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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Contemporary sources dominate carbonaceous aerosol on the North Slope of Alaska

As the Arctic continues to change and warm rapidly, it is increasingly important to understand the organic carbon (OC) contribution to Arctic aerosol. Biogenic sources of primary and secondary OC in the Arctic will be impacted by climate change, including warming temperatures and earlier snow and ice melt. Here, this study focuses on identifying potential sources and regional influences on the seasonal concentration of organic aerosol through analysis of chemical and isotopic composition. Aerosol samples were collected at two sites on the North Slope of Alaska ($Utqia\dot{g}vik$, UQK, and Oliktok Point, OLK, which is in an Arctic oilfield) over three summers from 2015 to 2017. The elemental carbon (EC) trends at each site were used to understand local combustion influences. Local sources drove EC concentrations at Oliktok Point, where high EC was attributed to oil and gas extraction activity, including diesel combustion emissions. $Utqia\dot{g}vik$ had very low EC in the summer. OC was more similar in concentration and well correlated between the two sites with high contributions of contemporary carbon by radiocarbon apportionment (UQK = 74%, OLK = 63%), which could include both marine and terrestrial sources of contemporary carbon (e.g. primary and secondary biogenic, biomass burning and/or associated SOA, and bioaerosols). OC concentrations are strongly correlated to maximum ambient temperatures on the NSA during the summer, which may have implications for predicting future OC aerosol concentrations in a warming Arctic. Biomass burning was determined to be an episodic influence at both sites, based on interpretation of combined aerosol composition, air mass trajectories, and remote sensing of smoke plumes. The results from this study overall strongly suggests contribution from regional sources of contemporary organic aerosol on the NSA, but additional analysis is needed to better constrain contributions from both biogenic sources (terrestrial and/or marine) and bioaerosol to better understand temperature-related aerosol processes in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Nuclear quantum effects of metal surface-mediated C–H activation

The nuclear quantum effects of surface-mediated C–H activation of surface CH 3 are considered for the pristine Pt(111) and Au(111) surfaces at 300 K. The kinetic barriers without nuclear quantum effects are calculated using both static density functional theory calculations and ab initio molecular dynamics. Static calculations are performed using the harmonic approximation while the free energy pathway is calculated using enhanced sampling molecular dynamics. Machine learning potentials are trained using generated datasets and validated against the ab initio molecular dynamics generated free energy pathways. The machine learning potentials are used to perform centroid molecular dynamics to consider the nuclear quantum effects of C–H activation. Nuclear quantum effects are found to have a very significant effect on the free energy pathway, with reduced importance at higher temperatures and in the CD 3 case.

Bunting, Rhys J. [Lawrence Livermore National Labo↗
DOE OSTI2021

Manipulating meso-scale solvent structure from Pd nanoparticle deposits in deep eutectic solvents

Deep Eutectic Solvents (DESs) are complex solutions that present unique challenges compared to traditional electrolytes. Unlike most electrolytes and ionic liquids, DESs have delicate hydrogen bond networks that are responsible for their highly sensitive compositional dependence on the melting point. Prior work has demonstrated unique nanoscale structure both experimentally and theoretically that brings both challenges and opportunities to their adoption in traditional electrochemical processes. In this study, we use in-situ sample-rotated ultra-small angle X-ray scattering to resolve the near-interface solvent structure after electrodepositing Pd nanoparticles onto a glassy carbon electrode in choline chloride:urea and choline chloride:ethylene glycol DESs. Our results indicate that solvent structure can be observed on the meso-scale in the choline chloride:urea and choline chloride:ethylene glycol systems. Importantly, this extended solvent structure peaks at -0.5 V (vs. Ag/AgCl) and decreases slightly with higher overpotentials during Pd electrodeposition. Experimentally, the nature of this structure is more pronounced in the ethylene glycol system, as evidenced by both the X-ray scattering as well as the electrochemical impedance spectroscopy (EIS). Here, molecular dynamics simulations and dipolar orientation analysis reveal that chloride delocalization near the Pd interface and long-range interactions between the choline and each hydrogen bond donor are very different and qualitatively consistent with the experimental data. These results show how the long-range solvent-deposit interactions can be tuned by changing the hydrogen bond donor in the DES and the applied potential.

