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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Revisiting the relationship between demand growth and electricity prices

Here, in this Commentary, we aim to clarify the relationship between electricity demand and prices in the short- and longer-term. We outline three dimensions that shape this relationship: system capacity utilization, system expansion costs, and cost allocation in the rate design process. We explain why demand growth has historically been largely associated with falling electricity prices and explore arguments that future C&I demand growth may increase prices.

O'Shaughnessy, Eric [Lawrence Berkeley National La↗
DOE OSTI2026

Cascading economic losses from port disruptions under capacity constrained multimodal freight networks

This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.

42 ENGINEERING↗
DOE OSTI2026

Transforming E-Waste Into Strategic Resources: Techno-Economic Analysis of Gallium and By-products Recovery from LEDs via Bioleaching

The growing demand for gallium in optoelectronics and renewable energy applications raises concerns about supply security and production sustainability. This study evaluates the techno-economic feasibility of recovering gallium and by-products (copper and nickel) from waste GaN-based LEDs via bioleaching. A process flowsheet encompassing transportation, robotic disassembly, ball milling, bioleaching, solvent extraction/electrowinning, and refining was modeled. Based on mass balance analysis, more than 53 tons of LED waste are required annually to yield 1 kg of gallium alongside substantial copper and nickel co-products. Preliminary techno-economic analysis (TEA) shows an average total cost (ATC) of 6.84 USD/kg metal when costs are allocated by mass-weighted economic value (market price) fraction, corresponding to 6.75 USD/kg for copper, 15.91 USD/kg for nickel, and 470.95 USD/kg for gallium. For gallium, direct operational costs account for more than 70% of the cost. Monte Carlo simulations further quantify cost uncertainties under market price fluctuations. This work represents the first TEA of gallium recovery from GaN-based LEDs and highlights potential pathways for future cost reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The interactivity of sources and dietary levels of resistant starches – impact on growth performance, starch, and nutrient digestibility, digesta oligosaccharides profile, cecal microbial metabolites, and indicators of gut health in broiler chickens

In a 21-d study, 480 Cobb 500 (off-sex) male broiler chicks were used to investigate the effects of feeding different sources and levels of resistant starches (RS) on growth performance, nutrient and energy utilization, and intestinal health in broiler chickens. The birds were allocated to 10 dietary treatments in a 3 × 3 + 1 factorial arrangement. The factors were 3 RS-sources (RSS): banana starch (BS), raw potato starch (RPS), and high-amylose corn starch (HCS); each at 3 levels (RSL) 25, 50, or 100 g/kg plus a corn-soybean meal control diet. Birds and feed were weighed on d 0, 8, and 21. On d 21, samples of jejunal tissue and digesta were collected for chemical analysis. Data were analyzed using the mixed model procedure of JMP with factor levels nested with the control. In the 0 to 21 phase, the birds fed the RPS diets had higher (P = 0.011) FI than those fed HCS or control diets, and FCR was greater (P = 0.030) in birds that received BS diets than in other diets. RSS × RSL was significant (P < 0.05) for total tract nutrient retention, AME, and AMEn on d 21. The starch digestibility was higher (P < 0.001) in birds that received the control diet than in RS diets, and decreased as RS levels increased, except for HCS. The apparent metabolizable energy (AME) and nitrogen-corrected AME (AMEn) were higher (P < 0.001) in birds fed 100 g/kg HCS diet, with both decreasing with increasing levels of BS and RPS, except for HCS. Relative ileal oligosaccharides profile showed significant (P < 0.05) RSS × RSL with a higher relative abundance of Hex(3) (P = 0.01) and Pent(3) (P = 0.001) in HCS diets. In conclusion, RS may influence gut health and growth performance in broiler chickens through modulation of cecal SCFA and nutrient digestion, but these depend largely on the botanical origin and concentrations of individual RS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Water acidification aggravates lithium-induced toxicity represented by energy supply, oxidative stress, and cell fate in Daphnia magna neonates

