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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Adhesion Capabilities of Permanent Foaming Fixatives

DOE-EM have identified an operational requirement for a fixative that can immobilize and/or encapsulate residual contamination in 3D void volumes (pipes, gloveboxes, waste containers, etc.) during D and D activities. Failure to safely and effectively immobilize residual contamination can: Put workers at risk; Contaminate the public and environment; Drive up costs. Commercial-off-the-shelf (COTS) polyurethane (PU) foams is one possible solution and is currently being investigated by Savannah River National Lab (SRNL). Adhesion testing will dictate how well PU foams will adhere and immobilize residual contamination onto a substrate. Results can provide a performance criteria for Section 5 of ASTM E3191: Standard Specification for Permanent Foaming Fixatives Used to Mitigate Spread of Radioactive Contamination. Initial mechanical testing showed intumescent rigid PU foams were best in class (tensile, compression, TGA/DSC) Application of intumescent rigid PU foam in pipe scenario with 'contamination' showed excellent adhesion capabilities as long as foam had contact with pipe. The foam, however, was not able to penetrate 'contamination' but created pockets where the contamination interacted with the substrate. Pretreatment options of pipe were considered to produce better immobilization capabilities. Pretreatment options included COTS products like baking spray, rubber cement, and soap. Foam was still able to immobilize on a global sense, but not a local sense. Essentially acted as a mechanical plug. A tensile tester (MTS Criterion Series 43) was utilized to evaluate the tensile adhesion strength of 6 COTS PU foams. ASTM D1623: Tensile and Tensile Adhesion Properties of Rigid Cellular Plastics. Procedures: PU foams were cured between two smooth 2'' x 2'' 304 stainless steel coupons with hinges glued on plates for gripping support. Parameters Used: Pull rate of 0.1 in/min. Calculations: Tensile adhesion strength, elongation, and ImageJ analysis of how PU foam is left on substrate. Outcome: The PU foam with the best adhesion capabilities would be downselected for further evaluation to be used as a permanent foaming fixative. The rigid PU foams had the best adhesion capabilities with the R1 foam having a max load of 236 N. One of the I-R2 samples reached almost 450 N and another sample reached 2500 N before slipping. The other intumescent foam, I-F4, had the most coverage on the stainless steel coupon's surface from the ImageJ results (61.43% surface coverage). The intumescent foam, I-R2, will be the foam of choice for future testing due its fire retardant and adhesion capabilities. Future directions: Conduct the following experiment in a pipe scenario with the down-selected PU foam. Procedures: PU foam will be cured between in a 304 stainless steel pipe (ID: 4'', OD: 4.5'', Height: 4''). Parameters Used: Compression testing results will be referenced to determine if foam's adhesion strength would be greater its compression strength as its being compressed. Calculations: Compression strength, shear stress, total time elapsed. Conduct further adhesion testing with the down-selected PU foam (I-R2) to be treated as a permanent foaming fixative (PFF). Determine the minimum contact the PFF should have with the substrate for adequate results. Evaluate if PFF's adhesion capabilities is time dependent (3.1.5 of ASTM E3911). Subject PFF to seismic stressors addressed in Safety Basis of Interim Operation documents (SBIO) to further evaluate adhesion capabilities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Image Characterization of Reactions Generated by an Aeroengine Micromixing Injector for Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blend

