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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Comprehensive Monte Carlo Framework for Jet-Quenching

This article presents the motivation for developing a comprehensive modeling framework in which different models and parameter inputs can be compared and evaluated for a large range of jet-quenching observables measured in relativistic heavy-ion collisions at RHIC and the LHC. Here, the concept of a framework is discussed within the context of recent efforts by the JET Collaboration, the authors of JEWEL, and the JETSCAPE collaborations. The framework ingredients for each of these approaches is presented with a sample of important results from each. The role of advanced statistical tools in comparing models to data is also discussed, along with the need for a more detailed accounting of correlated errors in experimental results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Mapping tree height in complex terrain of northern China using ultra-high-resolution images

Tree height is a key parameter for estimating forest biomass and carbon sequestration. In recent years, notable progress has been made in mapping tree height using satellite imagery. However, existing tree height products show low accuracy in mountainous and complex terrains, and few studies typically addressed tree height estimations in mountain areas. This study examines the Mentougou district of Beijing, China, characterized by complex terrain and mountainous landscapes. We analyzed two methods for estimating tree height: one using only spectral features and another combining spectral features with topographic factors (elevation, slope, aspect). We used 3-m resolution PlanetScope 8-band multispectral imagery, with 710 field-measured individual tree heights averaged to obtain 471 pixel-level tree height values as ground-truth, to develop tree height prediction models using eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Boosting Machine (GBM) models. The results show that the XGBoost model consistently presented the highest accuracy for both methods evaluated. Specifically, the XGBoost model that combined spectral data with elevation and slope variables with an R² of 0.75 and an RMSE of 2.69 m. Using the XGBoost model, we generated the tree height map for the Mentougou area at 3 m resolution, showing tree heights ranging from 0.5 to 30.4 m, and the model’s prediction error standard deviations ranged from 2.50 to 4.71 m, indicating reliable performance across varied terrain. Additionally, we compared and evaluated the global tree height products, identifying limitations in the accuracy within complex terrains. This study demonstrates the potential for accurately predicting tree heights by combining high-resolution multispectral satellites with a terrain factor modeling approach.

Complex terrain↗
DOE OSTI2025

Multimodal Defect Imaging of Pure Tungsten Components Fabricated via Electron Beam Powder Bed Fusion

The utilization of additive manufacturing (AM) techniques for refractory materials in high-temperature environments has significantly expanded because of the ability to fabricate geometrically complex components. Electron beam powder bed fusion (EB-PBF), which provides lower residual stress, a cleaner vacuum environment, and better efficiency for high melting point, is one of the best-suited AM methods to produce advanced refractory components. However, the property variation attributed to the heterogeneous microstructure and process-induced defects has hindered the widespread adoption of EB-PBF-produced material like tungsten. While numerous in-situ monitoring and defect detection methods have been demonstrated for EB-PBF, a workflow that compares and evaluates process-induced abnormalities from different imaging perspectives is still limited. This study examines a feature-embedded tungsten component manufactured via the EB-PBF process to demonstrate the defect detection capabilities of a multimodal defect imaging workflow. The predefined and process-induced defects are evaluated by harnessing various imaging techniques, including in-situ electron imaging, layerwise near-infrared (NIR) imaging, post-build high-energy x-ray computed tomography (CT), and conventional destructive metallography. The results highlight the strengths and limitations of distinctive defect imaging techniques concerning specific defect types, sizes, and conditions. It was found that electron imaging can provide more abnormal detection capabilities while maintaining a higher measuring accuracy, against the conventional metallography in this case study, compared with NIR and CT imaging techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Tidal Clock Effects of the Lunisolar Gravitational Field and the Earth’s Tidal Deformation

