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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH 2 O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH 2 O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. Finally, it is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis , M. sacchariflorus and M. fIoridulus , were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis , M. sacchariflorus and M. fIoridulus , respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗
DOE OSTI2017

MALDI Orbitrap Mass Spectrometry Profiling of Dysregulated Sulfoglycosphingolipids in Renal Cell Carcinoma Tissues

Matrix-assisted laser desorption/ionization coupled with Orbitrap mass spectrometry (MALDI-Orbitrap-MS) is used for the clinical study of patients with renal cell carcinoma (RCC), as the most common type of kidney cancer. Significant changes in sulfoglycosphingolipid abundances between tumor and autologous normal kidney tissues are observed. First, sulfoglycosphingolipid species in studied RCC samples are identified using high mass accuracy full scan and tandem mass spectra. Subsequently, optimization, method validation, and statistical evaluation of MALDI-MS data for 158 tissues of 80 patients are discussed. More than 120 sulfoglycosphingolipids containing one to five hexosyl units are identified in human RCC samples based on the systematic study of their fragmentation behavior. Many of them are recorded here for the first time. Multivariate data analysis (MDA) methods, i.e., unsupervised principal component analysis (PCA) and supervised orthogonal partial least square discriminant analysis (OPLS-DA), are used for the visualization of differences between normal and tumor samples to reveal the most up- and downregulated lipids in tumor tissues. Obtained results are closely correlated with MALDI mass spectrometry imaging (MSI) and histologic staining. Important steps of the present MALDI-Orbitrap-MS approach are also discussed, such as the selection of best matrix, correct normalization, validation for semiquantitative study, and problems with possible isobaric interferences on closed masses in full scan mass spectra. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

NMR-based serum metabolomics study reveals a innovative diagnostic model for missed abortion

A missed abortion (MA) is an in-utero death of the embryo or fetus before the 20th week of gestation with retained products of conception. In order to discover novel biomarkers for MA, a {sup 1}H NMR spectroscopy-based metabolomics approach was applied to detect human MA serum metabolic profiles. Serum samples were obtained from patients with MA (n = 15) and healthy controls (n = 9) for study. The NOESYPR1D spectrum combined with multi-variate pattern recognition analysis was used to cluster the groups and establish a disease-specific metabolites phenotype. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models were capable of distinguishing MA patients from healthy subjects. The results revealed that 24 metabolites altered in MA patients compared with the control population. Metabolomic pathway analysis demonstrated that alanine, aspartate and glutamate metabolism, citrate cycle (TCA cycle), taurine and hypotaurine metabolism were significantly altered in MA. The results indicated that serum NMR-based metabolomic profiling method is sensitive and specific enough to distinguish MA and from healthy controls, this method could be developed as a clinically useful diagnostic tool for MA. The finding from the MA serum metabolic profiling shed a new light on further understanding of MA disease mechanisms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A new classification scheme of plastic wastes based upon recycling labels

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1973

Application of ERTS-1 imagery to the study of caribou movements and winter dispersal in relation to prevailing snowcover

The author has identified the following significant results. Step-wise discriminate analysis has demonstrated the feasibility of feature identification using linear discriminate functions of ERTS-1 MSS band densities and their ratios. The analysis indicated that features such as small streams can be detected even when they are in dark mountain shadow. The potential utility of this and similar analytic techniques appears considerable, and the limits it can be applied to analysis of ERTS-1 imagery are not yet fully known.

Lent, P. C.↗
NASA NTRS2001

Predictive Compensator Optimization for Head Tracking Lag in Virtual Environments

We examined the perceptual impact of plant noise parameterization for Kalman Filter predictive compensation of time delays intrinsic to head tracked virtual environments (VEs). Subjects were tested in their ability to discriminate between the VE system's minimum latency and conditions in which artificially added latency was then predictively compensated back to the system minimum. Two head tracking predictors were parameterized off-line according to cost functions that minimized prediction errors in (1) rotation, and (2) rotation projected into translational displacement with emphasis on higher frequency human operator noise. These predictors were compared with a parameterization obtained from the VE literature for cost function (1). Results from 12 subjects showed that both parameterization type and amount of compensated latency affected discrimination. Analysis of the head motion used in the parameterizations and the subsequent discriminability results suggest that higher frequency predictor artifacts are contributory cues for discriminating the presence of predictive compensation.

