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NASA NTRS1984

Prediction of the space adaptation syndrome

The univariate and multivariate relationships of provocative measures used to produce motion sickness symptoms were described. Normative subjects were used to develop and cross-validate sets of linear equations that optimally predict motion sickness in parabolic flights. The possibility of reducing the number of measurements required for prediction was assessed. After describing the variables verbally and statistically for 159 subjects, a factor analysis of 27 variables was completed to improve understanding of the relationships between variables and to reduce the number of measures for prediction purposes. The results of this analysis show that none of variables are significantly related to the responses to parabolic flights. A set of variables was selected to predict responses to KC-135 flights. A series of discriminant analyses were completed. Results indicate that low, moderate, or severe susceptibility could be correctly predicted 64 percent and 53 percent of the time on original and cross-validation samples, respectively. Both the factor analysis and the discriminant analysis provided no basis for reducing the number of tests.

Reschke, M. F.↗

DOE OSTI2025

Advanced Method Optimization with Categorical and Constrained Continuous Parameters

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space. Previous work defined and implemented a generalized technique for method optimization for continuous method parameters, but a knowledge gap remains for the incorporation of categorical variables into these advanced method optimization schemes. This work presents and validates a generalized optimization approach that incorporates both continuous and categorical variables while also utilizing a multivariate, multiobjective optimization scheme with Karush–Kuhn–Tucker conditions to bound the optimization space to solutions within the physical limitations of the parameter space. Method optimization from a case study using GC–MS for the analysis of 11 analytical standards with objectives to minimize peak width and maximize peak height resulted in a 3 orders of magnitude improvement in the average peak height and a 2 orders of magnitude improvement in the average peak width compared to the least optimal (but reasonable) instrumental parameters utilized in this study. This approach to optimization allows for a customizable method optimization in which users can include both continuous and categorical variables to achieve objectives specific to their analytical goals. This approach significantly reduces the labor and cost associated with traditional method development approaches and can be applied in a variety of scientific fields across a range of laboratory techniques (e.g., instrument method development, sample preparation, and extraction techniques).

Amorphous materials↗

DOE OSTI2023

Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Left–right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case–control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case–control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case–control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case–control status. Subtle case–control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description

A novel framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Furthermore, using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is demonstrated that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

58 GEOSCIENCES↗

DOE OSTI2019

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these diculties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such diculties, we developed a new data-driven approach to construct orthonormal polynomials for polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The eectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems within high-dimensional conformational space (O(10)) where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Lei, Huan↗

NASA NTRS2014

Tropospheric Ozone Increases over the Southern Africa Region: Bellwether for Rapid Growth in Southern Hemisphere Pollution?

Increases in free-tropospheric (FT) ozone based on ozonesonde records from the early 1990s through 2008 over two subtropical stations, Irene (near Pretoria, South Africa) and Réunion (21 deg. S, 55 deg. E; approx. 2800 km NE of Irene in the Indian Ocean), have been reported. Over Irene a large increase in the urban-influenced boundary layer (BL, 1.5-4 km) was also observed during the 18-year period, equivalent to 30%decade−1. Here we show that the Irene BL trend is at least partly due to a gradual change in the sonde launch times from early morning to the midday period. The FT ozone profiles over Irene in 1990-2007 are re-examined, filling in a 1995-1999 gap with ozone profiles taken during the Measurements of Ozone by Airbus In-service Aircraft (MOZAIC) project over nearby Johannesburg. A multivariate regression model that accounts for the annual ozone cycle, El Niño-Southern Oscillation (ENSO) and possible tropopause changes was applied to monthly averaged Irene data from 4 to 11 km and to 1992-2011 Réunion sonde data from 4 to 15 km. Statistically significant trends appear predominantly in the middle and upper troposphere (UT; 4-11 km over Irene, 4-15 km over Réunion) in winter (June-August), with increases 1 ppbv yr(exp. −1) over Irene and approx. 2 ppbv yr(exp. −1) over Réunion. These changes are equivalent to approx. 25 and 35-45%decade( exp. −1), respectively. Both stations also display smaller positive trends in summer, with a 45%decade(exp. −1) ozone increase near the tropopause over Réunion in December. To explain the ozone increases, we investigated a time series of dynamical markers, e.g., potential vorticity (PV) at 330-350 K. PV affects UT ozone over Irene in November-December but displays little relationship with ozone over Réunion. A more likely reason for wintertime FT ozone increases over Irene and Réunion appears to be long-range transport of growing pollution in the Southern Hemisphere. The ozone increases are consistent with trajectory origins of air parcels sampled by the sondes and with recent NOx emissions trends estimated for Africa, South America and Madagascar. For Réunion trajectories also point to sources from the eastern Indian Ocean and Asia.