An aeroengine micromixing injector, originally designed for lean direct injection of jet fuel, was adapted to work with hydrogen, natural gas, and any blend in between. The ultimate goal of the design was to achieve low NOx emissions when operated on pure hydrogen; to better characterize its performance, flame imaging diagnostics was implemented. Three types of cameras mounted parallel to the injector base and pointing towards the quartz combustor cylinder with the same angle were operated simultaneously: Nikon D90, Dynacolor FB-N9-U, and Phantom v7.1, to capture the visible spectrum, OH* chemiluminescence, and dynamic behavior of the flames, respectively. The current work also includes the overlapping of visible spectrum and OH* chemiluminescence, as an effort to qualitatively define the heat release over the flame area, i.e., UV over visible domain. Additionally, a z-type schlieren configuration was employed to reinforce the presence of some small-scale details occurring near the injector outlet ports. Using the airbox pressure drop, preheat temperature, fuel composition and flame temperature as the factors of study, a Box Behnken model was designed yielding into a 16-points matrix for the operability region, at atmospheric pressure. The main responses were extracted from the OH* images, because of its correlation with the flame heat release, these being the flame area and its average brightness, the heat release area and its center of gravity and leading edge. To assess the importance of the main factors and their interactions, an analysis of variance (ANOVA) was performed for each response, considering as significant each contributor with a p-value below 0.05. For these five responses it was found that all four main factors need to be included into the model (most of them because its own significance, others to ensure the model hierarchy), as well as some common interactions as the preheat and flame temperatures or pressure drop and fuel composition, and quadratic terms from fuel composition or flame temperature. Three of the five models presented R2 and coefficient of variance (C.V.) indicators around 0.97 and 5%, respectively; the leading edge ANOVA yielded into R2=0.78 and C.V.=18.88%; and the heat release area showed R2=0.91 and C.V.=24.13%. Lastly, the confinement ratio effect of the combustor over the flame structure was quantified, for three different conditions: the original 80 mm inner diameter and 200 mm length cylinder was compared against a shorter 150 mm tube (same inner diameter) and a narrower 47 mm inner diameter one (same 200 mm length). The length had a smaller effect than the inner diameter, even though both factors were not strong enough to make a significant deviation on most of the flame parameters: the difference was within the repeatability margin of error for the brightness, flame area, and center of gravity and leading edge of the heat release area.

Imaging diagnostics, flame characterization, OH* c↗
DOE OSTI2025

Aided Active Learning (AAL) for Enhanced Critical Heat Flux Prediction

Accurate prediction of critical heat flux (CHF) is crucial for the safe and efficient operation of nuclear reactors. Traditional CHF modeling methods often require extensive experimental data, which are hard to obtain. This study introduces the Aided Active Learning (AAL) framework, which strategically minimizes data requirements without sacrificing model accuracy. Unlike conventional Active Learning (AL), AAL introduces an additional step of randomly selecting a subset from the sample pool before applying the query strategy. To evaluate the performance of AAL, two query strategies—uncertainty-based sampling and error-reduction sampling—were evaluated across the following models: random forest (RF), feedforward neural network (FNN), and variational feedforward neural network (vFNN). The proposed framework demonstrated that AAL effectively reduces the number of training samples needed to achieve comparable predictive accuracy. For the RF model, AL required only 710 samples to achieve an R2 score of 0.98, as compared to the 4,785 samples needed by random sampling. Similarly, the FNN model achieved the same R2 score with just 355 samples when using AL, a significant improvement over the 825 samples required by random sampling. In case of uncertainty-based sampling strategy, vFNN attained an R2 of 0.98 with 3,420 samples, reducing the sample requirement by 47% relative to the 6,440 samples needed for random sampling. Its performance suggests that larger training data are required to fully leverage its uncertainty quantification capabilities.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Putting the soil health principles to the test in Iowa

One of the most popular soil conservation campaigns is based on the USDA Natural Resource Conservation Service's Soil Health Principles (NRCS-SHPs). The NRCS-SHP program identifies four principles—maximize presence of living roots, minimize disturbance, maximize soil cover, and maximize biodiversity—with the underlying assumption that the more principles one follows, the greater improvements in soil health. Despite the popularity of the NRCS-SHPs, this underlying assumption has not been rigorously tested. To do so, we used nine long-term experiments all located in central Iowa, but with varying degree of NRCS-SHP adoption, to determine if greater adoption increases three slow-changing (maximum water holding capacity, bulk density [BD], and soil organic carbon) and three dynamic (microbial biomass carbon [MBC], potentially mineralizable carbon [PMC], and permanganate oxidizable carbon [POXC]) soil health indicators. We regressed these indicators with a soil health principle score that can scale soil management based on adoption of the NRCS-SHPs. Of the slow-changing soil properties, increased adoption of NRCS-SHPs only decreased soil BD (R2 = 0.22, p = 0.024). On the other hand, increased adoption of NRCS-SHPs strongly predicted increases in both MBC and PMC and across two sampling dates (R2 > 0.23, p < 0.015); POXC, however, did not increase with greater adoption. The consistent increases in MBC and PMC with greater adoption of NRCS-SHPs supports their usefulness as sensitive indicators of positive soil health change. Our study provides scientific evidence to support the NRCS-SHPs concept, improving its usefulness as an extension campaign, and stands as a step toward evidence-based soil conservation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rapid Biomass Compositional Analysis

