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103 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Airborne flux measurements of ammonia over the Southern Great Plains using chemical ionization mass spectrometry

Ammonia (NH 3 ) is an abundant trace gas in the atmosphere and an important player in atmospheric chemistry, aerosol formation and the atmosphere-surface exchange of nitrogen. It is recognized as a major source of aerosol pollution, and it may limit the formation of cloud nuclei in remote or cold parts of the atmosphere. For soil and plants, NH 3 -mediated nitrogen can act as a harmful pollutant or as a desirable nutrient, mostly in natural and agricultural settings, respectively. Agriculture is also the main source of atmospheric NH 3 via volatilization from fertilizers and manure processing in livestock farming. The accurate determination of NH 3 emission rates remains a challenge, partly due to the propensity of NH 3 to interact with instrument surfaces leading to high detection limits and slow response times. In this paper, we present a new method for quantifying ambient NH 3 , using chemical ionization mass spectrometry (CIMS) with deuterated benzene cations as reagents. The setup aimed at limiting sample-surface interactions and achieved a 1-σ precision of 10–20 pptv and an immediate 1/e response rate < 0.4 s, which compares favorably to the existing state of the art. The sensitivity exhibited an inverse humidity dependence, in particular in relatively dry conditions. Background of up to 10 % of the total signal required consideration as well, as it responded on the order of a few minutes. To showcase the method’s capabilities, we quantified NH3 mixing ratios from measurements obtained during deployment on a Gulfstream I aircraft during the HI-SCALE (Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems) field campaign in rural Oklahoma during May 2016. Typical mixing ratios were 1–10 parts per billion by volume (ppbv) for the boundary layer and 0.1–1 ppbv in the lower free troposphere. Sharp plumes of up to 10s of ppbv of NH3 were encountered as well. We identified two of their sources as a large fertilizer plant and a cattle farm, and our mixing ratio measurements yielded upper bounds of 350 ± 50 and 0.6 kg NH 3 h –1 for their respective momentary source rates. The fast response of the CIMS also allowed us to derive vertical NH 3 fluxes within the turbulent boundary layer via eddy covariance, for which we chiefly used the continuous wavelet transform technique. As expected for a region dominated by agriculture, we observed predominantly upward fluxes, implying net NH 3 emissions from surface. The corresponding analysis focused on the most suitable flight, which contained two straight-and-level legs at ~300 m above ground. We derived NH 3 fluxes between –4 and 18 mol km –2 h –1 for these legs, at an effective spatial resolution of 1–2 km. The analysis demonstrated how flux measurements benefit from suitably arranged flight tracks with sufficiently long straight-and-level legs, and explores the detrimental effect of measurement discontinuities. Following flux footprint estimations, comparison to the NH 3 area emissions inventory provided by the US Environmental Protection Agency indicated overall agreement, but also the absence of some sources, for instance the identified cattle farm. Our study concludes that high-precision CIMS measurements are a powerful tool for in-situ measurements of ambient NH 3 mixing ratios, and even allow for the airborne mapping of the air-surface exchange of NH 3 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2023

In-situ Synthesis of PuCl 3 and Corresponding Raman and Density Functional Theory Vibrational Modes

The experimental and calculated Raman spectrum of PuCl 3 has been reported for the first time. PuCl 3 is a primary species found in plutonium metal refinement, specifically in pyrochemical salt processes including multicycle direct oxide reduction, metal chlorination, and electrorefining. As such, Raman signatures of PuCl 3 could serve as potential forensic indicators of material process history. A novel technique for synthesizing PuCl 3 from the in situ chlorination of plutonium metal with HCl was developed to establish these signatures. Cerium metal surrogates were utilized to ensure optimization of the plutonium experiments and to minimize personnel exposure, and all experiments were carried out in a Raman reaction chamber designed for air-tight, high vacuum environments. In situ Raman spectroscopy was employed in conjunction with density functional theory (DFT) to investigate the vibrational modes of PuCl 3 . Associated mixed oxy and hydroxyl phases are also reported. The combined Raman and DFT results have eliminated inconsistencies in Raman mode assignments for the MX 3 family of metal chlorides having P6 3 /m symmetry, and IR modes derived from the DFT calculations are additionally presented. The data observed in this study are of potential interest to nuclear forensic analyses, nuclear sample aging, nuclear energy, plutonium processing, and stockpile stewardship.