Lithium is extensively utilized in industrial energy production, particularly in lithium-ion batteries, and in pharmaceuticals for the treating clinical mood disorders. Consequently, lithium is frequently detected in various environmental matrices. It has been reported to cause a range of toxic effects on aquatic organisms including oxidative stress, neurological disorders, and reproductive suppression. Water acidification is a global issue with numerous negative impacts on aquatic organisms. It can alter the physio-chemical properties and bioavailability of metal ions. The acidic leaching process during lithium battery treatment and global water acidification both suggest that lithium contamination often occurs in acidic environments. In the present study, Daphnia magna neonates were exposed to four treatments (control, lithium alone, low pH, and combined) to investigate whether an acidic environment exacerbates the toxic effects of lithium on aquatic organisms and to explore potential toxic action mechanisms. The results indicated that low pH posed a significant threat to the growth and reproduction of D. magna. When exposed to both lithium and low pH, there was increased lithium accumulation and an energy trade-off response, leading to increased energy allocation to reproduction and reduced energy for growth. Lithium exposure stimulated D. magna activity, while low pH inhibited it, suggesting that an imbalance in energy consumption and supply. Combined exposure to lithium and low pH resulted in severe oxidative stress due to mitochondrial dysfunction, under-utilization of energy substances, and increased ionic homeostasis disturbances. Consequently, the exposed organism altered apoptosis and autophagy processes to maintain homeostasis. In conclusion, the present study demonstrated that lithium and water acidification posed a population-level threat to D. magna, and their combined exposure significantly largely exacerbated the toxic effects.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2026

Distributed optimization and control for autonomous distributed energy resource power dispatch and frequency regulation considering communication failures

Distributed energy resources (DERs) can significantly enhance grid flexibility by supplying power and secondary frequency regulation (SFR) services. Yet, their large number and dispersed nature limit the effectiveness of traditional centralized power dispatch and frequency control schemes, and expose the system to single-point communication failures. This paper presents a unified distributed optimization and control framework for DER power dispatch and SFR provision. A transmission-loss-aware distributed direct current optimal power flow is first formulated to optimize DER power dispatch and reserve scheduling. On top of this, a fully distributed control scheme for DER autonomous SFR provision is developed to allocate automatic generation control signals cost-effectively while ensuring robustness to communication link disruptions. The framework is validated on IEEE 14-bus and 39-bus transmission systems with large-scale distribution feeders using transmission-distribution dynamic co-simulation, demonstrating reliable and autonomous DER power dispatch and SFR provision under diverse operating conditions and communication contingencies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Rewiring the unfolded protein response for plant growth recovery after stress

The unfolded protein response (UPR) is a highly coordinated signaling network that alleviates endoplasmic reticulum (ER) stress, a condition induced by diverse environmental challenges in plants. Over the past two decades, substantial progress has been made in elucidating the genetic and molecular mechanisms of ER stress sensing and signal transduction in plants, largely through studies in the model plant Arabidopsis thaliana . These advances have established the UPR as a central regulator of proteostasis and underscored its broader relevance to plant growth and development and crop productivity under stress conditions. Despite this progress, critical knowledge gaps remain, particularly concerning the downstream biological processes required for growth recovery once ER stress has subsided and how these processes are coordinated by UPR regulators. Recent systems-level and integrative studies have begun to reveal critical roles of UPR signaling in pathways governing growth re-establishment and homeostasis of nutrient allocation and energy metabolism. In this review, we highlight recent findings on the functional roles of the plant UPR in recovery from ER stress, with a focus on mechanisms mediated by UPR regulators and downstream biological pathways that enable the transition from stress mitigation to growth restoration. Although this research area is still emerging, accumulating evidence supports a model in which the UPR functions as a dynamic regulatory network that actively coordinates post-stress physiological recovery to support plant fitness.

