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DOE OSTI2013

Forward and adjoint sensitivity computation of chaotic dynamical systems

This paper describes a forward algorithm and an adjoint algorithm for computing sensitivity derivatives in chaotic dynamical systems, such as the Lorenz attractor. The algorithms compute the derivative of long time averaged “statistical” quantities to infinitesimal perturbations of the system parameters. The algorithms are demonstrated on the Lorenz attractor. We show that sensitivity derivatives of statistical quantities can be accurately estimated using a single, short trajectory (over a time interval of 20) on the Lorenz attractor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗

DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗

DOE OSTI2020

Transverse energy–energy correlations of jets in the electron–proton deep inelastic scattering at HERA

We study the event shape variables, transverse energy–energy correlation TEEC (cosφ) and its asymmetry ATEEC (cosφ) in deep inelastic scattering (DIS) at the electron–proton collider HERA, where φ is the angle between two jets defined using a transverse-momentum (k T ) jet algorithm. At HERA, jets are defined in the Breit frame, and the leading nontrivial transverse energy–energy correlations arise from the 3-jet configurations. With the help of the NLOJET++, these functions are calculated in the leading order (LO) and the next-to-leading order (NLO) approximations in QCD at the electron–proton center-of-mass energy $\sqrt{s}$ = 314 GeV. We restrict the angular region to -0.8 ≤ cosφ ≤ 0.8, as the forward- and backward-angular regions require resummed logarithmic corrections, which we have neglected in this work. Following experimental jet-analysis at HERA, we restrict the DIS-variables x, y = Q 2 /(xs), where Q 2 = -q 2 is the negative of the momentum transfer squared q 2 , to 0 ≤ x ≤ 1, 0.2 ≤ y ≤ 0.6, and the pseudo-rapidity variable in the laboratory frame (η lab ) to the range -1 ≤ η lab ≤ 2.5. The TEEC and ATEEC functions are worked out for two ranges in Q 2 , defined by 5.5GeV 2 ≤ Q 2 ≤ 80GeV 2 , called the low-Q 2 -range, and 150GeV 2 ≤ Q 2 ≤ 1000GeV 2 , called the high-Q 2 -range. We show the sensitivity of these functions on the parton distribution functions (PDFs), the factorization (μ F ) and renormalization (μ R ) scales, and on α s (M$^2_Z$). Of these the correlations are stable against varying the scale μ F and the PDFs, but they do depend on μ R . For the choice of the scale μ R =$\sqrt{\langle ET \rangle^2+Q^2}$, advocated in earlier jet analysis at HERA, the shape variables TEEC and ATEEC are found perturbatively robust. These studies are useful in the analysis of the HERA data, including the determination of α s (M$^2_Z$) from the shape variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1998

Nonlinear Analysis of the Space Shuttle Superlightweight LO2 Tank: Bahavior Under Booster Ascent Loads - Part 1

Results of linear bifurcation and nonlinear analyses of the Space Shuttle superlightweight (SLWT) external liquid-oxygen (LO2) tank for an important early booster ascent loading condition are presented. These results for thin-walled linear elastic shells that are subjected to combined mechanical and thermal loads illustrate an important type of response mode that may be encountered in the design of other liquid-fuel launch vehicles. Linear bifurcation analyses are presented that predict several nearly equal eigenvalues that correspond to local buckling modes in the forward ogive section of the LO2 tank. In contrast, the nonlinear response phenomenon is shown to consist of short-wavelength bending deformations in the forward ogive and barrel sections of the LO2 tank that growing amplitude in a stable manner increasing load. Imperfection sensitivity analyses are presented that show that the presence of several nearly equal eigenvalues does not lead to a premature general instability mode for the forward ogive section. For the linear bifurcation and nonlinear analyses, the results show that accurate predictions of the response of the shield generally require a large-scale, high-fidelity finite-element model. Results are also presented that show that the SLWT LO2 tank can support loads in excess of approximately 2.6 times the values of the operational loads considered.