36 MATERIALS SCIENCE↗
DOE OSTI2027

A multi-region approach for the analysis of porous materials and gas-solid reactions using USAXS-SAXS-WAXS: application to CaO carbonation

The reaction rate of gas-solid non-catalytic reactions is typically investigated using reactant conversion data over time and ex-situ measurements of the porous solid reactant textural properties; these data enable the experimental estimation of the initial intrinsic reaction rate, whereas the evolution of the textural properties and the reaction rate over time are evaluated theoretically using reaction models. In this work, a different methodology is presented, based on: a) in-situ time-resolved USAXS-SAXS-WAXS measurements of the solid sample, and b) a multi-region modeling approach; this methodology allows for the estimation of the textural properties and the intrinsic reaction rate at any time during the reaction. Referring to a reaction in which a solid product is obtained from a solid reactant and a gaseous reactant, the porous particle is described as consisting of several distinct regions with different microstructural properties, and both a gas-solid reaction model and a SAXS model are derived and applied to the carbonation of porous CaO. The proposed reaction model highlights the role of the inaccessible reactant and accurately predicts the solid reactant conversion versus time profile. The SAXS model accurately predicts the measured linear trends of the Porod invariant and of the pre-factor of the power law scattering profile versus CaO mass fraction; in the absence of chemical reactions, the proposed equations extend classical SAXS theory to porous materials containing macropores and nonporous solid phases in addition to standard nanoscale inhomogeneous regions.

CO2 solid sorbents↗
DOE OSTI2025

In situ multi-tier auto-ignition detection applied to dual-fuel combustion simulations

Here we use an anomaly detection methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in auto-ignition of combustion problems with large numbers of species. Unsupervised anomaly detection is challenging to generalize across problem types and domains. A recent technique, centered on analyzing information in the fourth-order joint moment co-kurtosis, has shown promise, especially for high-dimensional scientific data. In this work we present developments to the co-kurtosis based anomaly detection method needed to make it effective and scalable for large-scale distributed scientific data, such as those generated by massively parallel simulations. An in situ co-kurtosis algorithm is employed as the anomaly detection method for identifying ignition kernels in simulations of turbulent combustion. Here, we extend an existing methodology which identifies regions of the domain where anomalies are present, and add another tier of anomaly detection where the individual samples contributing to the anomaly are identified. We apply this algorithm on-the-fly to a variety of turbulent reacting flow problems and compare it to the widely used (but significantly more expensive) chemical explosive mode analysis (CEMA). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which can be used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Finally, we apply our lightweight in situ algorithm to an exascale high-fidelity simulation with a total of 2.4 Trillion degrees of freedom, performed using an adaptive mesh refinement solver. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Correlating inter-particle forces and particle shape to shear-induced aggregation/fragmentation and rheology for dilute anisotropic particle suspensions: A complementary study via capillary rheometry and in-situ small and ultra-small angle X-ray scattering

Predicting the stability of suspensions composed of anisotropic particles is difficult due to the complexity and interplay of hydrodynamic and colloidal forces. For example, it is not well understood how non-spherical particle shapes affect suspension-rheological responses. This study presents evidence showing how the stacking of boehmite platelets dispersed in aqueous solution leads to moderate shear thinning, even at low boehmite mass fractions. Wide angle X-ray, small angle X-ray, and ultra-small angle X-ray scattering were used to simultaneously monitor changes to the size and fractal dimensions of boehmite aggregates from 6 – 10,000 Å as the sample was recirculated through an in situ capillary rheometer. The latter also provided simultaneous measurements of the viscosity of the suspensions. Computational fluid dynamics modeling of the apparatus was used to provide a more rigorous analysis of the fluid flow. In addition to shear-induced aggregation/fragmentation correlated to a complicated balance between hydrodynamic and colloidal forces, the results indicate that orientationally-dependent interactions, arising from non-spherical boehmite aggregate sub-units, may explain the relatively large experimental viscosity when the hydrodynamic force is small compared to colloidal forces. Ultimately, the results from this study can provide a foundation for future work investigating particle shape effects on colloidal and hydrodynamics forces.

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

Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion

In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.