Trends↗

NASA NTRS1978

Analysis of vector wind change with respect to time for Cape Kennedy, Florida

Multivariate analysis was used to determine the joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from 15 years of twice-daily rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, that the joint distribution of wind component change with respect to time is univariate normal, that the joint distribution of wind component changes is bivariate normal, and that the modulus of vector wind change is Rayleigh are tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from 1 to 5 hours, calculated from Jimsphere data, are presented. Extension of the theoretical prediction (based on rawinsonde data) of wind component change standard deviation to time periods of 1 to 5 hours falls (with a few exceptions) within the 95 percentile confidence band of the population estimate obtained from the Jimsphere sample data. The joint distributions of wind change components, conditional wind components, and 1 km vector wind shear change components are illustrated by probability ellipses at the 95 percentile level.

Adelfang, S. I.↗

DOE OSTI2018

Large-Scale Environmental Characteristics of MJOs that Strengthen and Weaken over the Maritime Continent

The characteristics of Madden-Julian Oscillation (MJO) events that strengthen and weaken over the Maritime Continent (MC) are examined using long-term satellite observations and reanalysis datasets. The MJO weakens twice as often as it strengthens over the MC, with weakening MJOs being twice as likely during El Niño years compared to La Niña years and the reverse being true for MJO strengthening events. Weakening MJOs are characterized by large positive sea-surface temperature (SST) and moist-static energy (MSE) anomalies in the southern hemisphere (SH) of the MC compared to MJO strengthening events. These anomalies correspond to more precipitation and stronger upper-level divergence in the SH compared to the NH. The north-south polarity in these variables reflects the seasonal cycle in MJO weakening over the MC. MJOs that approach the MC during the NH winter typically weaken. There is a window during the NH summer and fall in which the MJO almost always strengthens over the MC. This seasonal cycle corresponds to whether the MJO crosses the MC primarily north or south of the equator. The MJO encounters more and taller MC islands when crossing in the SH, which previous results suggest is the likely cause of the frequent weakening. The finding of a seasonal cycle in MJO weakening over the MC complements and extends previous work on the topic and highlights the importance of sampling MJOs throughout the year in future field campaigns designed to study MJO-MC interactions.

Madden-Julian Oscillation, Maritime Continent, sat↗

DOE OSTI2023

Contraceptive Sabotage and Contraceptive Use at the Time of Pregnancy: An Analysis of People with a Recent Live Birth in the United States

Contraceptive sabotage and other forms of intimate partner violence (IPV) can interfere with contraceptive use. We used 2012 to 2015 Pregnancy Risk Assessment Monitoring System data from 8,981 people residing in five states who reported that when they became pregnant, they were not trying to get pregnant. We assessed the relationships between ever experiencing contraceptive sabotage and physical IPV 12 months before pregnancy (both by the current partner) and contraceptive use at the time of pregnancy using multivariable logistic regression. We also assessed the joint associations between physical IPV 12 months before pregnancy and ever experienced contraceptive sabotage with contraceptive use at the time of pregnancy. Few people ever experienced contraceptive sabotage (1.8%; 95% confidence interval [CI]: 1.4, 2.3) or physical IPV 12 months before pregnancy (2.8%; 95% CI: 2.3, 3.3). In models adjusted for age, race/ethnicity, marital status, education, and state of residence, ever experiencing contraceptive sabotage was associated with contraceptive use at the time of pregnancy (adjusted odds ratio [aOR]: 1.73; 95% CI: 1.06, 2.82), but not with physical IPV 12 months before pregnancy (aOR: 0.69; 95% CI: 0.46, 1.02). When examining the joint association, compared to not ever experiencing contraceptive sabotage or physical IPV 12 months before pregnancy, ever experiencing contraceptive sabotage was significantly related to contraceptive use at the time of pregnancy (aOR: 1.72; 95% CI: 1.00, 2.95). However, it was not associated with experiencing physical IPV 12 months before pregnancy (aOR: 0.68; 95% CI: 0.45, 1.04) or with experiencing both contraceptive sabotage and physical IPV 12 months before pregnancy (aOR: 1.21; 95% CI: 0.42, 3.50), compared to not ever experiencing contraceptive sabotage or physical IPV 12 months before pregnancy. Our study highlights that current partner contraceptive sabotage may motivate those not trying to get pregnant to use contraception; however, all people in our sample still experienced a pregnancy.

