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DOE OSTI2026

Assessment of multi-stage telescopic control rods in a pebble bed reactor. Part II: corrosion and wear performance

To support High-Temperature Gas-Cooled Reactor designs, the effects of corrosion and wear on a SS316L telescopic control rod prototype were investigated in helium at 775 °C with up to 5 ppm oxygen for durations up to 500 h. Three deployment conditions were tested to assess their impact on wear and oxide formation: severe cycles, daily cycles, and gravity-driven deployments. Here, a chromium-rich oxide layer developed on regions exposed to the helium flow, reaching up to 2 µm in the most affected areas. No carburization or decarburization was observed. Oxide breakthrough occurred mainly at grooves and contact points during severe cycling of the telescopic rod, while in the other tests it was limited to contact points. Friction forces increased due to oxide growth and thermal expansion, the latter being the dominant factor, but never enough to hinder rod deployment significantly. Coaxial misalignment of stages was identified as the main potential risk to reliable operation under normal conditions.

Control rod↗

DOE OSTI2026

Long-Term Impacts of Constrained Transmission Deployment on the Cost-Reliability Tradeoff

Traditional Resource Adequacy (RA) frameworks in the U.S. undervalue the contributions of inter-regional transmission to resource adequacy during stress periods, focusing on the availability of nameplate capacity instead. However, availability of nameplate capacity does not always translate into electricity delivery, especially during tail events. Moreover, the rapid deployment of energy-limited resources and increasing electricity demand challenge existing resource adequacy frameworks and couple regional electricity demand and availability of supply via transmission. We propose a two-stage framework that goes beyond the existing capacity-centered approaches to reveal the RA contributions of transmission. In the first stage we introduce a multi-objective optimization framework to quantify the merits of transmission expansion via Pareto Frontiers under alternative futures of no transmission investment, primary energy resources availability and demand growth. The second stage focuses on tail events and leverages the results of the first stage to characterize the risk profile of regional consumers across the U.S. under the alternative energy futures. We find that no new transmission can lead to a more expensive and less reliable national grid across scenarios, however, the impact on regional RA can vary. The probabilistic analysis reveals that transmission investments can alleviate the tail risk of consumers, however, the availability of fuel resources does not always alleviate regional tail risks. Our findings inform policymakers and utilities on the prioritization of transmission investments to mitigate the risk of widespread outages, also for tail events, and ensure reliable and affordable electricity delivery to all.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Value of Nuclear Energy to the Reliability of the North American Power System: Results for Western and Eastern Interconnections

This report documents the fulfillment of a milestone for the United States (U.S.) Department of Energy Office of Nuclear Energy Light Water Reactor Sustainability Program: completing a baseline study of regional impact of nuclear power plants and hydrogen production in maintaining grid services and power quality. Models have been comprehensively demonstrated for the Western Interconnection, or Western Electric Coordinating Council area, and in the Eastern Interconnection for scenarios representative of past extreme events (e.g., drought and heat waves). Understanding the impact on the reliability of the bulk electric system of any reduction in generation capacity from nuclear power, for any reason, is the motivation of this work. Factors that might lead to the premature or unplanned closure of nuclear plants, extended outages, or repurposing of nuclear power include: • Aging infrastructure: Many nuclear power plants in the U.S. are nearing the end of their designed operating lives. Upgrading aging infrastructure can be expensive, and some utilities may choose to retire plants rather than invest in costly upgrades. • Low wholesale electricity prices: The deregulation of the electricity market in many states has led to increased competition and driven down wholesale electricity prices, causing nuclear power operators to seek other revenue sources for their heat and power such as clean hydrogen production. • Renewables growth: The rapid growth of renewable energy sources like solar and wind power is posing a challenge to traditional generation sources like nuclear. While many see renewables as a key part of the clean energy transition, their intermittent nature requires additional grid solutions for reliable power supply. • The potential for regulatory decisions to be in conflict: In its 2021 rulemaking, EPA rule (86 FR 880), the Environmental Protection Agency (EPA) set a compliance date for the ban on processing and distribution in commerce of Decabromodiphenyl Ether (DecaBDE). Since DecaBDE is in many components, particularly wiring, of nuclear power plants which are deemed safety-related or important to safety, three plants would not have been able to restart after their 2023 spring outages, and numerous others would have faced issues in the near future. Fortunately, in this case, the EPA provided relief to the nuclear energy industry. The report provides a summary of the significant role nuclear energy plays in the United States’ power generation mix, supplying around 20% of the nation’s electricity generation, spread across 28 U.S. states. Nuclear power is reliable, mostly unaffected by weather and seasonal changes, and provides a consistent source of baseload power. In terms of capacity, nuclear power plants account for as much as 26% of balancing area power generation capacity. Nuclear power also provides a substantial contribution (e.g., 10% of the inertia in the Eastern Interconnection) of the synchronous spinning mass/inertia that buffers the rate at which frequency changes when a load and generation imbalance occurs (e.g., a large plant trips or a load is suddenly shed due to a transmission outage). This contribution is critical for maintaining grid stability during sudden changes in load or generation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

