was not productive. The major findings are that i) the MTPT schemes are robust and highly accurate across a range of arbitrarily complex reactions, ii) the methods can be efficiently parallelized and the parallel performance characteristics of MTPT are predictable, and iii) the MTPT tools allow simultaneous tracking of residence time. The development of these research tools into efficient software packages continues. The methods are already available to the community because open-source, working examples have been included with all publications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

EVI-Equity

EVI-Equity (Electric Vehicle Infrastructure for Equity) is a $200k project, started around in June of 2021, with a funding from the Vehicle Technologies Office (VTO). The motivation was to create a new analytical capability that can enable us to quantify and investigate equitable access to and distribution of existing and future deployment of PEVs and EVSEs in neighborhoods, cities, states, and the nation. EVI-Equity is a bottom-up equity-focused analysis model, built upon individual (synthetic) households, aggregated by census block groups. It consists of four core components - community engagement, environmental profiling, household expenditures, and network design. Although there are some commonalities, EVI-Equity is not a vehicle choice model, charging simulation model, or transportation demand model. EVI-Equity is rather a cross-cutting analysis tool, dedicated for evaluating equitable EV adoption and EVSE deployment, encompassing and bridging a wide variety of related tools, models, and frameworks. Some of the results indicate the importance of used vehicle market for low-income households. The presentation also highlights similarities and variations as to preferred public charging locations. For example, regardless of household income, retail spots are the most preferred location for public charging, followed by curbside/street. However, the results also imply that the importance of workplace charging may vary with income - the lower the income, the less important. Environmental profiling results, with an example of ground-level ozone in Atlanta area, show that the contrast between the haves and the have nots of plug-in electric vehicles depends on location. The assessment of household expenditures illustrates the economic impact of home charging access on an individual household level - the lower the income, the greater the impact is. Lastly, Denver metro area and the state of South Dakota are used to showcase the impact of different network design of charging infrastructure, as well as alternative (vs. baseline/existing) electric vehicle adoption pattern.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2024

Quartz Dissolution Effects on Flow Channelization and Transport Behavior in Three‐Dimensional Fracture Networks

We perform a set of reactive transport simulations in three-dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo-structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi-steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media.

Hyman, Jeffrey D.↗

DOE OSTI2022

Dispersion of modified fumed silica in elastomeric nanocomposites

In polymer nanocomposites, surface modification of silica aggregates can shield Coulombic interactions that inhibit agglomeration and formation of a network of agglomerates. Surface modification is usually achieved with silane coupling agents although carbon-coating during pyrolytic silica production is also possible. Pyrogenic silica with varying surface carbon contents were dispersed in styrene-butadiene (SBR) rubber to explore the impact on hierarchical dispersion, the emergence of meso-scale structures, and the rheological response. Pristine pyrogenic silica aggregates at concentrations above a critical value (related to the Debye screening length) display correlated meso-scale structures and poor filler network formation in rubber nanocomposites due to the presence of silanol groups on the surface. Here, in the present study, flame synthesized silica with sufficient surface carbon monolayers can mitigate the charge repulsion thereby impacting network structural emergence. The impact of the surface carbon on the van der Waals enthalpic attraction, a * , is determined. The van der Waals model for polymer nanocomposites is drawn through an analogy between thermal energy, k B T, and the accumulated strain, γ. The rheological response of the emergent meso-scale structures depends on the surface density of both carbon and silanol groups.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Integrated U.S. nationwide corridor charging infrastructure planning for mass electrification of inter-city trips

This study introduces an integrated modeling framework to evaluate long-term national corridor charging infrastructure requirements in the United States to support the growing inter-city charging demand with the rapid growth in the battery electric vehicle (BEV) market. The core model is an optimization model that considers spatial and temporal dimensions and models heterogeneous behaviors between travelers. The model also introduces the travelers’ inconvenience cost function by linking travelers’ acceptance of the charging infrastructure with exogenous technology and social factors. The inconvenience cost function simulates mode choice between BEVs and alternative modes by heterogenous travelers. We applied the framework to assess the inter-regional charging infrastructure requirements for the entire U.S. mainland interstate highway network. We evaluated impacts on the infrastructure design and its public acceptance with changes in policy, technology, and demographic characteristics, and we also quantified the importance of modeling full-scale inter-regional charging infrastructure requirements compared to the conventional regional level analyses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Optimizing dynamic wireless charging for electric buses: A data-driven approach to infrastructure planning

The network configuration significantly impacts the performance of dynamic wireless charging (DWC) technology for electric buses. Here, this study presents a novel approach to planning charging infrastructure for public transit using data-driven nonconvex mixed-integer optimization. Integrating DWC and charging station technologies reveals a trade-off between enroute and stationary charging times. Our framework optimizes bus frequency settings and transmitter coil arrangements to minimize operational and infrastructure costs. A case study in Chattanooga, Tennessee, demonstrates the method's effectiveness in mitigating range anxiety and reducing charging expenses. This research implies that integrating DWC technology into public transit systems can enhance the feasibility and cost-effectiveness of electric bus operations, promoting sustainable urban mobility.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Projected network performance for next-generation xenon monitoring systems

Next-generation radioxenon monitoring systems are reaching maturity and are expected to improve certain aspects of performance in verifying the absence of nuclear tests. To predict the improvement in detecting and locating nuclear releases, thousands of releases all over the globe were simulated and the global detection probability was calculated. This was done for the International Monitoring System network of noble gas samplers as it currently exists (25 certified stations), and how it would be for potential future network sizes of 39 and 79 stations. The probability of detection was calculated for releases ranging from 10 10 Bq to 10 16 Bq of 133 Xe and presented as coverage maps and global integrals for both current and next-generation monitoring systems. Similarly, the number of detecting stations and the number of detecting samples were tabulated to elucidate the possibilities for enhanced location capability. Improvements in global detection coverage are maximized at different release sizes in a way that depends on the station density. For example, for releases of 3×10 14 Bq and 39 stations, the detection probability would rise from 60% to 70% with next-generation systems, while for releases of 10 13 Bq and 79 stations, it would rise from 37% to 52%. Achieving an average of two detecting stations requires a 1015 Bq release for a 39-station network and a 10 14 Bq release for a 79-station network. The largest impact of using next-generation systems may be the confidence, failure tolerance, and location capability that arise from obtaining multiple samples associated with a single release event.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