The performance of a conventional laboratory near-infrared (NIR) spectrometer and two NIR spectrometer prototypes (a Texas Instruments NIRSCAN Nano evaluation model (EVM) and an InnoSpectra NIR-M-R2 spectrometer) are compared by collecting reflectance spectra of 270 well-characterized herbaceous biomass samples, building calibration models using the partial least squares (PLS-2) algorithm to predict five constituents of the samples from the reflectance spectra, and comparing the resulting model statistics. The prediction models developed using spectra from the Foss XDS spectrometer were slightly better than the prediction models developed using spectra from either the TI NIRSCAN Nano EVM and the InnoSpectra NIR-M-R2 as measured by the root mean square error (RMSECV) and the correlation coefficient (R 2 _cv) for “leave-one-out” cross-validation (CV). The models built from the two prototype units were not statistically significantly different from each other (p = 0.05). The Foss spectrometer has a larger wavelength range (400–2500 nm) compared with the two prototypes (900–1700 nm). When the spectra from the Foss XDS spectrometer were truncated so their wavelength range matched the wavelength range of the two prototype units, the resulting model was not statistically significantly different from the models from either prototype.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems

As one of the major agricultural production areas in the world, the United States (U.S.) Midwest plays a vital role in the global food supply and agricultural ecosystem services. Although significant efforts have been made in modeling the carbon cycle dynamics over this area, large uncertainty still exists in the previous simulations in terms of reproducing individual components of the carbon cycle and their responses to environmental variability. Here we evaluated the performance of an advanced agroecosystem model, ecosys, in simulating carbon budgets over the U.S. Midwest, considering both the magnitude of carbon flux/yield and its response to environmental (climate and soil) variability. We conducted model simulations and evaluations at 7 cropland eddy-covariance sites as well as over 293 counties of Illinois, Indiana, and Iowa in the U.S. Midwest. The site-level simulations showed that ecosys captured both the magnitude and seasonal patterns of carbon fluxes (i.e., net ecosystem carbon exchange (NEE), ecosystem gross primary production (GPP), and ecosystem respiration (Reco)), leaf area index (LAI), and dynamic plant carbon allocation processes, with R2 equal to 0.92, 0.87, 0.87, and 0.78 for GPP, NEE, Reco, and LAI, respectively across all the sites compared with the observations. For regional scale simulations, ecosys reproduced the spatial distribution and interannual variability of corn and soybean yields with the constraints of observed yields and a new remotely sensed GPP product, with R2 of multi-year averaged simulated and observed yield equal 0.83 and 0.80 for corn and soybean, respectively. The simulated responses of carbon cycle dynamics to environmental variability were consistent with that from the empirical observations at both site and regional scales. Furthermore, our results demonstrated the applicability of ecosys in simulating the carbon cycle and soil carbon dynamics of the U.S. Midwestern agroecosystems under different climate and soil conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Unraveling the effects of management and climate on carbon fluxes of U.S. croplands using the USDA Long-Term Agroecosystem (LTAR) network