In-situ synchrotron X-ray study on microstructure and stress evolutions of electroplated copper upon self-annealing

Background: The self-annealing behavior of electroplated copper (Cu) at room temperature is gaining attention in the microelectronics industry due to its significant impact on reliability issues such as substrate warpage and electrical resistivity. Methods: In this study, in-situ analysis of the microstructure transition and stress relaxation of the electroplated copper upon self-annealing was conducted via synchrotron white X-ray nanodiffraction (beamline 21A, Taiwan Photon Source) and grazing-incidence X-ray diffraction (beamline 17B1, Taiwan Light Source). Significant Findings: Remarkable relaxations of deviatoric stress and absolute strain component along the [002] direction were closely related to the Cu grain growth and crystallographic reorientation at the early stage of selfannealing, and a complete stress/strain relaxation can be achieved with the cessation of microstructure transition. In conclusion, the in-situ synchrotron X-ray studies provided an insight into Cu self-annealing mechanism, offering valuable information for improving Cu interconnect reliability.

Cu Self-annealing↗

DOE OSTI2026

Fluorinated ionic liquids as gas chromatographic stationary phases for the separation of volatile per- and polyfluoroalkyl substances

Background Here, the production of fluorinated organic compounds in the manufacturing, semiconductor, and pharmaceutical industries has increased exponentially over the past decade. This rapid growth has created an urgent need for efficient chromatographic platforms capable of selectively separating these compounds from complex mixtures, not only to support industrial quality control and waste management practices, but also to enable reliable environmental monitoring of volatile fluorinated contaminants. Conventional GC stationary phases lack the fluorophilic interactions needed for highly fluorinated analytes. Consequently, there is a clear demand for specialized stationary phases designed to improve chromatographic retention and selectivity for these compounds. Results Three stationary phases composed of fluorinated ionic liquids (ILs) with varied extent of fluorination were prepared to study fluorophilic interactions with fluorinated/non-fluorinated probe molecules by gas chromatography (GC). IL stationary phases featuring linear and branched perfluoroalkyl moieties, as well as a branched alkyl moiety, were systematically investigated. Chromatographic performance was examined using fluorinated compounds and their hydrocarbon analogs, including CF 3 -substituted aromatics, aliphatic alcohols, fluorotelomer alcohols (FTOHs), and perfluoroalkenes. Measurements on 5 m and 20 m columns revealed that the IL possessing branched alkyl provided stronger dispersive and hydrogen bonding interactions toward non-fluorinated aromatic and long-chain alcohols, whereas the fluorinated ILs enhanced retention of highly fluorinated FTOHs and perfluorodecene. Comprehensive two-dimensional GC (GC × GC), using a nonpolar primary column coupled with secondary columns featuring cross-bonded poly(trifluoropropylmethyl siloxane) (Rtx-200 ms), the branched fluorinated IL, or the branched non-fluorinated IL, highlighted complementary selectivity with the branched fluorinated IL providing the strongest interactions with fluorinated analytes. Significance These results demonstrate that fluorinated IL stationary phases are promising alternatives to conventional polysiloxane stationary phases for improving the separation of per- and polyfluoroalkyl substances and related fluorinated compounds. By correlating IL structure with fluorophilic interactions, this work establishes design principles for GC stationary phases that enable enhanced selectivity for highly fluorinated analytes while maintaining complementary interactions with non-fluorinated compounds.