Young, Richard D.↗

DOE OSTI2022

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

36 MATERIALS SCIENCE↗

NASA NTRS2022

Determining the Spectral Content of MOSES Images

The Multi-Order Solar Extreme Ultraviolet Spectrograph (MOSES) sounding rocket was launched from White Sands Missile Range on 2006 February 8th, to capture images of the Sun in the He ii 303.8 Å emission line. MOSES is a slitless spectrograph that forms images in multiple spectral orders simultaneously using a concave diffraction grating in an effort to measure line profiles over a wide field of view from a single exposure. Early work on MOSES data showed evidence of solar features composed of neither He ii 303.8 Å nor the nearby Si xi 303.3 Å spectral lines. We have built a forward model that uses cotemporal EIT images and the Chianti atomic database to fit synthetic images with known spectra to the MOSES data in order to quantify this additional spectral content. Our fit reveals a host of dim lines that alone are insignificant but combined contribute a comparable intensity to MOSES images as Si xi 303.3 Å. In total, lines other than He ii 303.8 Å and Si xi 303.3 Å contribute approximately 10% of the total intensity in the MOSES zero order image. This additional content, if not properly accounted for, could significantly impact the analysis of MOSES and similar slitless spectrograph data, especially those using a zero-order (undispersed) image. More broadly, this serves as a reminder that multilayer EUV imagers are sensitive to a host of weak contaminant lines.

Solar transition region↗

NASA NTRS1990

Summary of results and conclusions based on analysis of volume imaging and high spectral resolution lidar data acquired during FIRE phase 1, part 1

The collection of long term global statistics on cloud cover may be most easily accomplished with satellite based observations; however, measurements derived from passive satellite retrieval methods must be calibrated and verified by in situ or ground based remote sensor observations. Verification is not straight forward, however, because the highly variable nature of cloud altitude, morphology, and optical characteristics complicates the scaling of point measurements to satellite footprint sized areas. This is particularly evident for cirrus clouds which may be organized on horizontal scales of 10's of meters to 8 km or more, and have optical depths ranging from less than .003 to greater than 3. Cirrus clouds can strongly influence earths' radiative balance, but, because they are often transmissive, cirrus clouds are difficult to detect and characterize from satellite measurements. Because of its precise ranging capabilities, spatial resolution and sensitivity, lidar observations have played an important role in the detection, depiction, and characterization of cirrus clouds. Some of the characteristics of cirrus clouds are summarized which observed the High Spectral Resolution and Volume Imaging Lidars during the phase 1 IFO and ETO periods.

Grund, Christian J.↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A quantitative imaging framework for lithium morphology: Linking deposition uniformity to cycle stability in lithium metal batteries

Characterizing the morphology of lithium (Li) is crucial for developing long-lasting lithium metal batteries. It is well established that more uniform Li deposition correlates with better cell performance. Li morphology is often characterized through qualitative analysis of scanning electron microscopy (SEM) images; however, there are no widely accepted metrics to quantitatively describe deposition uniformity. Here, we propose a framework to quantify uniformity through SEM image analysis via the index of dispersion (ID) metric, which is defined and presented in the context of Li metal batteries. We also explore experimental impacts of sampling protocols onIDmeasurements. Our results demonstrate that theIDmetric is highly sensitive to variations in deposition uniformity, including the coexistence and uniformity of multiple morphologies, uniformity within a single morphology, and particle size distribution uniformity. Furthermore, it is demonstrated that uniformity, as measured by theID, can be related to the average potential of Li||Li symmetric cells over cycling. Higher capacity cycling leads to more pronounced changes in bothIDand average cell potential. Local minima/maxima are found consistently in bothIDand average cell potential immediately before cells short-circuit, which we suggest may indicate a collapse of the microstructure prior to failure. We put forward this framework as a more robust approach to quantify Li deposition uniformity, advancing the development of Li metal batteries that are safer and longer lasting.