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

High spatial resolution charge sensing of quantum Hall states

Charge distribution offers a unique fingerprint of important properties of electronic systems, including dielectric response, charge ordering, and charge fractionalization. Here, we develop an architecture for charge sensing in two-dimensional electronic systems in a strong magnetic field. We probe local change of the chemical potential in a proximitized detector layer using scanning tunneling microscopy, allowing us to infer the chemical potential and the charge profile in the sample. Our technique has both high energy (<0.3 meV) and spatial (<10 nm) resolution exceeding that of previous studies by an order of magnitude. We apply our technique to study the chemical potential of quantum Hall liquids in monolayer graphene under high magnetic fields and their responses to charge impurities. The chemical potential measurement provides a local probe of the thermodynamic gap of quantum Hall ferromagnets and fractional quantum Hall states. The screening charge profile reveals spatially oscillatory response of the quantum Hall liquids to charge impurities and is consistent with the composite Fermi liquid picture close to the half-filling. Our technique also paves the way to map moiré potentials, probe Wigner crystals, and investigate fractional charges in quantum Hall and Chern insulators.

charge sensing↗
DOE OSTI2026

Classical combinatorial optimization scaling for random Ising models on 2D heavy-hex graphs

Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with little or no overhead are used to probe capabilities of quantum algorithms such as the quantum approximate optimization algorithm. In this study, a specific class of near term quantum computing hardware defined combinatorial optimization problems, Ising models on heavy-hex graphs both with and without geometrically local cubic terms, are examined for their classical computational hardness via empirical computation time scaling quantification. Specifically the time-to-solution (TTS) metric using the classical heuristic simulated annealing is measured for finding optimal variable assignments (ground states), as well as the time required for the optimization software Gurobi to find an optimal variable assignment. Because of the sparsity of these Ising models, the classical algorithms are able to find optimal solutions efficiently even for large instances (i.e. 100 000 spin variables). The Ising models both with and without geometrically local cubic terms exhibit average-case linear-time or weakly quadratic scaling when solved exactly using Gurobi, and the Ising models with no cubic terms show evidence of exponential-time TTS scaling when sampled using simulated annealing. These findings point to the necessity of developing and testing more complex, namely more densely connected, optimization problems in order for quantum computing to ever have a practical advantage over classical computing. Our results are another illustration that different classical algorithms can indeed have exponentially different running times, thus making the identification of the best practical classical technique important in any quantum computing vs. classical computing comparison.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Adaptive Machine Learning for Bragg Coherent Diffraction Imaging (BCDI) of 3D Electron Density Maps with Application to La 2-x Ba x CuO 4 (LBCO) High Temperature Superconductor Studies [PowerPoint]

Understanding mesoscale heterogeneity is important for evaluating fatigue and failure of structural materials and for manufacturing processes. Data-driven tools can facilitate in guiding effort investment (measurements and computations) at various stages of the materials development. Our 3D reconstruction approach is to find a set of coefficients which define a bounding surface of the electron density. Data for training the 3D CNN was generated by sampling coefficients from uniform distributions. Test vs. prediction values are shown for the 28 even-valued coefficients for 1000 test structures, with the best and worst performers highlighted. The adaptive part of this work utilized a model independent extremum seeking (ES). The predictions of the CNN were used as the starting point for the ES algorithm. The convergence of the ES algorithm for 3 different structures is illustrated, and the robustness of the adaptive ML approach was demonstrated on experimentally measured 3D crystal from high energy diffraction microscopy. Reconstruction of a non-uniform density volume with different levels of Poisson noise is also shown. Preliminary attempts at reconstructing measured La 2-x Ba x CuO 4 diffraction patterns have so far failed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Modeling DOE Standard Canister Configurations with Updated Surface Chemistry