ER stress↗
DOE OSTI2025

Characterizing the Potential for Sustainable Azelaic Acid Production from High-Oleic Vegetable Oil Using Two-Step Oxidative Cleavage

Azelaic acid is a renewable monomer conventionally produced via the energy-intensive ozonolysis of oleic acid. Recent advancements have enabled the use of high-oleic vegetable oils (rather than tallow-derived oleic acid) and replaced ozonolysis with two-step oxidative cleavage using hydrogen and oxygen. Although this shift would improve process safety, the financial viability and environmental implications remain uncertain. In this study, we characterized the sustainability of azelaic acid production from high-oleic vegetable oil using two-step oxidative cleavage. Process design, simulation, technoeconomic analysis (TEA), and life cycle assessment (LCA) were executed under uncertainty using BioSTEAM. The modeled system produces azelaic acid at a market-competitive minimum selling price (MSP) of 8.32 [4.93−13.34] $\$kg$ −1 (median 5th−95th percentiles), below the minimum estimated market price of 9.93 $\$kg$ −1 . Further, it has the potential to approach carbon neutrality (0.0 [−5.5 to 5.6] kg of CO 2 -eq kg −1 ) under displacement allocation. Improvements to dihydroxylation (86 to 99%) and oxidative cleavage conversions (93 to 99%) would reduce MSP to $\$5.24$ kg −1 and carbon intensity to −1.90 kg of CO 2 -eq kg −1 (displacement). Additionally, increasing the feedstock triolein content (75 to 85%) lowers MSP by $\$0.82$ kg −1 . Overall, this research demonstrates the potential for financially viable production of azelaic acid from vegetable oils and the utility of agile TEA/LCA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗
DOE OSTI2025

Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Overlook the Spatial Variability of Miscanthus-Derived Sustainable Aviation Fuel

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. Here, this work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate that GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Using Financial Contracts to Facilitate Informal Leases Within a Western United States Water Market Based on Prior Appropriation

The ability to reallocate water to higher-value uses during drought is an increasingly important “soft-path” tool for managing water resources in an uncertain future. In most of the Western United States, state-level water market institutions that enable reallocation also impose substantial transaction costs on market participants related to regulatory approval and litigation. These transaction costs can be prohibitive for many participants in terms of both costs and lengthy approval periods, limiting transfers and reducing allocation efficiency, particularly during drought crises periods. This manuscript describes a mechanism to reduce transaction costs by adapting an existing form of informal leases to facilitate quicker and less expensive transfers among market participants. Instead of navigating the formal approval process to lease a water right, informal leases are financial contracts for conservation that enable more junior holders of existing rights to divert water during drought, thereby allowing the formal transfer approval process to be bypassed. The informal leasing approach is tested in the Upper Colorado River Basin, where drought and institutional barriers to transfers lead to frequent shortages for urban rights holders along Colorado's Front Range. Informal leases are facilitated via option contracts that include adaptive triggers and that define volumes of additional, compensatory, releases designed to mitigate impacts to instream flows and third parties. Results suggest that more rapid reallocation of water via informal leases could have resulted in up to $222 million in additional benefits for urban rights holders during the historical period 1950–2013.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Simulating Global Terrestrial Carbon and Nitrogen Biogeochemical Cycles With Implicit and Explicit Representations of Soil Microbial Activity

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due To Functional Trait Uncertainty

Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparison of Global Aboveground Biomass Estimates From Satellite Observations and Dynamic Global Vegetation Models

The global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO 2 and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite-based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite-based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite-based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high-quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite- and model-based AGB estimates. Furthermore, we suggest the carbon-modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved Representations of Land‐Atmosphere Interactions Over the Continental U.S. Through Dynamic Root Modeling