Comprehensive two-dimensional GC↗

DOE OSTI2024

Pore-Filling Induced Solid Electrolyte Failure of Ti-Doped Na 3 Zr 2 Si 2 PO 12 Characterized by Operando Synchrotron X-Ray Tomography

Solid-state batteries (SSBs), particularly those utilizing sodium metal, are emerging as a promising technology due to their potential for enhanced safety, higher energy density, and longer cycle life. NASICON (Na superionic conductor) materials, known for their robust crystalline structure and high ionic conductivity, are pivotal in the development of efficient sodium all-solid-state batteries. These materials exhibit high room-temperature ionic conductivity and electrochemical stability, making them ideal for various applications. Research has focused on improving NASICON's ionic conductivity and stability through doping, interface regulation, and composite anode design. Recent advancements include Ti-doped Na 3 Zr 2 Si 2 PO 12 (Ti-NZSP), which demonstrates improved surface stability, higher ionic conductivity, and increased critical current density. However, challenges such as Na dendrite formation and mechanical integrity under operational conditions persist. Advanced imaging techniques like operando synchrotron X-ray tomography have provided insights into failure mechanisms, revealing that pore-filling and dendrite growth are significant issues. Understanding these processes is essential for enhancing the performance and safety of Na-based SSBs. Here, this study underscores the need for continued research to address these challenges and develop reliable, high-performance solid-state electrolytes for future energy storage solutions.

25 ENERGY STORAGE↗

DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗

DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Validation of the DESI DR2 Ly$α$ forest full-shape analysis

We present the validation of the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) Lyman-$α$ (Ly$α$) forest full-shape analysis. This analysis combines three-dimensional Ly$α$ forest auto-correlations and cross-correlations with quasars to extract information from both the baryon acoustic oscillation (BAO) feature and the broadband clustering signal, with primary emphasis on the Alcock-Paczynski (AP) measurement. Compared to the DESI DR1 analysis, the DR2 validation uses substantially larger and more realistic mock datasets, including CoLoRe 2LPT and AbacusSummit Ly$α$ forest simulations. The modeling framework is also improved through analytic marginalization over small scales ($<10$$h^{-1}$Mpc) and the impact of ultraviolet background fluctuations. The validation program was completed prior to unblinding and defines quantitative requirements for the cosmological parameters of interest, which are evaluated using hundreds of mock realizations. We further test the analysis through independent fits to the auto- and cross-correlations, multiple catalog splits, and a broad suite of analysis and modeling variations applied to both mocks and blinded observational data. We find that the BAO and AP parameters satisfy all validation requirements and remain stable across all tests. In contrast, mock studies reveal a significant bias in the inferred growth-rate parameter $fσ_8$, leading us to exclude this measurement from the final analysis. The consistency across mocks, data splits, and robustness tests demonstrates that the DR2 Ly$α$ full-shape analysis provides a reliable and substantially improved broadband AP measurement over previous Ly$α$ forest studies.

Herbold, M. [Chicago U., KICP; Ohio State U.] (ORC↗

DOE OSTI2025

Modular Processing of Flare Gas for Carbon Nanoproducts

This project demonstrated the technical viability and economic promise of a modular system for converting flared natural gas into valuable carbon nanoproducts (CNPs) through catalytic chemical vapor deposition (CVD). All major project milestones were successfully completed, including reactor design and commissioning, catalyst development, process optimization, technoeconomic analysis, and application testing in concrete systems. The overarching goal was to create a scalable, field-deployable process that valorizes stranded methane by producing high-value carbon materials for use in cementitious composites. At the lab scale, the team designed and built a fluidized bed reactor optimized for use with silica fume-supported nickel catalysts synthesized via atomic layer deposition (ALD). A statistically designed sintering study enabled precise tuning of nickel nanoparticle size, identifying the influence of oxygen partial pressure, time, and temperature on catalyst morphology and performance. These insights allowed the team to target catalyst conditions that maximize carbon nanofilament growth. Subsequent CVD experiments achieved up to 31.8 wt% carbon deposition under optimized conditions, with TEM confirming the presence of nanofilament structures and sustained hydrogen evolution during reaction. Reactor upgrades and empirical fluidization studies supported the development of reliable, repeatable experimental protocols. The modular pilot-scale skid reactor was fully constructed, instrumented, and commissioned. Capable of operating at 675–800°C and pressures up to 290 psig, the system was designed for continuous operation at a carbon production rate of 1 kg/hr. Initial demonstration runs confirmed solids handling, thermal control, and system leak-tightness, although a critical reactor component (the downfeed tube) was inadvertently omitted during final assembly. This omission limited gas–solid contact and prevented meaningful carbon deposition during pilot-scale CVD runs. Nonetheless, the system operated safely under design conditions, and the root cause of performance limitations was clearly identified. Complementary work on UHPC formulations demonstrated that small additions of carbon nanoproducts, including those derived from flare gas, can significantly enhance mechanical performance while preserving workability. A comprehensive study of CNF dispersion techniques and mix design optimization led to a clear protocol for integrating these nanomaterials into concrete. Incorporation of CNPs improved flexural toughness and reduced porosity, supporting their use in high-performance infrastructure applications. A technoeconomic analysis (TEA) confirmed that this process can produce CNP-loaded catalyst material at a levelized cost below $\$$7/kg across a range of catalyst loadings and reaction yields. With estimated market values for the carbon composite product ranging from $\$$14 to over $\$$60/kg, and the ability to blend CNPs into concrete at sub-percent levels with less than 10% added cost, the system presents a compelling economic case. While additional engineering work is needed to optimize fluidization and heat transfer at scale, this project establishes a strong foundation for commercial development. The process is not only technically sound but also economically promising, representing a viable pathway for flare gas mitigation through modular carbon nanomaterial production.