Understanding the carbon fluxes and dynamics from a broad range of agricultural systems has the potential to improve our ability to increase carbon sequestration while maintaining crop yields. Short-term, single-location studies have limited applicability, but long-term data from a network of many locations can provide a broader understanding across gradients of climate and management choices. Here we examine eddy covariance measured carbon dioxide (CO 2 ) fluxes from cropland sites across the United States Department of Agriculture's Long-Term Agroecosystem Research (LTAR) network. The dataset was collected between 2001 and 2020, spanning 13 sites for a total of 182 site-years. Average seasonal patterns of net ecosystem CO 2 exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (R eco ) were determined, and subsequent regression analysis on these “flux climatologies” was used to identify relationships to mean annual temperature (MAT), mean annual precipitation (MAP), cropping systems, and management practices. At rainfed sites, carbon fluxes were better correlated with MAP (r2 ≤ 0.5) than MAT (r2 ≤ 0.22). Net carbon balance was different among cropping systems (p < 0.001), with the greatest net carbon uptake occurring in sugarcane (Saccharum spp. hybrids) and the least in soybean (Glycine max) fields. Crop type had a greater effect on carbon balance than irrigation management at a Nebraska site. Across cropping systems, grain crops often had higher GPP and were more likely to have net uptake when compared to legume crops. This multi-site analysis highlights the potential of the LTAR network to further carbon flux research using eddy covariance measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Transcriptional pathways linked to fetal and maternal hepatic dysfunction caused by gestational exposure to perfluorooctanoic acid (PFOA) or hexafluoropropylene oxide-dimer acid (HFPO-DA or GenX) in CD-1 mice

Per- and polyfluoroalkyl substances (PFAS) comprise a diverse class of chemicals used in industrial processes, consumer products, and fire-fighting foams which have become environmental pollutants of concern due to their persistence, ubiquity, and associations with adverse human health outcomes, including in pregnant persons and their offspring. Multiple PFAS are associated with adverse liver outcomes in adult humans and toxicological models, but effects on the developing liver are not fully described. Here we performed transcriptomic analyses in the mouse to investigate the molecular mechanisms of hepatic toxicity in the dam and its fetus after exposure to two different PFAS, perfluorooctanoic acid (PFOA) and its replacement, hexafluoropropylene oxide-dimer acid (HFPO-DA, known as GenX). Pregnant CD-1 mice were exposed via oral gavage from embryonic day (E) 1.5-17.5 to PFOA (0, 1, or 5 mg/kg-d) or GenX (0, 2, or 10 mg/kg-d). Maternal and fetal liver RNA was isolated (N = 5 per dose/group) and the transcriptome analyzed by Affymetrix Array. Differentially expressed genes (DEG) and differentially enriched pathways (DEP) were obtained. DEG patterns were similar in maternal liver for 5 mg/kg PFOA, 2 mg/kg GenX, and 10 mg/kg GenX (R2: 0.46-0.66). DEG patterns were similar across all 4 dose groups in fetal liver (R2: 0.59-0.81). There were more DEGs in fetal liver compared to maternal liver at the low doses for both PFOA (fetal = 69, maternal = 8) and GenX (fetal = 154, maternal = 93). Upregulated DEPs identified across all groups included Fatty Acid Metabolism, Peroxisome, Oxidative Phosphorylation, Adipogenesis, and Bile Acid Metabolism. Transcriptome-phenotype correlation analyses demonstrated > 1000 maternal liver DEGs were significantly correlated with maternal relative liver weight (R 2 >0.92). These findings show shared biological pathways of liver toxicity for PFOA and GenX in maternal and fetal livers in CD-1 mice. The limited overlap in specific DEGs between the dam and fetus suggests the developing liver responds differently than the adult liver to these chemical stressors. This work helps define mechanisms of hepatic toxicity of two structurally unique PFAS and may help predict latent consequences of developmental exposure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Arm and shoulder muscle segmentation in axial MRI with UNet deep learning model