Previous studies have identified the oversimplified root system representation as a key factor leading to inaccuracies in vegetation‐atmosphere feedbacks. In this study, a dynamic root water uptake scheme in the Noah‐MP land surface model has been coupled to the Weather Research and Forecasting (WRF) model to investigate its impact on the surface hydroclimate variables and land‐atmosphere interactions. To evaluate the impact of the dynamic root, two coupled simulations were conducted, one with the dynamic root water uptake scheme (DynRt) and one with the static root water uptake scheme (StcRt), which is based on the default root representation in Noah‐MP, with slight modifications, primarily in vegetation‐related parameters. Both DynRt and StcRt simulations were conducted with a small ensemble of three members to account for variations in physical parameterizations, initial and boundary forcing and model setup. When compared with reference data sets, the DynRt simulations show improved results than the StcRt simulations, reducing biases in the simulated leaf area index, surface energy fluxes, soil moisture and precipitation. Two different mechanisms through which roots affect land‐atmosphere coupling have been identified. Over the transitional climate zone between the dry and wet climate, the dynamic root scheme affects surface climate and land‐atmosphere coupling mainly through changes in soil moisture through hydraulic redistribution by plant root system. Over the energy‐limited mesic zone, the dynamic root affects regional land‐atmosphere coupling mainly through changes in carbon allocation. This work highlights the importance of dynamic root representation in improving vegetation‐atmosphere simulations by enhancing predictions of water, energy, and carbon fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics

Experimental soil heating experiments have found a consistent increase in soil-surface CO 2 emissions ( F s ), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO 2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F s (~17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape

Arctic warming is altering vegetation and carbon dynamics with global implications, yet Earth System Model (ESM) predictions in the Arctic remain highly uncertain, in part due to historically limited data for model parameterization and validation. As such, ESMs typically represent Arctic ecosystems in an oversimplified manner. Recently, nine plant functional types (PFTs) designed to realistically represent tundra vegetation were integrated into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and parameterized using plot-scale observations from a single site. Additional evaluation was needed to determine their transferability across the Arctic. Here, in this study, we evaluated whether refined representation of tundra vegetation improved model accuracy by conducting spatially explicit 100 × 100 m resolution ELM simulations on Alaska's Seward Peninsula. Simulations with the default two-PFT configuration and with the nine Arctic-specific PFTs were benchmarked against observations of net ecosystem exchange, gross primary production, and aboveground biomass from multiple data streams including an eddy covariance flux tower, flux chambers, and aircraft and unoccupied aerial system hyperspectral remote sensing. Evaluation revealed that Arctic-specific PFT simulations produced more realistic landscape-level carbon exchanges, and better captured observed heterogeneity in biomass and productivity, explaining 60%–70% of spatial variance (R 2 = 0.6–0.7) compared to just 12%–18% (R 2 = 0.12–0.18) with the default configuration. However, the refined model failed to reproduce observed aboveground biomass for highly productive alder-willow communities, requiring further evaluation of carbon allocation parameterizations for tall shrubs that are increasingly expanding across tundra landscapes. Our results demonstrate that enhanced representation of vegetation heterogeneity boosts predictive understanding of tundra carbon dynamics, facilitating regional to pan-Arctic model and remote-sensing scaling.

Murphy, Bailey A. [Oak Ridge National Laboratory (↗
DOE OSTI2024

Composition and metabolism of microbial communities in soil pores

Delineation of microbial habitats within the soil matrix and characterization of their environments and metabolic processes are crucial to understand soil functioning, yet their experimental identification remains persistently limited. We combined single- and triple-energy X-ray computed microtomography with pore specific allocation of 13 C labeled glucose and subsequent stable isotope probing to demonstrate how long-term disparities in vegetation history modify spatial distribution patterns of soil pore and particulate organic matter drivers of microbial habitats, and to probe bacterial communities populating such habitats. Here we show striking differences between large (30-150 µm Ø) and small (4-10 µm Ø) soil pores in (i) microbial diversity, composition, and life-strategies, (ii) responses to added substrate, (iii) metabolic pathways, and (iv) the processing and fate of labile C. We propose a microbial habitat classification concept based on biogeochemical mechanisms and localization of soil processes and also suggests interventions to mitigate the environmental consequences of agricultural management.

59 BASIC BIOLOGICAL SCIENCES↗