03 NATURAL GAS↗

DOE OSTI2025

TASTI-GRID: An Overview of West Virginia’s Grid Resilience Challenges

West Virginia’s legacy as a top power producer with a legacy of mining and natural gas is well-known across the country. A state with historic underinvestment, vulnerability to natural hazards, and a large rural population – much of West Virginia’s critical energy infrastructure is beyond its recommended lifecycle at 75+ years old. West Virginian’s are feeling the effects of neighboring Virginia’s data center growth, as its grid interconnectedness with neighboring states have impacted energy affordability as power supply has not kept up with demand. The state aims to attract data centers to improve economic development, however, the age of infrastructure, utility reliability, weather-induced major outages, and cost of proposed improvements compound West Virginia’s resilience challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Cosmological constraints from the cross-correlation of DESI Luminous Red Galaxies with CMB lensing from Planck PR4 and ACT DR6

Here, we infer the growth of large scale structure over the redshift range 0.4 ≲ z ≲ 1 from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latest Planck and ACT data. We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. We perform an extensive set of bandpower- and parameter-level systematics checks to ensure the robustness of our results and to characterize the uniformity of the LRG sample. We demonstrate that our results are stable to a wide range of modeling assumptions, finding excellent agreement with a linear theory analysis performed on a restricted range of scales. From a tomographic analysis of the four LRG photometric redshift bins we find that the rate of structure growth is consistent with ΛCDM with an overall amplitude that is ≃ 5-7% lower than predicted by primary CMB measurements with modest (∼ 2σ) statistical significance. From the combined analysis of all four bins and their cross-correlations with Planck we obtain S 8 = 0.765 ± 0.023, which is less discrepant with primary CMB measurements than previous DESI LRG cross Planck CMB lensing results. From the cross-correlation with ACT we obtain S 8 = 0.790 +0.024 -0.027 , while when jointly analyzing Planck and ACT we find S 8 = 0.775 +0.019 -0.022 from our data alone and σ 8 = 0.772 +0.020 -0.023 with the addition of BAO data. These constraints are consistent with the latest Planck primary CMB analyses at the ≃ 1.6-2.2σ level, and are in excellent agreement with galaxy lensing surveys.

cosmological parameters from LSS↗

DOE OSTI2025

Biofouling and Corrosion Study for a Novel Linear Guided Wave Energy Converter (Post Access Report)

