SEARCH · Engineering Papers
Results for “quantitative analysis”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models
The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.
Quantitative analysis of EXAFS data sets using deep reinforcement learning
Not provided.
Quantitative analysis of hydrogen sites and occupancy in deep mantle hydrous wadsleyite using single crystal neutron diffraction
Evidence from seismological and mineralogical studies increasingly indicates that water from the oceans has been transported to the deep earth to form water-bearing dense mantle minerals. Wadsleyite [(Mg, Fe2+)2SiO4] has been identified as one of the most important host minerals incorporating this type of water, which is capable of storing the entire mass of the oceans as a hidden reservoir. To understand the effects of such water on the physical properties and chemical evolution of Earth’s interior, it is essential to determine where in the crystal structure the hydration occurs and which chemical bonds are altered and weakened after hydration. Here, we conduct a neutron time-of-flight single-crystal Laue diffraction study on hydrous wadsleyite. Single crystals were grown under pressure to a size suitable for the experiment and with physical qualities representative of wet, deep mantle conditions. The results of this neutron single crystal diffraction study unambiguously demonstrate the method of hydrogen incorporation into the wadsleyite, which is qualitatively different from that of its denser polymorph, ringwoodite, in the wet mantle. The difference is a vital clue towards understanding why these dense mantle minerals show distinctly different softening behaviours after hydration.
Development and testing of a novel micro-Raman probe and application of calibration transfer method for the quantitative analysis of microfluidic nitric acid streams
To simplify and improve the safety of reprocessing used nuclear fuel, an initial assessment was made of Raman microscopy applied to microfluidic volumes with a view toward the on-line spectroscopic measurement of highly radioactive solutions. This study compares a microscopic Raman probe (excitation focal point diameter 70 µm) to a larger, well studied probe (excitation focal point diameter 125 µm) used in prior investigations. This was done by chemometrically modeling and predicting concentrations of HNO3 solutions (0 M to 8 M) as they flowed through microfluidic cells based upon spectra from each probe. Spectra recorded for each probe using the same static HNO3 solution set contained in 2 dram glass vials were used as training sets to produce models for the respective probes. Modeling required baseline, normalization and smoothing preprocessing to compensate for a reduced path length between the static glass vial training set (4 cm) and the reduced path length flow cell (1 cm), wide ranging solution concentrations, and the associated non-linear spectral changes, and abrupt and uneven concentration changes of flowing solutions. The micro-Raman probe is able to produce spectra that may be analyzed chemometrically to accurately predict the concentration of flowing HNO3 solutions down to microliter volumes. Based upon RMSECV and RMSEP modeling statistics concentration predictions of the micro-Raman probe are comparable to those obtained for a macro-Raman on identical samples.
Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning
Here, we present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.
A quantitative analysis of weak intermolecular interactions & quantum chemical calculations (DFT) of novel chalcone derivatives
The novel chalcone derivatives have widespread applications in material science and medicinal industries. The density functional theory (DFT) is used to optimized the molecular structure of the three chalcone derivatives (M-I, II, III). The observed discrepancies between the theoretical and experimental (X-ray data) results attributed to different environments of the molecules, the experimental values are of the molecule in solid state there by subjected to the intermolecular forces, like non-bonded hydrogen bond interactions, where as isolated state in gas phase for theoretical studies. The lattice energy of all the molecules have been calculated using PIXELC module in Coulomb –London –Pauli (CLP) package and is partitioned into corresponding coulombic, polarization, dispersion and repulsion contributions. Lattice energy data confirm and strengthen the finding of the X-ray results that the weak but significant intermolecular interactions like C-H…O, Π- Π and C-H… Π plays an important role in the stabilization of crystal packing.
Quantitative analysis of hydrogen in SiO{sub 2}/SiN/SiO{sub 2} stacks using atom probe tomography
We have demonstrated that it is possible to reproducibly quantify hydrogen concentration in the SiN layer of a SiO{sub 2}/SiN/SiO{sub 2} (ONO) stack structure using ultraviolet laser-assisted atom probe tomography (APT). The concentration of hydrogen atoms detected using APT increased gradually during the analysis, which could be explained by the effect of hydrogen adsorption from residual gas in the vacuum chamber onto the specimen surface. The amount of adsorbed hydrogen in the SiN layer was estimated by analyzing another SiN layer with an extremely low hydrogen concentration (<0.2 at. %). Thus, by subtracting the concentration of adsorbed hydrogen, the actual hydrogen concentration in the SiN layer was quantified as approximately 1.0 at. %. This result was consistent with that obtained by elastic recoil detection analysis (ERDA), which confirmed the accuracy of the APT quantification. The present results indicate that APT enables the imaging of the three-dimensional distribution of hydrogen atoms in actual devices at a sub-nanometer scale.