Adelstein, Barnard D.↗
DOE OSTI2018

Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors

We report that carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine-learning algorithms, we have successfully discriminated among five selected purine compounds, adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory calculations. Furthermore, by testing a variety of prepared as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis and in 93.4% of the samples with only 11 features when using a support vector machine analysis. Lastly, we also performed recursive feature elimination and identified three NTFET parameters, transconductance, threshold voltage, and minimum conductance, as the most crucial features to analyte prediction accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Plasmonics Based Harsh Environment Compatible Chemical Sensors

Au-YSZ, Au-TiO 2 and Au-CeO 2 nanocomposite films have been investigated as a potential sensing element for high-temperature plasmonic sensing of H 2 , CO, and NO 2 in an oxygen containing environment. The Au-YSZ and Au-TiO 2 films were deposited using PVD methods, while the CeO 2 thin film was deposited by molecular beam epitaxy (MBE) and Au was implanted into the as-grown film at an elevated temperature followed by high temperature annealing to form well-defined Au nanoclusters. Each of the films were characterized by x-ray diffraction (XRD) and Rutherford backscattering spectrometry (RBS). For the gas sensing experiments, separate exposures to varying concentrations of H 2 , CO, and NO 2 were performed at a temperature of 500°C in oxygen backgrounds of 5.0, 10, and ~21% O 2 . Changes in the localized surface plasmon resonance (LSPR) absorption peak were monitored during gas exposures and are believed to be the result of oxidation-reduction processes that fill or create oxygen vacancies in the respective metal oxides. This process affects the LSPR peak position either by charge exchange with the Au nanoparticles or by changes in the dielectric constant surrounding the particles. Hyperspectral multivariate analysis was used to gauge the inherent selectivity of the film between the separate analytes. From principal component analysis (PCA), unique and identifiable responses were seen for each of the analytes. Linear discriminant analysis (LDA) was also used on the Au-CeO 2 results and showed separation between analytes as well as trends in gas concentration. Results indicate that each of the films are is selective towards O 2 , H 2 , CO, and NO 2 in separate exposures. However, when the films were analyzed in a sensor array based experiment, ie simultaneous exposures to the target gases, PCA analysis of the combined response showed an even greater selective character towards the target gases. Combined with the observed stability over long exposure periods, each of these Au-metal oxide films shows good potential as an optical sensing element for harsh environmental conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Helioseismology of pre-emerging active regions. III. Statistical analysis

The subsurface properties of active regions (ARs) prior to their appearance at the solar surface may shed light on the process of AR formation. Helioseismic holography has been applied to samples taken from two populations of regions on the Sun (pre-emergence and without emergence), each sample having over 100 members, that were selected to minimize systematic bias, as described in Paper I. Paper II showed that there are statistically significant signatures in the average helioseismic properties that precede the formation of an AR. This paper describes a more detailed analysis of the samples of pre-emergence regions and regions without emergence based on discriminant analysis. The property that is best able to distinguish the populations is found to be the surface magnetic field, even a day before the emergence time. However, after accounting for the correlations between the surface field and the quantities derived from helioseismology, there is still evidence of a helioseismic precursor to AR emergence that is present for at least a day prior to emergence, although the analysis presented cannot definitively determine the subsurface properties prior to emergence due to the small sample sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1978

Selection of a seventh spectral band for the LANDSAT-D thematic mapper

The author has identified the following significant results. Each of the candidate bands were examined in terms of the feasibility of gathering high quality imagery from space while taking into account solar illumination, atmospheric attenuation, and the signal/noise ratio achievable within the TM sensor constraints. For the 2.2 micron region and the thermal IR region, inband signal values were calculated from representative spectral reflectance/emittance curves and a linear discriminant analysis was employed to predict classification accuracies. Based upon the substantial improvement (from 78 t0 92%) in discriminating zones of hydrothermally altered rocks from unaltered zones, over a broad range of observation conditions, a 2.08-2.35 micron spectral band having a ground resolution of 30 meters was recommended.

