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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cyclic moisture reactivation of calcium sorbents for long duration thermochemical energy storage

The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Observed Temperature and Moisture Advection Characteristics in the Southern Great Plains

Until recently, advection profiles in the planetary boundary layer have been difficult to quantify from observations. As a result, not much is known about its basic characteristics, like magnitude and diurnal cycles, or how these differ as a function of large-scale environments. Here, to provide insight into advective characteristics, a Green’s Theorem–based method for calculating profiles of advection in the lowest 3 km from an array of ground-based thermodynamic and kinematic profiling instruments has been applied to 2 years of observations from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observing site. Since advection largely depends on synoptic-scale forcing, a self-organizing map (SOM) was used to classify days based on mean sea level pressure analyses; from that, the diurnal evolution of vertical profiles of advection for different synoptic environments was quantified. The overall mean magnitude of the potential temperature advection is approximately ±3 K h −1 , while the moisture advection is approximately ±1.5 g kg −1 h −1 , with substantial variability in the sign and magnitude of the advective tendencies at different heights and throughout the diurnal cycle. Advection magnitude is strongly connected to the strength of synoptic-scale forcing, which varies depending on the time of year. Variability in advection is larger for potential temperature than it is for moisture, and strongly forced environments, like midlatitude cyclones, feature greater variability in the advection magnitude than weakly forced or quiescent environments.

advection↗
DOE OSTI2024

Upwind Moisture Controls on Interannual Variations of Precipitation and Vegetation in China's Drylands

Dryland precipitation depends on upwind and local moisture sources via moisture recycling. How upwind moisture variations affect interannual variations of downwind precipitation and vegetation in China's drylands remains unclear. We used high-resolution moisture tracking data sets and found terrestrial moisture (93%) was the dominant moisture source for China's drylands, especially from drylands themselves (46%). In most dryland grids, we observed strong correlations between precipitation and upwind moisture sources from 2003 to 2022 (median r = 0.55), with a more significant effect in drier areas. These demonstrated the upwind moisture control on interannual variations of dryland precipitation, in which internal moisture from drylands exceeds the influence of external terrestrial sources. The upwind moisture variations, especially the recycled moisture of drylands, propagate to influence downwind vegetation greenness in precipitation-sensitive dryland areas. Our findings revealed that upwind moisture variations induced by climate or land-cover changes have important implications for water and food security in China's drylands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying Moisture Sources of Arctic Atmospheric Rivers During the Recent Historical Period

Atmospheric rivers (ARs), filaments of intense atmospheric moisture transport, play a significant role in delivering moisture poleward into the Arctic and triggering weather extremes. Although previous studies have focused on large‐scale circulations driving these events, this study investigates ARs through attributing their moisture sources using the Community Atmosphere Model version 5 (CAM5) with moisture‐tagging capability. Examining ARs in the Atlantic and Pacific sectors of the Arctic separately revealed distinct contributions from remote versus local, and ocean versus land, moisture sources. Unlike non‐AR events, Arctic ARs primarily draw moisture from their respective ocean basins in lower‐latitude regions during the cold season, and shift to land sources in the warm season. Cold‐season ARs in the Atlantic and Pacific sectors source 73.2% and 85.3% of their moisture from their respective ocean basins at lower latitudes, however warm‐season contributions decrease to 41.3% and 29.4%. In contrast, mid‐ and high‐latitude continents combined contribute 40.1% and 51.3% in the warm season. Trajectory clustering analysis shows that AR moisture sources depend on both their genesis regions and transport pathways. In recent decades, the Arctic has experienced a moistening trend in both the cold and warm seasons. Our results suggest that local and remote sources equally drive the observed cold‐season moistening, whereas remote sources predominantly drive the warm‐season moistening. A better understanding of Arctic AR moisture sources under current climate conditions provides valuable insights into their potential future changes amid the projected heterogeneous northern hemispheric warming.