State-of-the-art atomic and optical clocks have the great potential to precisely test fundamental physical assumptions and enhance our understanding of nature. Their widespread applications require us to rigorously deduce the relativistic frequency shift in the framework of general relativity. One interesting question for clocks is tidal field effect which contains variously periodic variations. By introducing tidally deformed Earth, the general algorithms for clock and frequency comparisons are derived. We investigate the effects of external gravitational bodies and Earth’s tidal deformation on the ground- and space-based clocks. The orbital elements of satellite and locations of laboratory are introduced for corresponding clocks, in which the secular and long-period terms and short-period terms are subsequently presented. These effects called tidal clock effects produce the non-negligible contributions in the modern clock experiments and can be directly evaluated from our parameterized formulas. In addition, we also demonstrate for tidal clock effects the position and distance dependences, as well as periodicity. These tidal effects and dependences can provide valuable information for the clock comparison experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Efficient derivative computation for unsteady fatigue-constrained nonlinear aero-structural wind turbine blade optimization

Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especially when fatigue constraints are considered. In this work, we systematically compare and evaluate four differentiation techniques, namely algorithmic differentiation, implicit differentiation, sparsity exploitation, and parallelization, to determine their effectiveness in computing accurate gradients through time-domain aero-structural simulations. By integrating these techniques with unsteady nonlinear aerodynamic and structural models, we develop software designed for accurate gradient computation. We show that combining these techniques addresses memory and runtime challenges associated with long simulations required by design load cases. Specifically, the most effective combination reduces derivative computation wall time by over an order of magnitude compared to finite differencing while maintaining superior accuracy. We demonstrate this approach in a proof-of-concept aero-structural optimization of a wind turbine blade that improves the cost of energy by 12.78 %. This comparative study establishes a viable approach for fatigue-aware blade design that balances computational efficiency with modeling accuracy.

17 WIND ENERGY↗
DOE OSTI2023

Bayesian Parameter Estimation of the k-ω Shear Stress Transport Model for Accurate Simulations of Impinging-Jet Heat Transfer

Given the lack of fusion-relevant component test facilities, current estimates of the thermo-fluid performance of plasma-facing components are based for the most part on numerical simulations. A major source of uncertainty in these simulations is the semiempirical turbulence (closure) models for the Reynolds stresses appearing in the governing Reynolds-averaged Navier-Stokes equations, which involve a set of constants that depend upon the flow. The objective of this study is to evaluate Bayesian parameter estimation of turbulence closure constants in ANSYS Fluent to model heat transfer in impinging jets. The Bayesian statistical calibration produces a probability distribution for these constants from experimental data; the maximum a posteriori estimates are then taken to be the calibrated constants, or parameters. The turbulence model constants are calibrated using an experimental study of a submerged jet of air impinging on a flat heated surface at Reynolds numbers Re = O(10 4 ) and impingement distance in jet diameters H/d = 2. Numerical predictions using the calibrated model parameters are then compared with those generated using the default constants. Predictions obtained with model parameters calibrated on datasets of two different sizes are compared to evaluate the effect of the number of calibration samples. Lastly, the extrapolative ability of the calibrated model is examined by predictions at a Re beyond the calibration values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

3D imaging techniques for characterising microcracks in cement-based materials

Concrete inherently contains pores and microcracks that can adversely impact its mechanical properties and long-term durability. However, characterising microcracks is difficult due to their complex, multiscale and three-dimensional (3D) nature. This paper presents an evaluation of 3D imaging techniques for characterising microcracks induced by different mechanisms. Seven cement pastes, mortars and concretes subjected to drying shrinkage, autogenous shrinkage and freeze-thaw cycles were investigated using focused ion beam nanotomography (FIB-nt), broad ion beam serial section tomography (BIB-SST), laser scanning confocal microscopy (LSCM) combined with serial sectioning and X-ray microtomography (μCT). The study shows that the characteristics of microcracks vary significantly depending on exposure conditions. Yet there is no single technique that can capture the entire size range of microcracks from sub to tens of μm within a sufficiently representative sampling volume. The achievable image volume and resolution, and the advantages and disadvantages of each technique are compared and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ESM data downscaling: a comparison of super-resolution deep learning models

Abstract Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25 $$^{\circ }$$ ∘ ) and coarse-resolution (1 $$^{\circ }$$ ∘ ) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.