Huber-Krum, Sarah↗

NASA NTRS2003

Investigation of electrolyte measurement in diluted whole blood using spectroscopic and chemometric methods

The feasibility of using near-infrared (NIR) spectroscopy in combination with partial least-squares (PLS) regression was explored to measure electrolyte concentration in whole blood samples. Spectra were collected from diluted blood samples containing randomized, clinically relevant concentrations of Na+, K+, and Ca2+. Sodium was also studied in lysed blood. Reference measurements were made from the same samples using a standard clinical chemistry instrument. Partial least squares (PLS) was used to develop calibration models for each ion with acceptable results (Na+, R2 = 0.86, CVSEP = 9.5 mmol/L; K+, R2 = 0.54, CVSEP = 1.4 mmol/L; Ca2+, R2 = 0.56, CVSEP = 0.18 mmol/L). Slightly improved results were obtained using a narrower wavelength region (470-925 nm) where hemoglobin, but not water, absorbed indicating that ionic interaction with hemoglobin is as effective as water in causing measurable spectral variation. Good models were also achieved for sodium in lysed blood, illustrating that cell swelling, which is correlated with sodium concentration, is not required for calibration model development.

Non-NASA Center↗

DOE OSTI2020

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

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

47 OTHER INSTRUMENTATION↗

NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗

NASA NTRS2012

The Validity Chlorophyll-a Estimation by Sun Induced Fluorescence in Estuarine Waters: An Analysis of Long-term (2003-2011) Water Quality Data from Tampa Bay, Florida (USA)

Satellite observation of phytoplankton concentration or chlorophyll-a is an important characteristic, critically integral to monitoring coastal water quality. However, the optical properties of estuarine and coastal waters are highly variable and complex and pose a great challenge for accurate analysis. Constituents such as suspended solids and dissolved organic matter and the overlapping and uncorrelated absorptions in the blue region of the spectrum renders the blue-green ratio algorithms for estimating chlorophyll-a inaccurate. Measurement of sun-induced chlorophyll fluorescence, on the other hand, which utilizes the near infrared portion of the electromagnetic spectrum, may provide a better estimate of phytoplankton concentrations. While modelling and laboratory studies have illustrated both the utility and limitations of satellite baseline algorithms based on the sun induced chlorophyll fluorescence signal, few have examined the empirical validity of these algorithms using a comprehensive long term in situ data set. In an unprecedented analysis of a long term (2003-2011) in situ monitoring data from Tampa Bay, Florida (USA), we assess the validity of the FLH product from the Moderate Resolution Imaging Spectrometer (MODIS) against chlorophyll ]a and a suite of water quality parameters taken in a variety of conditions throughout a large optically complex estuarine system. A systematic analysis of sampling sites throughout the bay is undertaken to understand how the relationship between FLH and in situ chlorophyll-a responds to varying conditions within the estuary including water depth, distance from shore and structures and eight water quality parameters. From the 39 station for which data was derived, 22 stations showed significant correlations when the FLH product was matched with in situ chlorophyll-alpha data. The correlations (r2) for individual stations within Tampa Bay ranged between 0.67 (n=28, pless than 0.01) and-0.457 (n=12, p=.016), indicating that for some areas within the Bay, FLH can be a good predictor of chlorophyll-alpha concentration and hence a useful tool for the analysis of water quality. Overall, the results show a 106% increase in the validity of chlorophyll -a concentration estimates using FLH over the standard the blue-green OC3M algorithm. This analysis also illustrates that the correlations between FLH and in situ chlorophyll -a measurements increases with increasing water depth and distance of the monitoring sites from both the shore and structures. However, due to confounding factors related to the complexity of the estuarine system, a linear improvement in the FLH to chlorophyll ]a relationship was not clearly noted with increasing depth and distance from shore alone. Correlations of FLH with turbidity, nutrients (total nitrogen and total phosphorous) biological oxygen demand, salinity, sea surface temperature correlated positively with FLH concentrations, while dissolved oxygen and pH showed negative correlations. Principle component analyses are employed to further describe the relationships between the multivariate water quality parameters and the FLH product. The majority of sites with higher and very significant correlations (pless than 0.01) also showed high correlation values for nutrients, turbidity and biological oxygen demand. These sites were on average in greater than seven meters of water and over five kilometers from shore. A thorough understanding of the relationship between the MODIS FLH product and in situ water quality parameters will enhance our understanding of the accuracy MODIS fs global FLH algorithm and assist in optimizing its calibration for use in monitoring the quality of estuarine and coastal waters worldwide.