The overall objective of this project was to examine the reliability and performance of antibiofouling coatings used for a wave energy converter (WEC) developed by E-Wave Technologies. The particular coatings were selected for their low toxicity and potential compatibility with aquaculture. The aim of this work was to 1) test coating solutions to prevent biofouling growth and saltwater corrosion on the static (paddle and attachment frame surface) components of the WEC that are submerged, 2) determine adhesion of the coatings to system components, and 3) assess the ease and effectiveness of biofouling cleaning to insure long term performance of the system. An analysis of commercial coatings was performed using methods to examine the prevention of biofouling and coating adhesion properties on two key materials of the WEC, which were 316L low carbon marine grade stainless steel (SS) and Ultra High Molecular Weight Polyethylene (PE). Three marine antifouling paints were selected based on their unique properties to test how different paint styles perform on different materials. The selected paints were ePaint Ecominder self-polishing paint with Zinc Omadine for slime control, Pettit ECO HRT Copper-Free ablative antifouling with Econea biocide, and Intersleek 1100SR foul release. Pacific Northwest National Laboratory (PNNL) prepared PE and SS substrates coated with the three paints and compared the performance against uncoated substrates when submerged in raw seawater for 3-, 6-, and 9-month (m) time periods. Results in adhesion testing indicated that Pettit and ePaint materials clearly bonded strongly to SS, but did not bond comparably well to PE. It was noted during adhesion testing that the Intersleek surfaces were especially difficult to test as the paint highly resists bonding to the epoxy adhesives used with the adherence testing platform. The wear rate of the coatings was not measured under this study; however, based on adhesion testing, coatings in the sliding regions of the Ewave device are expected to wear rapidly. Sandia National Laboratories (SNL) evaluated the adhesion of three different paints to PE and SS substrates which were exposed to a marine environment for time intervals of 0, 3, 6, and 9 months. From qualitative visual analysis of the 3 in2 coupons when pulled from the tank, the 3 in2 coupons generally only appeared to have biofouling consisting of filamentous algae or diatoms, which all have relatively low mass and can be easily wiped from the surface of coupons. Qualitative visual analysis indicated that ECO HRT and Unpainted were consistently worse than Intersleek and Ecominder at all time points. Results provide insight to aid with down-selection of commercial coatings under static conditions to support reliability of the WEC and potential maintenance schedules. This investigation was conducted using small coupon samples suspended in seawater and the development of testing rigs for dynamic component level testing is needed for future work. In addition, the potential toxicity of these commercial coatings on aquaculture has not been determined by this study. One recommendation is to conduct toxicity investigations at the Environmental Toxicity Laboratory at Oak Ridge National Laboratory.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Water use of co‐occurring loblolly ( Pinus taeda ) and shortleaf ( Pinus echinata ) in a loblolly pine plantation in the Piedmont

Abstract Measuring water use in co‐occurring loblolly pine (Pinus taedaL.) and shortleaf pine (Pinus echinataMill.) enhances our understanding of their competitive water use and aids in refining watershed water budget model parameters. This study was conducted in a 12‐ha forested headwater catchment in the Piedmont of North Carolina, southeastern U.S., from 2018 to 2019 (pre‐thinning) to 2020 (post‐thinning). Sap flux density (J s ), species‐level transpiration (T s ), and watershed‐level transpiration (T w ) were quantified. Water use efficiency (WUE) in loblolly and shortleaf pines was compared, alongside an investigation into how both species'J s andT s responded to atmospheric vapor pressure deficit (VPD). Loblolly pine had 19%–36% higherJ s than shortleaf pine. DailyT s for loblolly pine ranged from 15.0 to 29.0 L/day whileT s in shortleaf pine ranged from 3.0 to 6.8 L/day. TheT s was significantly higher in loblolly pine when compared to shortleaf pine likely due to higher canopy position and higher growth rates of the former. WUE, defined by annual tree biomass growth per tree water use, was not significantly different between the two. DailyJ s andT s in both species responded nonlinearly to VPD, with loblolly pine being more sensitive and variable. Species‐specific water use should be considered when quantifyingT w and developing reliable models to predict the effects of forest management practices on water resources.

Engineering↗

DOE OSTI2025

Using leaf and stomatal traits to predict biomass production and water use efficiency in Populus

Climate change is reshaping ecosystems, driving plants to adapt through leaf-trait plasticity that reflects strategies for growth and water use. Predicting biomass production and intrinsic water use efficiency (iWUE) remains challenging because of genetic, taxonomic, and environmental variability. Here, we used eastern cottonwood and Populus hybrids as a model system to test whether easily measurable leaf traits can serve as reliable predictors of performance, and whether adding stomatal and biochemical traits improves predictive power. Across two field sites in Mississippi, leaf mass per area (LMA), biomass production, iWUE, leaf area, and foliar nitrogen ( N %) differed significantly among taxa and sites, while other traits were conserved. Factorial analysis of mixed data (FAMD) revealed distinct clustering of taxa and sites, indicating coordinated variation among leaf and stomatal traits. Pairwise correlations highlighted fundamental trade-offs, with biomass positively related to LMA and petiole length but negatively associated with iWUE, N %, and carbon isotopic ratios (δ 13 C). Leaf temperature and leaf angle varied among taxa and were significantly correlated with LMA and petiole length, suggesting mechanisms of heat dissipation and leaf movability that link simple traits to gas exchange and productivity. Weighted multiple linear regression models explained 80%–91% of variation in biomass production and iWUE. Models using only LMA, petiole length, and stomatal metrics performed nearly as well as those incorporating N %, and δ 13 C, with complex traits adding approximately 10% explanatory power. These results demonstrate that simple morphological traits capture integrated functional trade-offs, while complex traits refine predictions. This tiered approach provides an efficient framework for selecting high-yielding, water-efficient genotypes of Populus and other hardwood species, offering practical pathways to enhance carbon uptake and iWUE under climate change.