Quantifying individual upper-limb muscle volumes from MRI provides key insight into muscle-specific strength, deficits, and adaptations. Manual delineation is the gold standard but time‑intensive, and the performance of current deep learning approaches, particularly for small or anatomically complex muscles, remains incompletely characterized. We evaluated a state‑of‑the‑art deep learning framework across the entire upper limb and analyzed factors governing segmentation performance, with attention to the forearm. Three previously published MRI datasets (1.5 T, 3D GRE T1‑weighted; total n = 39) spanning young, middle‑aged, and older adults were curated and quality‑checked, including expert manual segmentations for 31 muscles. Following multiclass mask reconstruction, we trained three 3D nnU‑Net multiclass models matched to the muscle subsets present across datasets, using five‑fold cross‑validation and a composite Dice Similarity Coefficient (DSC) + cross entropy loss. Segmentation accuracy was assessed with DSC. Performance varied across muscles (mean DSC = 0.806 ± 0.098), ranging from 0.920 (Deltoid) to 0.461 (Extensor pollicis brevis). In uncertainty‑weighted regressions, muscle volume was positively associated with DSC (R2 = 0.36, p < 0.001), whereas training segmentation count and muscle orientation showed negligible associations (R2 ≤ 0.06). A weighted mixed‑effects model identified volume as the strongest evaluated predictor, explaining 23.9% of variance in DSC; orientation and training count each contributed <1%, leaving 61.5% unexplained. These results indicate that deep learning–based segmentation can accurately quantify muscle volume for many upper‑limb muscles but remains constrained for small, low‑contrast forearm muscles.

Gillespie, Samuel↗
DOE OSTI2025

Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method

Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.

Indicator↗
DOE OSTI2021

On the Rate Constant for NH 2 +HO 2 and Third-Body Collision Efficiencies for NH 2 +H(+M) and NH 2 +NH 2 (+M)

In low-temperature flash photolysis of NH 3 /O 2 /N 2 mixtures, the NH 2 consumption rate and the product distribution is controlled by the reactions NH 2 + HO 2 → products (R1), NH 2 + H (+M) → NH 3 (+M) (R2), and NH 2 + NH 2 (+M) → N 2 H 4 (+M) (R3). In the present work, published flash photolysis experiments by, among others, Cheskis and co-workers, are re-interpreted using recent direct measurements of NH 2 + H (+N 2 ) and NH 2 + NH 2 (+N 2 ) from Altinay and Macdonald. To facilitate analysis of the FP data, relative third-body collision efficiencies compared to N 2 for R2 and R3 were calculated for O 2 and NH 3 as well as for other selected molecules. We report results were in good agreement with the limited experimental data. Based on reported NH 2 decay rates in flash photolysis of NH 3 /O 2 /N 2 , a rate constant for NH 2 + HO 2 → NH 3 + O 2 (R1a) of $k_{1\text{a}}$ = 1.5(±0.5) × 10 14 cm 3 mol –1 s –1 at 295 K was derived. This value is higher than earlier determinations based on the FP results but in good agreement with recent theoretical work. Kinetic modeling of reported N 2 O yields indicates that NH 2 + HO 2 → H 2 NO + O (R1c) is competing with R1a, but perturbation experiments with addition of CH4 indicate that it is not a dominating channel. Measured HNO profiles indicate that this component is formed directly by NH 2 + HO 2 → HNO + H 2 O (R1b), but theoretical work indicates that R1b is only a minor channel. Based on this analysis, we estimate $k_{1\text{c}}$ = 2.5 × 10 13 cm 3 mol –1 s –1 and $k_{1\text{b}}$ = 2.5 × 10 12 cm 3 mol –1 s –1 at 295 K, with significant uncertainty margins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Re-Examination of the N 2 O + O Reaction

The reaction of N 2 O with O is a key step in consumption of nitrous oxide in thermal processes. It has two product channels, NO + NO (R2) and N 2 + O 2 (R3). The rate constant for R2 has been measured both in the forward and the reverse direction at elevated temperature and is well established. However, the rate constant for the N 2 + O 2 channel (R3) has been difficult to quantify and has significant error limits. Here, the direct reaction on the triplet surface has a barrier of around 40 kcal mol –1 , and it is too slow for the N 2 + O 2 channel to have any practical significance. Recently, Pham and Lin (2022) suggested an alternative low activation energy reaction path that involves intersystem crossing and reaction on the singlet surface. In the present work, we re-examined a wide range of experiments relevant for the N 2 O + O reaction through kinetic modeling, paying attention to the impact of artifacts such as impurities and surface reactions. Experimental results from shock tubes and batch reactors on the final NO yield in N 2 O decomposition, covering temperatures of 973–2200 K and pressures of 0.013–11.5 atm, support k 3 ~ 0, consistent with the high activation energy for reaction on the triplet surface and a low probability of ISC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Si–Cl Bond Activations at Ni(0) to Give Bimetallic Ni(I) μ 1,2 -Cl–SiR 1 R 2 Complexes that Undergo Selective Hydrogenolyses to R 1 R 2 SiH 2 Dihydrosilanes