Science & Technology - Other Topics↗

NASA NTRS2000

A Method for Assessing the Quality of Model-Based Estimates of Ground Temperature and Atmospheric Moisture Using Satellite Data

A method is developed for validating model-based estimates of atmospheric moisture and ground temperature using satellite data. The approach relates errors in estimates of clear-sky longwave fluxes at the top of the Earth-atmosphere system to errors in geophysical parameters. The fluxes include clear-sky outgoing longwave radiation (CLR) and radiative flux in the window region between 8 and 12 microns (RadWn). The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data, and multiple global four-dimensional data assimilation (4-DDA) products. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic clear-sky longwave fluxes from two different 4-DDA data sets. Simple linear regression is used to relate the clear-sky longwave flux discrepancies to discrepancies in ground temperature ((delta)T(sub g)) and broad-layer integrated atmospheric precipitable water ((delta)pw). The slopes of the regression lines define sensitivity parameters which can be exploited to help interpret mismatches between satellite observations and model-based estimates of clear-sky longwave fluxes. For illustration we analyze the discrepancies in the clear-sky longwave fluxes between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS2) and a recent operational version of the European Centre for Medium-Range Weather Forecasts data assimilation system. The analysis of the synthetic clear-sky flux data shows that simple linear regression employing (delta)T(sub g)) and broad layer (delta)pw provides a good approximation to the full radiative transfer calculations, typically explaining more thin 90% of the 6 hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters, Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (delta)T(sub g) to approx. 20% for the lower tropospheric moisture between 500 hPa and surface. The regression relationships developed from the synthetic flux data, together with CLR and RadWn observed with the Clouds and Earth Radiant Energy System instrument, ire used to assess the quality of the GEOS2 T(sub g) and pw. Results showed that the GEOS2 T(sub g) is too cold over land, and pw in upper layers is too high over the tropical oceans and too low in the lower atmosphere.

Wu, Man Li C.↗

DOE OSTI2024

Investigation of Correlation Methods for Use in Criticality Safety

Although their adoption by practitioners has been limited, the introduction of similarity indices in criticality safety was a major step forward in reducing the reliance on expert judgement in discerning applicable experiments for the validation of new appliations in criticality safety analyses. Similarity indices have been successfully employed in bias trending and data assimilation techniques, but it is often unclear which acceptance criteria should be used. In their 2004 paper, Broadhead et al. specify the most widely used similarity parameter, ck, as an acceptance cutoff at 0.9. (Broadhead et al., ”Sensitivity and Uncertainty-Based Criticality Safety Validation Techniques,” Nucl. Sci. Eng. 146, 340–366, 2004). Experiments with a ck < 0.9 are often not considered applicable for code validation. This heuristic is based on quantitative studies and engineering judgement, but in some cases, experiments with ck < 0.9 can be used to accurately estimate computational bias. This suggests that further analysis is needed to determine what components of ck are driving applicability and accuracy in bias estimation. For cases in which applicable experiments may not be available (as is the case with UF6 transport canisters), understanding what distinguishes experiments in providing adequate bias estimates aside from just the similarity index is particularly necessary. To further the goal to better interpret ck values, several visualization tools were developed to assist in the investigation of which components of ck are driving applicability.

ck↗

DOE OSTI2015

Analysis of reversed torsion of FCC metals using polycrystal plasticity models

Large strain behavior of FCC polycrystals during reversed torsion are investigated through the special purpose finite element based on the classical Taylor model and the elastic-viscoplastic self-consistent (EVPSC) model with various Self-Consistent Schemes (SCSs). It is found that the response of both the fixed-end and free-end torsion is very sensitive to the constitutive models. The models are assessed through comparing their predictions to the corresponding experiments in terms of the stress and strain curves, the Swift effect and texture evolution. It is demonstrated that none of the models examined can precisely predict all the experimental results. However, more careful observation reveals that, among the models considered, the tangent model gives the worst overall performance. As a result, it is also demonstrated that the intensity of residual texture during reverse twisting is dependent on the amounts of pre-shear strain during forward twisting and the model used.