Pawar, Nikhil M. (ORCID:0000000211613289)↗
DOE OSTI2021

Watermarks in stream processing systems: semantics and comparative analysis of Apache Flink and Google cloud dataflow

Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-world streams introduces a critical challenge: how does one reason about the completeness of a stream that never ends? In this paper, we present a comprehensive definition and analysis of watermarks, a key tool for reasoning about temporal completeness in infinite streams.First, we describe what watermarks are and why they are important, highlighting how they address a suite of stream processing needs that are poorly served by eventually-consistent approaches:• Computing a single correct answer, as in notifications.• Reasoning about a lack of data, as in dip detection.• Performing non-incremental processing over temporal subsets of an infinite stream, as in statistical anomaly detection with cubic spline models.• Safely and punctually garbage collecting obsolete inputs and intermediate state.• Surfacing a reliable signal of overall pipeline health.Second, we describe, evaluate, and compare the semantically equivalent, but starkly different, watermark implementations in two modern stream processing engines: Apache Flink and Google Cloud Dataflow.

Akidau, Tyler↗
DOE OSTI2021

Impact of soiling on Si and CdTe PV modules: Case study in different Brazil climate zones

Soiling, particulate accumulation on photovoltaic (PV) module surfaces, reduces the available solar resource and the resulting generated device power. This case-study summarizes initial results of 5-year research on the contrasting soiling conditions in the tropical, subtropical, and semi-arid climates in Brazil. A major objective is to present a case study of the effects of soiling on PV module performance in different Brazil climate zones that represent the primary areas for the current and expanding Brazil solar installations. For this, the paper presents methodologies to quantify the soiling ratio (SRatio) and soiling rate (SRate) for two representative commercial technologies, polycrystalline or multicrystalline silicon (mc-Si) and thin-film cadmium telluride (CdTe) modules, through soiling monitoring stations deployed in the selected climate regions. An aim is to add to the growing soiling-research knowledge base through addressing these key factors and their relationships to critical electrical, solar resource, thermal, and local meteorological and environmental parameters. This paper presents, evaluates, and compares soiling rates and losses in Belo Horizonte, Minas Gerais (Equatorial Tropical: 19.92° S, 43.99° W), Porto Alegre, Rio Grande do Sul (Humid-Subtropical: 30.05° S, 51.17° W), and Brotas de Macaúbas, Bahia (Semi-Arid: 12.00° S, 42.63° W). The results show that soiling is moderate in all 3-regions, for example with 0.1%/day < SRate < 0.2%/day for Belo Horizonte. Precipitation dominates the cleaning of the modules in the summertime in this climate zone, while it is the major factor year-round in Rio Grande do Sul. Wind is the major issue mitigating the soiling accumulation for the Bahia installation. The methodology incorporates several key refinements, including the normalization and adjustment for the meteorological parameters (temperature, irradiance, wind, precipitation). The evaluations include the region-specific differing effects of non-uniform soiling, natural cleaning, and ambient temperatures.