Quantitative analysis of total reflection X-ray fluorescence from finely layered structures using XeRay
Total reflection x-ray fluorescence (TXRF) is a widely applicable experimental technique for studying chemical element distributions across finely layered structures at extremely high sensitivity. To promote and facilitate scientific discovery using TXRF, we developed a MATLAB-based software package with a graphical user interface, named XeRay, for quick, accurate, and intuitive data analysis. XeRay lets the user model any layered system, each layer with its independent chemical composition and thickness, and enables fine-tuned data fitting. The accuracy of XeRay has been tested in the analysis of TXRF data from both air/liquid interface and liquid/liquid interfacial studies and has been compared to literature results. In an air/liquid interface study, Ca 2+ sequestration was measured at a Langmuir monolayer of 1-stearoyl-2-oleoyl-sn-glycero-3-phosphatidic acid (SOPA) on a buffer solution of 1 mM CaCl2 at pH 7.5. Data analysis with XeRay reveals that each 1 nm2 of interfacial area contains 2.38 ± 0.06 Ca 2+ ions, which corresponds to a 1:1 ratio between SOPA headgroups and Ca 2+ ions, consistent with several earlier reports. For the liquid/liquid interface study of Sr 2+ enrichment at the dodecane/surfactant/water interface, analysis using XeRay gives a surface enrichment of Sr 2+ at $68^{+6}_{-5}$ Å 2 per ion, consistent with the result published for the same dataset.
Quantitative analysis of leakage current in III-nitride micro-light-emitting diodes
In this study, the electrical characteristics under forward- and reverse-bias conditions of III-nitride blue and green micro-light-emitting diodes (μLEDs) are analyzed. A fitting model is proposed to determine the contributions of reverse leakage current and the effectiveness of sidewall treatments. Moreover, the forward-bias currents of the μLEDs are examined using the extracted ideality factor to examine the impacts of sidewall defects. The results show that sidewall treatments are highly effective for suppression of leakage currents. From the efficiency perspective, higher wall-plug efficiency (WPE) than external quantum efficiency (EQE) is observed when the operating voltage is lower than the photon voltage in both blue and green 20 × 20 μm 2 devices. This enhancement of the WPE over the EQE is due to the suppression of Shockley–Read–Hall (SRH) nonradiative recombination. These observations indicate that μLEDs with sidewall treatments not only improve optical performance but also further enhance the electrical performance of devices by suppressing the leakage current paths due to SRH nonradiative recombination processes.
Quantitative analysis of cost savings and occupants’ preferences in grid-interactive smart home operation
Many utility companies in the United States have introduced time-of-use (TOU) rates for homeowners with the goal of regulating electricity consumption during peak hours. The electrical appliances in homes include various thermostatically controlled devices, such as air conditioners (AC) for thermal comfort, and nonthermostatically controlled devices such as clothes washers. As a result, homeowners face the complicated challenge of economically operating multiple electrical appliances in their homes while maintaining comfort and convenience. This is usually due to the lack of an explicit understanding of the correlation between cost saving and the users’ comfort. To understand the correlation, this article is designed to construct a framework by integrating three major components: a multi-objective optimization method accommodating multiple competing goals with different weights, a learning-based system modeling approach describing the dynamics and thermal coupling effects of appliances, and a novel comfort index method differentiating preferred and acceptable thermal comfort. Our proposed framework can allow the indoor air temperature to fall into the "preferred" range with a marginal cost increase. Furthermore, the simulation result shows that an additional 8 h for the preferred thermal comfort can be achieved with a cost increase of only 1.77%.