Arctic↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 2. Quantitative Moisture Dynamics Estimation Model

Abstract The long‐term containment of high‐level radioactive waste in geological disposal repositories relies on Engineered Barrier Systems (EBS), with bentonite clay emerging as a candidate material due to its unique properties. Understanding moisture dynamics within bentonite buffers is crucial for EBS performance, as it directly influences the material's swelling capacity, thermal and hydraulic conductivity, mechanical properties, and long‐term evolution under complex thermal‐hydrological‐mechanical (THM) processes. This study develops an advanced Electrical Resistivity Tomography (ERT)‐based framework to quantitatively monitor moisture dynamics under THM conditions. Our framework extends the Waxman‐Smits model to incorporate the coupled effects of temperature, water content, fluid chemistry, and mechanical changes on bentonite's electrical properties. Utilizing HotBENT‐Lab data from our companion paper, which includes electrical conductivity, CT density, and thermocouple measurements, this study offers a novel methodological framework bridging different scales of the model. Our results show that the extended model can estimate water content from ERT data, capturing spatial and temporal variations in moisture distribution within bentonite columns. However, the model tends to overestimate water content compared to CT density‐derived measurements. We address this discrepancy by incorporating a simplified swelling effect model, which improves agreement between ERT and CT density‐based water content estimates. We also discuss model limitations, including simplified treatment of swelling and micropore effects, and propose a conceptual framework for transitioning from laboratory to field applications, addressing challenges such as parameter scalability, field validation methods, and integration of diverse data sources. This ERT‐based framework can potentially advance real‐world moisture monitoring of bentonite‐based EBS in nuclear waste repositories. Plain Language Summary Safely containing high‐level radioactive waste depends on barriers made from materials like bentonite clay, which is effective because it swells and seals in the waste. To ensure these barriers work well over time, it's important to understand how moisture moves through the clay. Our study developed a new method using ERT to monitor moisture levels in bentonite under conditions that mimic those in actual storage sites, including changes in temperature, water content, and mechanical stress. This study improved an existing model to better account for how these factors affect the clay, allowing us to create more accurate moisture maps. Initially, the proposed model overestimated the amount of water in the clay, but its accuracy was improved by factoring in how the clay swells when wet. This study also identified some limitations of the model and suggested ways to adapt it for use in real‐world waste storage sites. This new approach could lead to better monitoring and safety checks for nuclear waste storage systems, helping to ensure long‐term containment. Key Points This work develops an ERT‐based framework extending the Waxman‐Smits model to monitor bentonite moisture dynamics during coupled THM processes The extended model accurately estimates water content from Electrical Resistivity Tomography data, incorporating swelling effects to improve precision This work proposes a conceptual framework for transitioning from laboratory to field applications, advancing EBS monitoring in nuclear waste repositories

Chen, Hang↗
DOE OSTI2025

Regional and Temporal Variability of Atmospheric River Seasonality: Influences of Detection Algorithms and Moisture Transport Dynamics

Abstract Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. While ARs were thought to peak in winter, recent research shows they exhibit region‐specific seasonality and are heavily influenced by the chosen detection algorithm. This study examines the link between the year‐to‐year consistency of peak‐AR activity to the presence of a dominant seasonal pattern, considering both location and algorithm choice. Regions are categorized by their temporal characteristics: consistent patterns (e.g., East Asia), patterns with occasional outliers (e.g., British Columbia coast), and regions lacking a clear dominant peak season (e.g., South Atlantic, parts of Australia). Hence, not all regions display a consistent seasonal cycle of AR activity. This study quantifies the extent to which a region experiences a dominant peak season of AR activity (or lacks one) and offers insights to enhance decision‐making in water management, natural hazard preparedness, and forecasting. Furthermore, given our finding that detection algorithms influence the peak season of AR activity, we also examine two diagnostic variables representative of moisture transport to corroborate our results. Integrated vapor transport, which captures meridional and zonal moisture transport, and Moist Wave Activity, representing moisture intrusions from lower to higher latitudes, are examined. Our analysis indicates that inconsistencies in the seasonal cycle of AR activity are not solely due to discrepancies in detection algorithms but also arise from changes in moisture transport. Plain Language Summary Atmospheric rivers (ARs) are critical weather phenomena that can cause extreme events like heavy rainfall and flooding. Understanding when and where ARs are most likely to occur throughout the year is essential for preparing and responding to these events. Traditionally, ARs were thought to peak in winter, but recent studies show this varies by region. Our study helps address the challenge decision‐makers face in anticipating and preparing for AR events by providing insights into the consistency of peak seasonal patterns across different areas. Some regions, like East Asia, and the British Columbia coast, show a consistent peak season, while others, like the South Atlantic and parts of Australia, have significant year‐to‐year variations, making it hard to identify a dominant season. To better understand these changes over time, the study also examines how moisture moves in the atmosphere, using Integrated Vapor Transport (which looks at moisture movement in various directions) and Moist Wave Activity (which tracks moisture shifts from lower to higher latitudes). The findings suggest that inconsistencies in AR patterns are due not only to detection methods but also due to changes in moisture transport. Key Points The peak season of atmospheric river activity can change depending on the year in some areas Interannual variations in the peak season can make identifying a dominant season challenging for some regions Frequent shifts in peak season across years reflect inconsistencies tied to detection algorithms and to underlying dynamics