14 SOLAR ENERGY↗
DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part II: Risk Assessment

This comprehensive report explores the intricate interplay through “sector coupling” between the Transportation Sector, Energy Sector, and the critical infrastructure challenges faced by the United States. Aligned with the ambitious federal goal of reducing greenhouse gas emissions, the report analyzes the present vulnerabilities in the fossil fuel supply chain, highlighting instances where the dependence on this industry has been susceptible to local and global disruptions. With a focus on risk assessment, this report establishes a framework to compare the fossil fuel-based transportation sector with the burgeoning electrified sector, considering the evolving technologies in electric vehicles (EVs) and the nascent charging infrastructure. This study delves into the historical reliability of fossil fuel-based transportation and identifies opportunities for strengthening sector coupling between transportation and the electric sector. Assessments of the gasoline-diesel supply chain provide insights into both opportunities and threats, offering a roadmap for creating a more reliable and resilient electrified transportation system. We modeled sectoral coupling scenarios for the current internal combustion engine (ICE)-based and future EV-dominant landscapes, leveraging qualitative sector attributes to identify relative strengths and weaknesses in Part 1 of the report. In this report (Part 2) we continue to understand the role of the electricity sector on transportation and develop a semi-quantitative methodology is employed to evaluate and compare the risks associated with the fossil fueled and electric transportation sector during emergency scenarios, drawing from real-world events like hurricanes and geopolitical disruptions. Different operational scenarios for the fossil fuel and electrified sectors have been developed to understand the risks associated with them. This report concludes with recommendations for the architectural designs of electric charging infrastructure, underlining the need for informed decisions in the transition towards a sustainable and resilient transportation future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Techno-Economic Analysis of Synthetic Fuels Pathways Integrated with Light Water Reactors

The purpose of this work is to identify, model, and compare two possible synfuel production routes utilizing CO 2 as the feedstock. Heat from an LWR nuclear plant is integrated wherever possible as a means to positively affect the economics of the LWR. Process and economic modeling for a conceptual synfuel production plant co-located (or in near proximity) with an LWR is presented, including the cost of CO 2 captured from an ethanol plant, compressed, and transported to the LWR hybrid plant, co-electrolysis of the CO 2 with water in a solid oxide electrolyzing cell (SOEC) system to produce syngas, and thermo-catalytic conversion of the syngas to transportation fuel. The hybrid LWR/synfuels plant is assumed to be located within 50-150 miles of an ethanol plant, e.g., located in the Midwest region of the United States. Performance and nth plant economics for the co-electrolysis-based processes are evaluated and compared with biomass-gasification-based technology for the synfuel routes considered. Sensitivity analysis around the price of CO 2 and electricity, two of the major cost drivers, is presented for each case. Consideration of a carbon credit is also included in the sensitivity analysis.

08 HYDROGEN↗
DOE OSTI2025

Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modelled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using NSGA-III. The recovered Pareto-fronts were analyzed across uncertainty levels: in the moderator problem, normalized hypervolume dropped from 0.886 at 1.0% uncertainty to 0.748 at 10% uncertainty, while in the converter problem it remained near 0.50 for all cases. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected—low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can recover the Pareto-front under noisy conditions, and multi-fidelity studies help identify suitable uncertainty levels for each problem to balance efficiency and accuracy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Techno-Economic Analysis of Synthetic Fuels Pathways Integrated with Light Water Reactors

The purpose of this work is to identify, model, perform techno-economic analysis, and compare two possible synfuel production routes utilizing CO 2 as the feedstock. Heat from an LWR nuclear plant is integrated wherever possible to positively affect the economics of the LWR by converting power to fuels during times of low grid electricity demand. Process and economic modeling for a conceptual synfuel production plant co-located (or in near proximity) with an LWR is presented, including the cost of CO 2 captured from an ethanol plant, compressed, and transported to the LWR hybrid plant, co-electrolysis of the CO 2 with water in a solid oxide electrolyzing cell (SOEC) system to produce syngas, and thermocatalytic conversion of the syngas to transportation fuel. The hybrid LWR/synfuels plant is assumed to be located within 50–150 miles of an ethanol plant (e.g., located in the midwest region of the United States). Performance and nth-plant economics for the co-electrolysis-based processes are evaluated and compared with biomass-gasification-based technology for the synfuel routes considered. Sensitivity analysis around the price of CO 2 and electricity, two of the major cost drivers, is presented for each case. Consideration of a carbon credit is also included in the sensitivity analysis.