Exploring the environmental drivers of vegetation seasonality changes in the northern extratropical latitudes: a quantitative analysis *
Abstract Vegetation seasonality in the northern extratropical latitudes (NEL) has changed dramatically, but our understanding of how it responds to climate change (e.g. temperature, soil moisture, shortwave radiation) and human activities (e.g. elevated CO 2 concentration) remains insufficient. In this study, we used two remote-sensing-based leaf area index and factorial simulations from the TRENDY models to attribute the changes in the integrated vegetation seasonality index ( S ), which captures both the concentration and magnitude of vegetation growth throughout the year, to climate, CO 2 , and land use and land cover change (LULCC). We found that from 2003 to 2020, the enhanced average S in the NEL (MODIS: 0.0022 yr −1 , p < 0.05; GLOBMAP: 0.0018 yr −1 , p < 0.05; TRENDY S3 [i.e. the scenario considering both time-varying climate, CO 2 , and LULCC]: 0.0011 ± 7.5174 × 10 −4 yr −1 , p < 0.05) was primarily determined by the elevated CO 2 concentration (5.3 × 10 −4 ± 3.8 × 10 −4 yr −1 , p < 0.05) and secondly controlled by the combined climate change (4.6 × 10 −4 ± 6.6 × 10 −4 yr −1 , p > 0.1). Geographically, negative trends in the vegetation growth concentration were dominated by climate change (31.4%), while both climate change (47.9%) and CO 2 (31.9%) contributed to the enhanced magnitude of vegetation growth. Furthermore, around 60% of the study areas showed that simulated major climatic drivers of S variability exhibited the same dominant factor as observed in either the MODIS or GLOBMAP data. Our research emphasizes the crucial connection between environmental factors and vegetation seasonality, providing valuable insights for policymakers and land managers in developing sustainable ecosystem management strategies amidst a changing climate.
Quantitative analysis of zonal flow influence on turbulent plasmas driven by trapped electrons
The role of zonal flow (ZF) in the turbulence saturation of trapped electron modes (TEM) in magnetically confined plasmas is revisited. Here, in this study, we examine ZF excitation and saturation mechanisms in TEM turbulence using detailed free-energy transfer diagnostics from nonlinear gyrokinetic simulations. When the ion channel becomes subdominant, electrons shift to transferring energy to the zonal component, which results in zonal flows always playing a significant though subdominant role, in the case of the temperature gradient driven TEM. The velocity-space structure of energy transfer reveals that trapped electrons directly couple to ZF. Regarding the saturation physics, the zonal flow advection, drift-wave–drift-wave interactions, and stable modes at the pump waves are compared. When ZF saturation is weak, drift-wave–drift-wave interactions play a larger role in maintaining turbulence saturation. The findings elucidate the roles of nonzonal to zonal flow coupling, stable modes, and nonzonal wave-wave interactions, and provide a basis for improving reduced models of turbulent transport.
Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data
Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. Here, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feedforward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.
Quantitative analysis of dislocations in 4H-SiC wafers using synchrotron X-ray topography with ultra-high angular resolution
Utilization of an Si(331) beam conditioner together with an Si(111) double-crystal monochromator (DCM) enables the angular resolution of synchrotron X-ray topography to be increased by an order of magnitude compared with grazing-incidence topography or back-reflection topography conducted with the DCM alone. This improved technique with extremely small beam divergence is referred to as synchrotron X-ray plane-wave topography (SXPWT). This study demonstrates that the rocking curve width of 4H-SiC 0008 in PWT is only 2.5" and thus the lattice distortion at the scale of 1" will significantly affect the diffracted intensity. This work reports the ultra-high angular resolution in SXPWT which enables detailed probing of the lattice distortion outside the dislocation core in 4H-SiC, where the sign of the Burgers vector can be readily determined through comparison with ray-tracing simulations.
Quantitative analysis of three-dimensional fatigue crack path selection in Mg alloy WE43 using high-energy X-ray diffraction microscopy
Fatigue short-cracks in Mg alloys display complex growth behavior due to high plastic anisotropy and crack path dependence on local microstructural features. In this study, the three-dimensional crystallography of short-crack paths in Mg alloy WE43 was characterized by mapping near-field high-energy X-ray diffraction microscopy (HEDM) reconstructed grain maps to high-resolution X-ray CT reconstructions of the fracture surfaces in the crack initiation and short-crack growth regions of six ultrasonic fatigue specimens. Crack–grain–boundary intersections were analyzed at 81 locations across the six crack paths. The basal intragranular, non-basal intragranular, or intergranular character of short-crack growth following each boundary intersection was correlated to crystallographic and geometric parameters of the trailing and leading grains, three-dimensional grain boundary plane, and advancing crack front. The results indicate that crack paths are dependent on the combined crystallographic and geometric character of the local microstructure, and crack path prediction can be improved by use of dimensionality reduction on subsets of high-linear-correlation microstructural parameters.