Road-ready and final disposition packaging configurations for the advanced test reactor (ATR) fuel currently specifies storage within helium backfilled DOE sealed standard canisters. The aluminum cladding of the ATR fuel contains an oxyhydroxide layer of boehmite/bayerite that generates hydrogen when subjected to irradiation. Understanding the effect of this hydrogen buildup over time to important for long term storage considerations. Previous modeling efforts have built a coupled CFD-chemical model to simulate the temperature gas phase concentrations within the DOE sealed standard canisters. These models have coupled the temperature conditions to both the gas phase radiolysis chemistry and the surface chemistry associated with the oxyhydroxide layer. Continued experimental work has identified trends for aluminum surrogate samples with oxyhydroxide layers for tests done at higher dose rates. At low initial doses a fast generation rate of hydrogen occurs which starts to roll over to a lower generation rate as the total dose applied in increased. A previous study created a small-scale chemical model was built to replicate a mini-canister surrogate system at SRNL as well as for the smaller capsule tests performed at INL. The previous iteration of the model utilized step function for its G-values for the hydrogen generation over a 200-year period. This model makes an update to the surface chemistry to account for theorized surface chemistry reactions allowing for oxygen to remain bounded to the oxyhydroxide layer. As additional experiments continue, the kinetic fits may be adjusted to adapt to new data. Three canister configurations are modeled – the base 18-inch, 15-foot DOR standard canister with 30 ATR fuel elements, an 18-inch, 10-foot DOE standard canister with 32 ATR fuel elements and a 24-inch, 10-foot DOE standard canister with 40 ATR fuel elements. In previous reports, the primary sensitivity of the canister conditions was identified as the decay heat of the fuel and the dried condition of the fuel. Only these parameters are studied in the report. For the nominal case with dried fuel, the hydrogen generation is only about 2%, so it is even less than the flammability limit if it were exposed to oxygen. For the densely packed 18-inch case the total hydrogen concentration is only around 4% with the nominal decay heat. For the densely packed 24-inch case the total hydrogen concentration is only 2.5%, roughly 20% higher than the original packing design, and still under flammability conditions if exposed to oxygen.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

The Muon $g-2$ experiment at Fermilab and the First Physics Run

Measurement of the muon anomalous magnetic moment (muon g-2) is a sensitive tool for testing the Standard Model (SM) and searching for new physics. It is an important and complementary tool to probe the high energy frontier. In this talk, I will provide an overview on the Fermilab Muon g-2 experiment, which aims to perform the measurement the muon g-2 with a precision goal of 140 parts per billion, a fourfold improvement over the previous BNL measurement. The first physics run finished in 2018 collecting a data sample with similar size of the BNL measurement. The current experimental status and prospects of the experiment will also be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Microcomputed X-Ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives

Microstructural characterization of composite high explosives (HEs) has become increasingly important over the last several decades in association with the development of high fidelity mesoscale modeling and an improved understanding of ignition and detonation processes. HE microstructure influences not only typical material properties (e.g., thermal, mechanical) but also reactive behavior (e.g., shock sensitivity, detonation wave shape). A detailed nondestructive 3D examination of the microstructure has generally been limited to custom-engineered samples or surrogates due to poor contrast between the composite constituents. Highly loaded (>90 wt%) HE composites such as plastic-bonded explosives (PBX) are especially difficult. Here, we present efforts to improve measurement quality by using single and dual-energy microcomputed X-ray tomography and state-of-the-art image processing techniques to study a broad set of HE materials. Some materials, such as PBX 9502, exhibit suitable contrast and resolution for an automatic segmentation of the HE from the polymer binder and the voids. Other composite HEs had varying levels of success in segmentation. Post-processing techniques that used commercially available algorithms to improve the segmentation quality of PBX 9501 as well as zero-density defects such as cracks and voids could be easily segmented for all samples. Aspects of the materials that lend themselves well to this type of measurement are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Investigation of Nano-Scale Segregation in Nanostructured Ferritic Alloy 14YWT after Heavy Ion Irradiation

Oxide-dispersion-strengthened (ODS) steels, which contain nano-scale Y-Ti-O particles, are being considered for high-temperature radiation environments of nuclear reactors. It is important to accurately characterize the structure of grain boundaries and understand the behavior of segregation at grain boundaries in ODS steels during irradiation. The effect of heavy ion irradiation at 700 °C on Nanostructured Ferritic Alloy 14YWT was investigated using Atom Probe Tomography. Enrichment of Cr occurs at the grain boundaries as well as at nano oxide particle surfaces in the unirradiated sample. The enrichment of Ti and Y at a grain boundary corresponds with Y-Ti-O nano oxide particles with larger size compared to those in the grain, and the Cr enrichment is particularly accentuated at these larger nano oxide particles. The segregation of W occurs at the grain boundaries that are without nano oxide particles. O is segregated at grain boundaries without oxide particles after irradiation. The segregation behavior of Cr, W, Ti, and Y at the grain boundary in the irradiated samples is similar to that in the unirradiated sample. The nano oxide particles embedded in the grain boundary are a primary reason for the increase in Cr segregation at the grain boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2020

Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis

The leaf economic spectrum is a widely-studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of PFTs. We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (SLA, N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (R_d, V_{c,max}, J_max) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, these covariance were an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

biodiversity, ecological modeling, ecology, leaf b↗
DOE OSTI2020

Rheological and microstructural characterization of aqueous suspensions of carbon black and reduced graphene oxide