Infrared imaging↗
DOE OSTI2025

In situ high-temperature Raman spectroscopy for online EAF slag analysis

Real-time monitoring of slag chemistry is critical for optimizing Electric Arc Furnace (EAF) steelmaking operations, where dynamic variations in slag composition directly influence slag foaming, refractory degradation, and thermal efficiency. Conventional techniques such as X-ray fluorescence (XRF), Fourier-transform infrared (FTIR), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are commonly used to analyze slag composition, but their offline nature and equipment constraints limit their applicability for online monitoring in harsh industrial environments. To address this challenge, we present an in situ, high-temperature analytical approach that integrates Raman spectroscopy with a custom designed fiber-optic probe for real-time slag characterization at 1550 °C. The system enables non destructive spectral acquisition from molten slags, providing molecular-level insights into silicate polymerization and iron oxidation states. Eight synthetic slag samples were evaluated, and key Raman features—such as Q n silicate units and FeO₄/FeO₆ coordination environments—were identified and quantitatively correlated with slag basicity and Fe₂O₃ content. The results demonstrate agreement between Raman spectral ratios and bulk slag chemistry, validating the method’s capability to track compositional and structural changes under molten temperature. This work establishes the feasibility of deploying fiber-optic Raman probe for online EAF slag monitoring and highlights their potential to support closed-loop control strategies, thereby enhancing process stability, refractory protection, and steel quality in industrial steelmaking applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Exploring the interfacial structure and crystallinity for direct growth of Mn 3 Sn(0001) on sapphire (0001) by molecular beam epitaxy

The Kagome antiferromagnet Mn 3 Sn has garnered a lot of attention due to the presence of interesting properties such as anomalous Hall effects and Nernst effects. Until now, few papers have been reported to grow using molecular beam epitaxy with a buffer layer or on other substrates. In this paper, we discuss the synthesis of crystalline Mn 3 Sn layers, prepared on Al 2 O 3 (0001) without a buffer layer using molecular beam epitaxy. The growth is monitored in-situ using reflection high energy electron diffraction and measured ex-situ using X-ray diffraction, Rutherford back-scattering, and cross-sectional scanning transmission electron microscopy. The samples were deposited at 524 ± 5 °C, with an Mn: Sn atomic flux ratio of 3.2:1 for 90 minutes. Orientation relationships between the Mn 3 Sn films and the sapphire substrates are determined from in-plane and out-of-plane measurements. Our analysis indicates that the resulting film is predominantly c−plane oriented. Lastly, the samples prepared in this way were found to be discontiguous, showing a 3-dimensional morphology. According to first-principles calculations, the Mn 3 Sn exhibits a displaced Kagome structure in the very first stages of growth, for 2ML and 4ML growth on Al 2 O 3 (0001). This result is corroborated by calculating the surface formation energies and explains the observed RHEED patterns.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Feature Analysis, Tracking, and Data Reduction: An Application to Multiphase Reactor Simulation MFiX-Exa for In-Situ Use Case

As we enter the exascale computing regime, powerful supercomputers continue to produce much higher amounts of data than what can be stored for offline data processing. To utilize such high compute capabilities on these machines, much of the data processing needs to happen in situ, when the full high-resolution data is available at the supercomputer memory. In this article, we discuss our MFiX-Exa simulation, which models multiphase flow by tracking a very large number of particles through the simulation domain. In one of the use cases, the carbon particles interact with air to produce carbon dioxide bubbles from the reactor. These bubbles are of primary interest to the domain experts for these simulations. For this particle-based simulation, we propose a streaming technique that can be deployed in situ to efficiently identify the bubbles, track them over time, and use them to down-sample the data with minimal loss in these features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Removable, Sequestration Coatings for Mitigating Hazardous Contaminants Related to Deactivation and Decommissioning Activities