Holmes, Q. A.↗
DOE OSTI2011

Zone-based analysis for automated detection of abnormalities in chest radiographs

Purpose: The aim of this study was to develop an automated method for detection of local texture-based and density-based abnormalities in chest radiographs. Methods: The method was based on profile analysis to detect abnormalities in chest radiographs. In the method, one density-based feature, Density Symmetry Index, and two texture-based features, Roughness Maximum Index and Roughness Symmetry Index, were used to detect abnormalities in the lung fields. In each chest radiograph, the lung fields were divided into four zones initially and then the method was applied to each zone separately. For each zone, Density Symmetry Index was obtained from the projection profile of each zone, and Roughness Maximum Index and Roughness Symmetry Index were obtained by measuring the roughness of the horizontal profiles via moving average technique. Linear discriminant analysis was used to classify normal and abnormal cases based on the three indices. The discriminant performance of the method was evaluated using ROC analysis. Results: The method was evaluated on a database of 250 normal and 250 abnormal chest images. In the optimized conditions, the zone-based performance Az of the method for zones 1, 2, 3, and 4 were 0.917, 0.897, 0.892, and 0.814, respectively, and the case-based performance Az of the method was 0.842. Our previous method for detection of gross abnormalities was also evaluated on the same database. The case-based performance of our previous method was 0.689. Conclusions: In comparing the previous method and the new method proposed in this study, there was a great improvement by the new method for detection of local texture-based and density-based abnormalities. The new method combined with the previous one has potential for screening abnormalities in chest radiographs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures

A multivariate model was developed here to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Serum Proteome Profiles in Stricturing Crohn’s Disease: A pilot study.

Background: Crohn’s disease (CD) is a form of inflammatory bowel disease (IBD) with different described behaviors, including stricture. At present, there are no laboratory studies that can differentiate stricturing CD from other phenotypes of IBD. We performed a pilot study to examine differences in the proteome among patients with stricturing Crohn’s disease, non-stricturing Crohn’s disease, and ulcerative colitis (UC). Methods: Serum samples were selected from the Ocean State Crohn’s and Colitis Area Registry (OSCCAR), an established cohort of patients with IBD. Crohn’s disease patients with surgically-resected stricture were matched with similar patients with Crohn’s disease without known stricture, and with UC. Serum samples from each patient were digested and analyzed using liquid chromatography-mass spectrometry to characterize the proteome. Statistical analyses were performed to identify peptides and proteins that can differentiate CD with stricture. Results: Samples from 9 patients in each group (27 total patients) were analyzed. Baseline demographic characteristics were similar among the three groups. We quantified 7668 peptides and 897 proteins for analysis. ROC analysis identified a subset of peptides with an area under the curve greater than 0.9, indicating greater separation potential. Partial least squares discriminant analysis was able to distinguish among the three groups with up to 70% accuracy by peptides, and up to 80% accuracy by proteins. We identified the significantly different proteins and peptides, and determined their function based on previously published literature. Conclusions: The serum of patients with stricturing CD, non-stricturing CD, and UC are distinguishable via proteomic analysis. Some of the proteins that differentiate the stricturing phenotype have been implicated in complement activation, fibrinolytic pathways, and lymphocyte adhesion.

Environmental Molecular Sciences Laboratory↗
NASA NTRS1982

Comparative analysis of co-registered SIR-A, Seasat and Landsat images

A number of areas were covered by Seasat SAR and SIR-A, thus providing information at two different incidence angles (20 deg and 50 deg respectively). These angles correspond to scattering from two different surface roughness scales. The results of the analysis from two areas will be presented. The first is the San Rafael area in Utah where Seasat/SIR-A/Landsat data were coregistered and discriminant analysis is being applied to classify surface units. The second area is the Chotts area in Algeria where appreciable changes have been observed between Seasat and SIR-A images. However, it is not clear if the changes are due to the different observation geometry or changes in the surface between 1978 and 1981.

Elachi, C.↗