Carbon black is often used as a conductivity enhancer in battery electrodes. Reduced graphene oxide is another conducting material, with a high aspect ratio sheet-like morphology, and offers the possibility of generating a conducting network at loadings that are small compared to carbon black. Suspensions of conducting carbon, active materials and binders are typically coated on current collectors during the formation of electrodes. The rheology of these suspensions is an important indicator of the connectivity of particles in the suspension and impacts the electrical conductivity as well as the thickness of the coated layer. In this work, the rheology and microstructures of aqueous suspensions of para-aminobenzoic acid terminated carbon black (CB) with and without reduced graphene oxide (RGO) were investigated. The CB loading varied between 0.05 wt.% -10 wt.%, while the samples containing RGO had an RGO loading of 0.05 wt.%. The pH of the suspension was varied from 7.5 to 3. The carboxyl groups on the CB surface are deprotonated at pH 7.5, making the particles hydrophilic. Protonation of the carboxylate groups at pH 3 makes the particles partially hydrophobic. Suspensions of CB showed Newtonian behavior at all loadings for pH 7.5, and at loadings below 1.5 wt.% for pH 3. They are shear thinning at loadings between 1.5–10 wt.%. Interestingly, the low shear viscosity is non-monotonic with CB loading, rising up to a CB loading of 7.5 wt.%, and then dropping at 10 wt.%. Cryogenic scanning electron microscopy (SEM) showed CB particles forming aggregates at pH 3 for CB loadings greater than 1.5 wt.%. At pH 3, addition of 0.05 wt.% RGO resulted in a viscosity increase for suspensions containing 0.5 wt.% CB, as the RGO formed connections with CB particles that enhanced network formation. Adding RGO had little effect on suspensions that had 1.5 wt.% CB. The combination of SEM imaging and suspension rheology provides insight into the particle connectivity for the carbon-based materials studied here. These insights will be useful in determining coating conditions for slurries used in rechargeable battery electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of a Freeze-Dried CRISPR-Cas12 Sensor for Detecting Wolbachia in the Secondary Science Classroom

Training the future synthetic biology workforce requires the opportunity for students to be exposed to biotechnology concepts and activities in secondary education. Detecting Wolbachia bacteria in arthropods using polymerase chain reaction (PCR) has become a common way for secondary students to investigate and apply recombinant DNA technology in the science classroom. Despite this important activity, cutting-edge biotechnologies such as clustered regularly interspaced short palindromic repeat (CRISPR)-based diagnostics have yet to be widely implemented in the classroom. To address this gap, we present a freeze-dried CRISPR-Cas12 sensing reaction to complement traditional recombinant DNA technology education and teach synthetic biology concepts. The reactions accurately detect Wolbachia from arthropod-derived PCR samples in under 2 h and can be stored at room temperature for over a month without appreciable degradation. Here, the reactions are easy-to-use and cost less than $40 to implement for a classroom of 22 students including the cost of reusable equipment. We see these freeze-dried CRISPR-Cas12 reactions as an accessible way to incorporate synthetic biology education into the existing biology curriculum, which will expand biology educational opportunities in science, technology, engineering, and mathematics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗
DOE OSTI2023

Exploring the working range of automated standard dilution analysis of nutrient elements in foods by inductively coupled plasma optical emission spectrometry

Inductively coupled plasma optical emission spectrometry (ICP-OES) is an important tool for measuring nutrient elements in food. ICP-OES methods typically determine analytical concentrations using external standard calibration but can be susceptible to matrix effects. The method of standard additions does not suffer from matrix effects but is time consuming and labor intensive. Automated standard dilution analysis (SDA) allows for online matrix matched calibration without preparing individual standard additions for each sample matrix. This approach may solve both time and matrix issues and has been described in the literature as an attractive alternative to standard additions. The working range of the method for nutrient elements, however, is an understudied feature of SDA that may be a potential drawback to routine analysis of foods. We evaluated automated SDA performance through the analysis of 10 reference materials and four fortified (i.e., spiked) foods spanning the AOAC food triangle. We evaluated the working range, accuracy, and precision for analyses of nutrient elements in foods. Accepted accuracy (80–120% recovery) was achieved for 10 nutrient elements, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, and Zn, when the analytical solution concentration to standard concentration ratio was less than 10. This equates to a working range for each element spanning at least two orders of magnitude. Removing outliers, Z scores (n = 95) ranged from –1.8 to 0.88, and the average recovery (n = 85) from fortification experiments was 97 ± 12% (2σ). Therefore, automated SDA applied to ICP-OES may be used for nutrient elemental analyses in samples with difficult matrices such as foods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