Kamnani, Diya↗
DOE OSTI2025

Impact of moisture on microbial decomposition phenotypes and enzyme dynamics

Soil organic matter decomposition is a complex process reflecting microbial composition and environmental conditions. Moisture can modulate the connectivity and interactions of microbes. Due to heterogeneity, a deeper understanding of the influence of soil moisture on the dynamics of organic matter decomposition and resultant phenotypes remains a challenge. Soils from a long-term field experiment exposed to high and low moisture treatments were incubated in the laboratory to investigate organic matter decomposition using chitin as a model substrate. By combining enzymatic assays, biomass measurements, and microbial enrichment via activity-based probes, we determined the microbial functional response to chitin amendments and field moisture treatments at both the community and cell scales. Chitinolytic activities showed significant responses to the amendment of chitin, independent of differences in field moisture treatments. However, for other measurements of carbon metabolism and cellular functions, soils from high moisture field treatments had greater potential enzyme activity than soils from low moisture field treatments. A cell tagging approach was used to enrich and quantify bacterial taxa that are actively producing chitin-degrading enzymes. By integrating organism, community, and soil core measurements we show that (i) a small subset of taxa compose the majority (>50%) of chitinase production despite broad functional redundancy, (ii) the identity of key chitin degraders varies with moisture level, and (iii) extracellular enzymes that are not cell-associated account for most potential chitinase activity measured in field soil.

activity-based probes↗
DOE OSTI2024

Volumetric Soil Moisture Measurements at the Teller 27 Site, Seward Peninsula, Alaska, 2022-2023

The Teller 27 watershed on the Seward Peninsula, Alaska, has been well characterized by the NGEE Arctic project. The study site is underlain by discontinuous permafrost that is thawing as the climate warms. As a result, the site is experiencing a short-term wetting trend as a perched water table above the remaining permafrost provides plant available water during the growing season. Soil moisture patterns drive microbial activity and plant species compositions including plant density and height. This study aimed to understand how soil moisture patterns were influenced by tundra microtopography. To accomplish this, we placed a strategic network of soil moisture sensors in micro-highs, micro-lows, and control areas under different vegetation types within the Teller 27 watershed from summer of 2022 through fall of 2023. This data was used in conjunction with other soil data from the Teller 27 watershed to gain a more comprehensive understanding of soil moisture patterns in a rapidly thawing discontinuous permafrost region. This dataset includes six *.csv files: four of time series soil moisture data, one of field soil moisture data, and one of site conditions. The dataset also includes one *.kml file of the watershed and the soil moisture sensor sites as well as this user guide to provide details on data collection and processing methods. NGEE Arctic Project Summary The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Moisture Performance of Unvented Attics With Vapor Diffusion Ports and Buried Ducts in Hot, Humid Climates

Energy efficiency measures, such as cool roofs, radiant barriers, interior radiative control coatings, and buried ducts are increasing in popularity and are promoted by energy codes because of their energy-saving potential. However, these strategies can also pose moisture risks in attics by lowering surface temperatures and increasing condensation potential and moisture accumulation. Of particular concern in hot-humid climates is dripping condensation on cold air-conditioning ductwork in the summer - commonly referred to as duct "sweating" - which threatens the attic floor with conditions conducive to mold growth and rot. One strategy to mitigate these moisture issues is to wrap ductwork in thicker duct-wrap insulation with an integrated exterior vapor barrier, but thick duct wrap can be difficult to come by, expensive, and unwieldy to work with. This study explores an alternative strategy of reducing moisture issues while embracing energy efficiency by using unvented attics with vapor diffusion ports and buried ductwork in hot-humid climates. Vapor diffusion ports have been studied so far in a wide range of U.S. climates, mostly in the context of conditioned attics. In this study, the strategy is implemented in the novel context of hot-humid climates with ductwork sitting atop blown-in attic floor insulation in unconditioned attics. Using a combination of field experiments and hygrothermal modeling, the findings of this project indicate that an unvented attic with vapor diffusion ports and buried ducts may be a key part of a successful low-cost method for reducing the attic moisture load by venting excess moisture out of the attic, keeping duct-jacket surfaces above dew point temperature, and keeping the roof deck safe from winter moisture accumulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Identifying preferential flow from soil moisture time series: Review of methodologies