Moreno-Madrinan, Max Jacobo↗

DOE OSTI2026

Pu(IV) quantification via visible–near-infrared absorption spectroscopy: tackling interferences using D-optimal design and partial least squares

Here, this study presents a novel analytical approach for quantifying Pu(IV) in glove box environments using fiber-optic-based visible–near-infrared absorption spectroscopy in combination with partial least squares regression (PLSR) and design of experiments. The method addresses significant challenges posed by overlapping spectral features arising from Nd(III), which is a common fission product impurity, and the speciation variability of Pu(IV) nitrato complexes in HNO 3 concentrations ranging from 2.5 to 11 M. A curated training set consisting of data from 20 samples was developed via D-optimal design to enable robust PLSR model calibration for Pu(IV) using the near-infrared band near 1050 nm. The training set was acquired from samples in cuvettes with a 1-cm path length and was used to build the PLSR model. The robustness of the model was validated with data collected using a dip probe with a 1-cm path length and varying Pu(IV) concentrations. The strong performance of the model indicates good model transfer from cuvette to dip probe and highlights the potential for in situ measurements and online monitoring of reactions in a crystallization reactor vessel. The results demonstrate that this combined spectroscopic and chemometric approach can accurately and simultaneously quantify Pu(IV) and HNO 3 , thereby offering a promising tool for real-time monitoring in process environments.

Actinide↗

DOE OSTI2020

Genetic algorithm optimization of tin–copper graded shielding for improved plutonium safeguards measurements

Plutonium nuclear material safeguards is essential to ensure that states do not divert plutonium to clandestine nuclear weapons programs. Traditional safeguards methods rely on correlated neutron measurements. Detection systems based on organic scintillators, which are sensitive to fast neutrons and photons, are gaining support for use in safeguards applications due to increased capabilities and shorter measurement times compared to the current state-of-the-art. A drawback of organic scintillators in plutonium measurements is data throughput limitations; 241 Am builds up in plutonium samples and emits 60-keV gamma rays with a high specific activity. In this work, a genetic algorithm is used to design graded shielding made of tin and copper to attenuate the 60-keV gamma rays. The shield is designed to limit the attenuation of the higher-energy, fission photons, because fission photons can be used as a signature. The genetic algorithm optimization takes days to run, while a complete search space exploration would take years. The genetic algorithm was run for different levels of 60-keV gamma-ray attenuation, and the thickness of the required shielding layers was determined at each level. Tin–copper shielding was placed in front of an array of organic scintillation detectors for a measurement of approximately 4.5 kg of alpha-phase, weapons-grade plutonium, an AmLi source, and a 252 Cf source. We show that the shield successfully attenuated low-energy, non-fission gamma rays, which significantly improved the organic scintillator performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Advantages of Intraprocedural Unenhanced CT During Adrenal Venous Sampling to Confirm Accurate Catheterization of the Right Adrenal Vein

Purpose: To evaluate the advantages of intraprocedural CT during adrenal venous sampling (AVS) to confirm accurate catheterization of the right adrenal vein (RAV). Materials and Methods: This single-institution study included 106 patients (mean age 52.4 years; range 28–74 years) with primary aldosteronism who performed contrast-enhanced CT (CECT) before AVS following AVS between January 2011 and March 2018. After catheterization of the RAV under fluoroscopic guidance, unenhanced CT images were obtained to confirm catheter position on unified CT angiography system. Catheter repositioning was performed when the catheter was inaccurately positioned. Venography findings were classified into two groups: (1) presumably cannulated in the RAV (presumed RAV group) and (2) obscured visualization of the RAV because of collateral vessels (obscured RAV group). Success rates of AVS were compared using Fisher’s exact test. Results: The overall success of AVS was achieved in 104 patients (98.1%). Catheter was deviated into the IVC during intraprocedural CT in four patients. Fourteen patients (14.0%) required catheter repositioning by intraprocedural CT images, and accurate catheterization in the RAV was eventually accomplished. The success rate of AVS was significantly higher in the presumed RAV group (90.1% [73/81]) than that in the obscured RAV group (68.4% [13/19]) (p = 0.024). If intraprocedural CT was not acquired during AVS, the success rate of AVS would have been significantly lower (84.9% [90/106]) compared with that use of intraprocedural CT (98.1% [104/106]) (p < 0.001). Conclusions: Intraprocedural unenhanced CT by referring to the preprocedural CECT before AVS enables the confirmation of accurate catheterization of the RAV. Level of Evidence: Level 4, case series.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy

The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.

59 BASIC BIOLOGICAL SCIENCES↗