Chlorosilanes are cheap and abundant raw materials as crucial building blocks in silicon chemistry, yet the metal-mediated activation and functionalization of Si–Cl bonds typically require precious metal sources due to their thermodynamic inertness. Herein, we report the stoichiometric, facile activation, and hydrogenolysis of chlorosilanes mediated by a series of low-valent NHC–Ni (NHC = N-heterocyclic carbene) complexes. Treatment of a Ni(0) complex (IPr)Ni(η 6 -toluene) (IPr = 1,3-bis(2,6-diisopropylphenyl)imidazole-2-ylidene) with chlorosilanes (R 1 R 2 SiCl 2 , R 1 = Cl, R 2 = Cl, Me, Ph, or R 1 = R 2 = Me, Et, Ph, 4-MePh) rapidly afforded di-Ni(I) complexes with a bridging silyl ligand ([(IPr)Ni] 2 (μ-SiR 1 R 2 Cl)(μ-Cl), 1 R1,R2 ) in high yields. Use of a bulkier chlorosilane, Ph 2 SiCl 2 , allowed the isolation of the mono-Ni(II) silyl complex (IPr)Ni(SiPh 2 Cl)Cl (2 Ph ) as an intermediate generated via Si–Cl oxidative addition, which underwent comproportionation with (IPr)Ni(η 6 -toluene) to form 1 Ph,Ph in nearly quantitative yield. Interestingly, 1 R1,R2 was found to react with H 2 at room temperature to form mono- or di-hydrosilanes in moderate to high yields, and the product selectivity was found to be highly dependent on the identity of substituents on Si. In conclusion, these results demonstrate a novel example of facile Si–Cl activation and hydrogenolysis mediated by low-valent mono- and dinuclear NHC–Ni complexes under mild conditions.

Liu, Tianchang [University of California, Berkeley↗
DOE OSTI2020

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to evaluate and constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and the radiative transfer of SIF to remote sensing sensors is an essential prerequisite for the evaluation and data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy level is usually not well-represented. In this work, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). An efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and a canopy-level model agreed well at sites except one in needle leaf forest (R2>0.91, root-mean-square error < 0.19W m -2 sr -1 um -1 ), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R 2 >0.68). At the global scale, simulated SIF generally captured the spatial and seasonal pat37 terns of satellite-observed SIF (R2 > 0.76 except for tropical forest). Factors including the fluorescence emission model, clumping, bidirectional effect, and canopy properties (leaf optical properties and leaf area index) had considerable impacts on SIF simulation, and the discrepancies between simulated and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF towards constraining and evaluating GPP simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Electric field dependence of nanoscale cathodoluminescence inside Cr doped β-Ga 2 O 3 interfaces

Chromium (Cr) is a common impurity in β-Ga 2 O 3 crystals, where its characteristic R1 and R2 luminescence lines are susceptible to both the host crystal field and externally applied fields. Here, in this work, we demonstrate that the Cr cathodoluminescence (CL) quenches toward the bulk of the crystal but enhances with applied reverse bias, reflecting the effect of free carrier depletion with increasing electric field. Furthermore, we illustrate that the R1/R2 CL intensity ratio, measured as the integrated area ratio of R1 to R2, can be used as a direct probe of the electric field in a Ni-β-Ga 2 O 3 : Cr Schottky diode. This optical calibration method provides a complementary approach to conventional C–V and I–V measurements for determining electric field strength in the depletion region of β-Ga 2 O 3 -based Schottky diodes and can be extended to other semiconductors and multilayer device structures.

36 MATERIALS SCIENCE↗