This project is a collaborative effort between InnoSense LLC (ISL) and Oak Ridge National Laboratory (ORNL) aimed at mitigating the mercury (Hg) contamination problem at the Y-12 National Security Complex (NSC). The report summarizes the research and technology development work of the Phase IIB Project, led by Dr. Uma Sampathkumaran at ISL for the period of July 31, 2017–October 31, 2021. The Y-12 site cleanup includes technologies for safe and cost-effective facility demolition, soil remediation, and on-site treatment and disposal of mercury bearing wastes. One of the technology demonstration goals was a reactive strippable coating for targeted pre-demolition and de-contamination to limit Hg mobilization and the volume of debris requiring treatment for disposal. Toward this, ISL has developed a water-based reactive strippable coating (Trap & See-Hg™) to sequester elemental Hg(0) and mercuric Hg(II) species in the coating. The coating provides multiple benefits including (1) Hg vapor suppression, (2) sealing in the hazardous contaminants, (3) visually locating the contaminant through color changes, and (4) remediating the contaminated surfaces upon coating removal. Through collaboration between ORNL and ISL in Phase II work, the team successfully demonstrated the Trap & See-Hg technology to (1) visually locate the presence of Hg on concrete, soil and drywall surfaces, (2) effectively sequester all mercury species (elemental, soluble and insoluble Hg) from lab-generated and ex-situ soil and brick/rubble samples collected from the Y 12 site with 80 to 95% efficiency to capture/reduce Hg vapor concentration, (3) be applied by manual and robotic spray applicators and cure at room ambient, (4) remain a cohesive and peelable coating upon drying, and (5) sequester Hg0 from walls in the alpha-2 building basement at Y-12 during a small scale in-situ field trial. In Phase IIB, ISL’s effort was focused on refining the coating formulations with additives containing passive and reactive sorbents, colorimetric indicator, and thixotropic agents to achieve the desired performance. The paints were then evaluated at ORNL for applicability to metal and concrete surfaces of different sizes and shapes, and soil rubble, Hg-sorption capacity and achieving sag-free coating on surfaces when applied by a manual spray applicator. Ambient cure of the coating resulted in a dry thickness of 16–20 mil. The coatings were removed from smooth or rough surfaces with low to moderate force. Both lab-generated Hg-contaminated samples and soil and brick/rubble samples containing a range of Hg concentrations (collected from the Y-12 site by UCOR) were used in this project. Optimized formulations were applied to these samples and evaluated for ability to suppress Hg vapors, sequester Hg 0 and Hg(II) species and color mapping of mercury species on the test surface. The ORNL team initially identified three to four potential sites at Y-12 and ORNL for potential demonstration. However, the pandemic closed any possible avenues for on-site demonstration of the robotic spray applicator. The metal and concrete samples with low to high Hg content provided by UCOR from an ongoing demolition were subsequently used for ex-situ sample characterization and analysis at ORNL over a period of 25 days. The coatings suppressed Hg vapors with an efficiency of 95–99% from heavily contaminated metal and 60% from concrete substrate. Analysis of total Hg in the peeled paints indicates sorption capacities ranging from 12 µg Hg/g of paint from concrete to 1.2 mg Hg/g of paint from the metal substrates while peeled paints from soil samples could capture a total Hg from 2.3 mg Hg/g to 66 mg/g paint. Toxicity characteristic leaching procedure (TCLP) tests recovered total mercury leached at 0.15±0.0008 µg/L for concrete to 126.81±0.67 µg/L and 377.25±5.67 µg/L for the two metal substrates, to allow for informed decisions on downstream disposal considerations. Together, this Phase IIB demonstrated the potential value of the Trap & See-Hg for Hg uptake, vapor suppression, and color mapping, and may thus be used to enhance worker safety during site cleanup, facility demolition, and downstream waste disposal at Y-12 NSC.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Ex-situ Heat Treatment of TEM Foils in a Custom Titanium Fixture: A Case Study on Ni-based Superalloy

The advancement of microstructural characterization at the nanoscale is critical to understanding the microstructural evolution and performance of engineering materials. Transmission electron microscopy (TEM) plays a vital role in such investigations, particularly when coupled with controlled ex- and in-situ experiments. In this study, we introduce a novel method for ex-situ heat treatment (HT) of TEM foils using a custom-designed fixture, made from pure titanium, and vacuum furnace to inhibit oxidation. This approach ensures precise temperature control and minimal sample contamination during HT, critical for substructural characterization and analysis of metallic materials. The fixture consists of a titanium base with three slits, for three sample foils, and a titanium cap for closure. The entire assembly is placed in a vacuum heat treatment furnace with high vacuum capability to prevent oxidation. To validate the effectiveness of the setup, precipitation behavior and microstructural changes were studied in an IN725 variant heat-treated at 500°C for 1 hour and 282 variant heat-treated at 700°C for 1 hour, as case studies.

electron microscopy↗