10 SYNTHETIC FUELS↗
DOE OSTI2021

Open-Loop Control of Adjustable Tuned Mass Dampers for Floating Wind Turbine Platforms: Preprint

Floating offshore wind turbines have several advantages over their land-based counterparts, including access to stronger, more consistent winds and proximity to population centers. However, increased costs from the platform have been a challenge inhibiting their widespread adoption. New damping technologies with tunable target frequencies and damping ratios promise a greater degree of control over platform motions, allowing hulls to be designed smaller and reducing the overall cost of the turbines. In this work, a control strategy is proposed for these adjustable tuned mass dampers (TMDs). A frequency-domain model of the system is developed, from which response amplitude operators (RAOs) for the platform's rigid-body modes can be obtained for different natural frequencies and damping ratios of the dampers. Using these RAOs, and JONSWAP spectrums of operational and extreme sea states, the performance of the various damper settings are compared by evaluating the standard deviation of a rigid-body motion of interest (platform pitch or heave). By finding the optimal damper setting for a range of representative design load cases (DLCs), a lookup function is made to return the damper target frequency given the peak period of the sea state. To determine the current sea state, we propose a simple estimator that uses wave measurements (e.g., from a measurement buoy) to determine the peak period and significant wave height. The significant wave height is determined using the statistics of the past 100 seconds of wave elevation information and the peak period is computed using a frequency locked loop. Initial results show that we can use these estimated values to control the TMD natural frequency with an open loop controller. The controlled system is tested in a range of environmental conditions determined by the International Electrotechnical Commission design load cases (DLCs), which include normal and extreme wind and wave models. In these tests, we compare the performance of 4 cases: (1) no TMD, (2) a constant TMD based on the worst case DLC, (3) a controlled TMD based on known wind and wave environments, and (4) a real-time controlled TMD using estimated wind and wave environments. The effect of hull-based TMD control is also compared to changes in traditional wind turbine control via blade pitch.

control↗
DOE OSTI2022

Determination of transition metal ions in fossil fuel associated wastewaters using chelation ion chromatography

Here, this study outlines the development and subsequent validation of a method using chelation ion chromatography (CIC) pretreatment followed by traditional ion chromatography (IC) and post column UV/vis detection to measure transition metals in fossil fuel wastewaters, such as oil &amp; gas (O&G) brines and coal mine drainage (CMD) waters. Measurement of transition metals is often an important characterization step in the research of environmental and energy systems. IC represents one way to measure these metals with the advantages of being versatile, simple and relatively low cost compared to other analytical methods. However, high concentrations of alkali and alkaline earth metals present in fossil fuel wastewaters will decrease IC detectability of transition metals in these waters. In this study, a CIC method was developed for the analysis of transition metal ions (Fe 3+ , Cu 2+ , Ni 2+ , Zn 2+ , Co 2+ , Mn 2+ , and Fe 2+ ) in fossil fuel associated wastewaters such as Appalachian CMD and O&G wastewaters from the Permian and Bakken shale basins in the United States. CIC system incorporated an on-line chelator column (e.g., the MetPac CC-1) with high selectivity for transition metals over alkali and alkaline earth metals for salt matrix removal prior to transition metal separation and detection. Additional method developments also included acidifying all samples to 2% v/v HCl and using gradient elution rather than isocratic. The recoverability of transition metals in simple salt solutions commonly found in CMD and brine samples (e.g. NaCl, Na 2 SO 4 , CaCl 2 ) using CIC was evaluated and compared to that using traditional IC. Our results found that the CIC system significantly improved transition metal recoveries for samples in 10,000 mg/L CaCl 2 matrix, reaching 87%-108% recovery for all analytes, as opposed to 2-323% recovery in traditional IC. The limits of detection in this study achieved 10.09 – 161.2 μg/L, comparable to reported values in similar IC studies. The developed method was also verified with certified water samples, resulting in 89% - 111% recoveries in samples with higher analyte concentrations (i.e. >4x the LoDs). The developed method achieved 87% -112% recoveries for most analytes in CMD samples and 72%-138% recoveries for Bakken shale samples, relative to ICP-MS values. Overall, the current IC method can be a very good screening tool for fast and cheap analysis for transition metals at mg/L level, to facilitate selection of samples for more detailed ICP-MS analysis.

01 COAL, LIGNITE, AND PEAT↗