biomass production↗

DOE OSTI2025

Congestion Management Solutions for Enhanced Distribution System Operations with Aggregated Distribution Grid Resources Providing Grid Services and Market Participation

Microgrids and other aggregations of distribution grid resources (DGRs) are poised to actively participate in electricity markets and provide essential grid services in the coming years. In fact, DGRs already play such a role through behind-the-meter (BTM) demand response programs and small-scale BTM dispatchable generation initiatives. At the same time, the rapid growth of artificial intelligence (AI) and cryptocurrency datacenters imposes significant, often unpredictable, demands on the power distribution system. Aggregated DGRs can serve as flexible resources that help mitigate these pressures by using available transmission and distribution capacity more efficiently, supporting resource adequacy and other reliability services, and providing bridge strategies while long-term transmission infrastructure is being developed. The impacts this activity will have on distribution networks are not fully understood and could present significant challenges for distribution utilities due to capacity constraints and the need for congestion management. Technical issues include reverse power flow, variability and possible degradation of equipment integrity, voltage violations, and customer power quality concerns. These issues will likely intensify as electricity market operators across the United States implement Federal Energy Regulatory Commission Order 2222 over the next few years.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

The relationship between gene traits and transcription in soil microbial communities varies by environmental stimulus

Codon and nucleotide frequencies are known to relate to the rate of gene transcription, yet how these traits shape transcriptional profiles of soil microbial communities remains unclear. Here we test the prediction that functional genes with high codon optimization and energetically lower cost nucleotides (i.e., nucleotides requiring less adenosine triphosphate (ATP) for synthesis) have higher transcriptional expression in a soil microbial community. In laboratory incubations, we subjected an agricultural soil to two separate short-term environmental changes: labile carbon (glucose) addition or a sudden 30-min increase in temperature from 20 °C to 60 °C. Using the total genomic codon frequencies to predict preferred codon usage for each taxon, we then estimated codon optimization for each transcript. On the community level, we found a higher average level of codon optimization after the addition of glucose. Synonymous nucleotide composition in the transcript pool also shifted towards energetically cheaper nucleotides, favoring uracil (U) over adenine (A) and cytosine (C) over guanine (G). Similarly, we found that encoded amino acid usage shifted towards energetically cheaper amino acids in response to labile carbon. In contrast, in communities responding to heat shock, there were no significant differences in the averaged gene traits of expressed transcripts. We used metagenome-assembled-genomes to further examine the ability of gene traits to predict transcriptional responses within and between taxa. We found that traits of individual genes could not reliably predict the level of transcription of a gene within or between taxa—highlighting the limits of this approach. However, we did find that when traits were averaged across several related genes, codon optimization was able to predict levels of transcription in metabolic pathways associated with growth and nutrient uptake in response to glucose. Similar relationships were not observed in response to heat, or for functions associated with stress—such as genes associated with sporulation or heat shock. These results demonstrate that gene traits, such as codon usage, nucleotide selection, and amino acid selection, relate to the transcriptional expression of genes in soil microbial communities and suggests that these relationships may be dependent on both gene function and the specific type of environmental stimuli.

Biological and medical sciences↗

DOE OSTI2026

Chaos as the cause of randomness in tearing mode onset times in DIII-D ITER baseline scenario plasmas

The first evidence of widespread chaotic plasma dynamics relevant to the onset of disruptive tearing modes is found in DIII-D tokamak discharges. This evidence is obtained by calculating positive maximal Lyapunov exponents from Mirnov signals using the Rosenstein algorithm, implying the chaotic growth and/or rotation of magnetohydrodynamic modes in DIII-D [Luxon, Nucl. Fusion 42, 614 (2002)] plasmas. Such chaotic dynamics offer an explanation for the random tearing mode onset times previously observed in ITER baseline scenario discharges. Further, the Lyapunov timescales associated with this chaos could inhibit reliable prediction of tearing mode onset beyond the angular momentum confinement timescale; this constraint should inform the design of plasma control systems on ITER.

Chaotic dynamics↗