42 ENGINEERING↗

DOE OSTI2020

Isopropanol dehydration reaction rate kinetics measurement using H 2 O time histories

H 2 O formation during thermal decomposition of isopropanol was measured behind reflected shock waves at temperatures ranging from 1127 to 1621 K at an average pressure of 1.42 atm using a laser absorption technique. Of the five modern chemical kinetics models used to compare the H 2 O time histories, the model from Li et al (Combust. Flame 2019;207:171-185) showed the best overall agreement. Sensitivity and rate of production analyses using the Li et al model (as well as those from AramcoMech 3.0, CRECK, and Togbe et al (Energy Fuel. 2011;25:676-683)) showed unimolecular dehydration of isopropanol, iC 3 H 7 OH ⇌ H 2 O + C 3 H 6 (R1), is nearly the sole reaction controlling H2O production at early times, allowing for an a priori measurement of the forward rate constant k 1 . The Arrhenius expression k1 (s –1 ) = 2.60 × 10 13 exp(-31 120 K/T) was determined to represent best the data from this study. According to the models and previous experimental investigations, the pressure investigated is well within the high-pressure limit (HPL) for this reaction. Additional, higher-pressure experiments also confirmed the HPL assumption. An uncertainty analysis was performed by varying secondary reactions within their uncertainties and examining their effect on the overall prediction, establishing an uncertainty within ±20% for all but the highest temperature cases, which have a maximum uncertainty of ±40%. Experiments conducted with a radical trapper, toluene, showed little influence from radical chemistry, suggesting this estimated uncertainty is fairly conservative. Experimental data from Heyne et al (Z Phys Chem. 2015;229:881-907) were found to be in good agreement with the rate measurements from this study and, therefore, a second Arrhenius expression, k 1 (s –1 ) = 2.11 × 10 13 exp(-30 820 K/T), was found to represent both datasets well. This second expression has a larger temperature range of 976-1621 K. Here the present study provides the first high-temperature data collected for this reaction, adding to the limited data available for isopropanol in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

IER 489: Critical Unresolved Region Integral Experiment (CURIE) CEDT Phase-2 Final Design

The final design for the CURIE integral benchmark experiment is presented in this report. Measurements will be performed on the Comet vertical assembly machine and include various critical configurations consisting of High Enriched Uranium (HEU) metal plates and Teflon moderator plates surrounded by a thick copper reflector. The various configurations with different Teflon plate thickness planned allow for complimentary measurements with differing nuclear data sensitivity in the Unresolved Resonance Region (URR). Similar to previously performed Zeus experiments, the proposed work will serve to validate vital nuclear data quantities and uncertainties and identify deficiencies in current radiation transport codes. Additionally, it will assess the applicability of optimization approaches to nuclear critical experiment design. The final experiment design is detailed in this report and includes design, optimization, uncertainty and continuous-energy nuclear data sensitivity analysis for the final experimental configurations that will provide the basis for a future benchmark evaluation intended for submission to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook [1]. This document discusses the method, approach, considerations and final design chosen with conclusions. It also includes cost and schedule. The basis and principles of this document come from the preliminary design report [2]. Section 2 discusses the content and potential impact; section 3 gives an overview of the nuclear data and unresolved resonance region; section 4 gives a summary of the work done during the preliminary design; section 5 discusses the machine learning method and principles used for final design; section 6 summaries the results from final design; section 7 provides conclusions and the range of configurations chosen to carry forward to experiment execution; and section 8 includes a summary of costs.

42 ENGINEERING↗

DOE OSTI2013

Low Background Materials and Assay: A Supplement to the Cosmic Frontier CF1 Summary.