Abstract Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones. Core Ideas Soil moisture data can be used to identify the occurrence of preferential flow (PF) and its initiating conditions. Various data‐analysis methods to identify PF differ in susceptibility to error, bias, and subjectivity. These methods can utilize vast amounts of data from soil moisture monitoring networks to develop understanding of when, where, and under what conditions PF occurs. Newly developed methods may lead to better accuracy and reliability, and reduce the need for subjective judgments. Plain Language Summary Preferential flow through soil occurs when a large amount of water is suddenly available, as during an intense storm. This type of flow moves rapidly through the soil in distinct narrow pathways rather than moving evenly throughout the body of soil, with major consequences for groundwater resources, ecosystems, spreading of contaminants, and other vital concerns. Methods of detecting preferential flow have been developed that utilize measurements of soil water content made by sensors installed at various depths. This measurement technology has been widely implemented, many locations now having datasets years in length, and various methods have been developed for using these to identify preferential flow. The various methods are based on different features in the soil moisture records and vary in their advantages and shortcomings. In this review, we explain and evaluate these methods, highlighting important information for their implementation to identify preferential flow from soil moisture data and for efforts to develop new methods or improve existing ones.

Nimmo, John R↗
DOE OSTI2026

Observed Land Surface Influence on Atmospheric Heat and Moisture Profiles During Interstorms

Land-atmospheric (L-A) feedbacks have historically been studied using models whose structure and parameterizations influence outcomes and insights. The representation of L-A feedbacks based on observations alone remains an ongoing challenge for understanding boundary layer development and precipitation. To address this gap, we use ground-based passive remote sensing and in-situ observations to present an analysis of the atmosphere during 103 interstorm soil moisture drydown events spanning nine warm seasons (2016–2024) in the U.S. Southern Great Plains region. By separating events based on local L-A coupling signals and characterizing the profiles of atmospheric heat and moisture to surface energy flux behavior, we investigate the physical mechanisms linking land surface processes to boundary layer development. We find that during interstorm drydowns, the atmospheric column follows a consistent pattern: moisture increases within the boundary layer, peaks near its top, and declines rapidly above, while warming occurs through the depth. Drydowns that shift toward evaporation produce stronger and deeper thermodynamic responses than cases dominated by sensible heating, which are weaker and shallower. Additionally, moisture is accumulated faster within the boundary layer during shorter drydowns, with longer drydowns representing slower, moisture-limited growth. Drydowns with wetter initial soil moisture will sustain stronger moistening within and above the boundary layer, accelerating buoyancy growth and convective potential toward the next storm. These results provide observational evidence linking surface flux evolution to boundary layer thermodynamics and offer a process-level benchmark for evaluating coupled L-A representations in models and demonstrating the influence of soil moisture on short-term weather forecasting skill.

Zhang, M. S. [Massachusetts Inst. of Technology (M↗
DOE OSTI2025

Soil moisture gradients strengthen mesoscale convective systems by increasing wind shear

Mesoscale convective systems are a class of storm linked to extensive flooding and other destructive hazards in many regions globally. In West Africa, soil moisture impacts provide a valuable source of predictability for mature storm hazards, but little is known about mature storm sensitivity to soil moisture in other climatic regions. Here we use a storm track dataset, satellite observations and reanalysis fields to investigate the response of mature storms to soil moisture in seven global storm hotspots—West Africa, India, South America, South Africa, Australia and the United States Great Plains. We demonstrate that mesoscale soil moisture gradients (~500 km) can enhance storms by driving increased vertical wind shear conditions, a crucial ingredient for storm organization, through the strengthening of atmospheric temperature gradients. This is evidenced by a 10–30% increase in precipitation feature size and rainfall for the largest storms on days with favourable soil moisture gradients compared with unfavourable gradients. Global simulations confirm that soil moisture gradients influence wind shear. The results demonstrate the importance of soil moisture feedbacks for accurate forecasting of mesoscale convective systems and future projections of extreme events under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil moisture-atmosphere interactions drive terrestrial carbon-water trade-offs

Soil moisture is coupled with vegetation and atmosphere, influencing global cycling of water, carbon, and energy. However, it remains unclear how soil moisture-atmosphere interactions affect land-atmosphere carbon and water exchanges simultaneously. Using Earth system model experiments, we show widespread carbon-water trade-offs between net ecosystem production and precipitation-minus-evapotranspiration driven by soil moisture dynamics. Soil moisture positively controls net ecosystem production and negatively affects precipitation-minus-evapotranspiration, through direct soil water stress and indirect soil moisture-atmosphere feedbacks. While soil moisture variability magnifies the interannual variability of net ecosystem production, it moderates that of precipitation-minus-evapotranspiration over land. These opposing effects lead to a pronounced carbon-water trade-off, which originates from the interplay between carbon acquisition through photosynthesis and water extraction through evapotranspiration. This trade-off is projected to intensify in a warming and drying future, as soil moisture increasingly regulates carbon and water exchanges, posing a serious challenge to sustaining both terrestrial carbon sink and water supply.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The critical role of soil moisture in compound hazards

Soil moisture regulates the exchange of energy, water, and carbon across land–vegetation–atmosphere interfaces. Extremes in soil moisture can amplify natural hazards through interactions with diverse Earth system processes. Despite its mechanistic importance, soil moisture remains underrepresented in hazard research and predictive frameworks. Here, in this study, we review our current understanding of the role of soil moisture in the evolution and onset of diverse compound hazards by synthesizing the latest findings from observational and modelling studies. We highlight key soil moisture mechanisms, including atmospheric feedbacks that amplify drought–heatwave–wildfire events, precipitation couplings that promote clustered storms, and threshold responses that drive vegetation die-offs, trigger landslides, and induce flooding. Persistent challenges in observational data, model representation and operational implementation have limited the integration of soil moisture into hazard early-warning systems. Addressing these gaps through advances in observations, data assimilation, and physics-based and data-driven modelling will enhance hazard prediction and preparedness in a rapidly changing world.

Li, Chuxuan [University of California, Los Angeles↗
DOE OSTI2024

Modeling the Moisture Content and Dry Matter Loss in Dynamic Woody Biomass Storage Piles with Variable Extraction

The urgent need to mitigate climate change has spurred significant interest in renewable energy sources. This paper explores the storage and processing of woody biomass for biofuel production, considering the dynamic nature of biomass piles in real-world scenarios. A model has been developed to analyze moisture content changes and dry matter loss in woody biomass stored in piles prior to processing, taking into account varying extraction methods and environmental conditions. Case studies that examine the effects of different unpiling methods (FIFO, LIFO, and homogeneous) on moisture content and dry matter loss under various feedstock arrival rates and weather conditions are presented. Results indicate that unpiling methods significantly impact moisture content, with LIFO typically resulting in higher moisture content due to the utilization of fresher feedstock. Dry matter loss increases with pile size and time, emphasizing the importance of accurate modeling for assessing carbon emissions and feedstock quality. Furthermore, the model highlights the importance of process loading order and extraction methods in biomass storage, suggesting potential cost benefits associated with decreased moisture content. The difference between different extraction methods could vary the moisture content in the feedstock reaching the biofuel process by as much as 37.6%, however dry matter loss varies minimally for realistic pile changes. Overall, this research contributes to a better understanding of biomass storage dynamics and informs sustainable biofuel production practices.

Niska, Janel↗
DOE OSTI2024

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

Farguell, Angel (ORCID:000000032395220X)↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

Exploring Phosphonium‐Based Anion Exchange Polymers for Moisture Swing Direct Air Capture of Carbon Dioxide

This study explores the performance and stability of ammonium and phosphonium-based polymeric ionic liquids (PILs) with methyl and butyl substituents in moisture-swing direct air capture of CO 2 . The polymers are synthesized with chloride counterions, followed by ion exchange to the bicarbonate ion, and tests for CO 2 capture capacity and stability under cyclic wet–dry conditions. The phosphonium polymer with methyl substituents [PVBT-MeP] demonstrates the highest CO 2 capture capacity at ≈510 µmol g⁻¹, attributed to minimal steric hindrance and stronger ion pairing with bicarbonate. However, oxidative degradation is detected by 31 P NMR spectroscopy after the moisture swing experiment, with the appearance of a phosphine oxide peak at 61.28 ppm, which indicates phosphorus oxidation as the primary degradation pathway. In contrast, the ammonium polymer with butyl substituents [PVBT-BuN] exhibits the highest stability, showing no degradation over five moisture swing cycles. Additional stability experiments in 0.5 m KHCO 3 solutions reveal no degradation for any PIL, suggesting that oxidative degradation is driven by dynamic acid-base reactions during the moisture swing cycles in the air. Furthermore, these findings reveal the potential of phosphonium-based PILs for moisture-swing direct air capture, achieving high capacity while highlighting the need for optimized stability through counterion and structural design.

36 MATERIALS SCIENCE↗