Executive Summary The next generation of direct detection dark matter experiments will require stringent radiopurity in their target materials, internal instrumentation, and shielding components. A survey of major material assay facilities worldwide and of anticipated assay needs of generation 2 (G2) experiments indicate the current assay capability is marginally adequate in sensitivity and inadequate in throughput. High purity germanium (HPGe) gamma-ray assay has been the workhorse for material screening. It appears hundreds of samples will require screening at the limits of current achievable HPGe sensitivity. Mass spectroscopy methods and neutron activation analysis have demonstrated superior assay sensitivity in some specific cases. However, they are also currently throughput limited. In addition, both alpha/beta screening and radon emanation analysis at or beyond the current achievable sensitivity levels will be needed for dozens of samples. Experiments that follow G2 will have even more stringent material radiopurity specifications, exceeding current assay capabilities in both sensitivity and throughput. These screening needs must precede the commissioning of experiments by 3-5 years to inform design and quality control of components. Achieving the radiopurity goals of the next decade’s experiments will require investment in new techniques and tools to improve material assay sensitivity and throughput. Engineered low-background materials are a significant R&D expenditure, requiring developmental lead-time, and should benefit many end-user experiments. Reserving underground real estate for assay methods affected by cosmic rays (e.g. HPGe) and engineered radiopure materials activated by cosmic rays (e.g. electroformed copper) is judicious forward planning. In many cases, these spaces will require radon-suppressed sample preparation and material storage space. The above-mentioned survey of material assay facilities indicated very little development is taking place to improve sensitivity or throughput capability. In order to create the necessary infrastructure, it may help to form a consortium of low-background assay centers in the U.S that is managed by a scientific board with representatives from the community. This would provide for common use of existing screening facilities and a unified plan for new infrastructure and site development (see the Facilities White Paper). Such a loose organization already exists as AARM (Assay and Acquisition of Radiopure Materials), which is currently populating a community-wide materials database with published assay information. A more formal organization such as a Consortium is being actively investigated with DOE and NSF. Eventually, results of all screening performed in Consortium assay centers could be input directly to the open-access database, reducing duplication and providing for more efficient vendor selection.

Cooley-Sekula, Jodi↗

DOE OSTI2017

Demonstration and Validation of a Portable Raman Sensor for In-Situ Detection and Monitoring of Perchlorate (ClO 4 - )

Costs for environmental analysis and monitoring are increasing at a rapid rate and represent a significant percentage of the total and future remedial expenses at many U.S. Department of Defense (DoD) contaminated sites. It has been reported that about 30 to 40% of the remediation budget is usually spent on long-term monitoring (LTM), of which a large percentage represents laboratory analytical costs. Energetics such as perchlorate (ClO 4 - ) are among the most frequently detected contaminants in groundwater and surface water at or near military installations due to their persistence and mobility. Currently, the standard protocol entails collecting samples in the field, packaging them, and shipping them overnight to a designated laboratory for analysis. This process requires significant sample preparation and handling, and analytical results may not be available for several days to weeks. In this project, we developed and demonstrated a portable Raman sensor based on surface enhanced Raman scattering (SERS) technology to detect ClO 4 - in contaminated water. We summarize major accomplishments as follows: • A SERS sensor based on elevated gold (Au) nano-ellipse dimer architectures was designed and developed for ClO 4 - with a detection limit of ~10 -6 M (or 100 μg/L); The performance of these sensors was evaluated and optimized through variation of their geometric characteristics (i.e., dimer aspect ratio, dimer separation, etc.). • Large-scale commercial production of SERS substrate sensors via nanoimprinting by Nanova Inc. and Nanoimprint lithography (NIL) technology was successfully demonstrated. This is a substantial step forward toward the commercialization of the SERS sensors and may potentially lead to significantly reduced fabrication costs of SERS substrates. • Commercially produced SERS sensors were demonstrated to detect ClO 4 - at levels above 10 -6 M using a portable Raman analyzer. The performance of the commercial SERS sensors for ClO 4 - detection in the presence and absence of interferences was determined for a series of standard solutions. Sulfate (SO 4 2- ) was found to exhibit the greatest interference for the anions tested, which included Cl-, NO 3 - , and SO 4 2- . • Field demonstration of the portable Raman sensor with commercially produced SERS substrates was completed at two Department of Defense (DoD) sites; twice at the Indian Head Naval Surface Warfare Center, Indian Head, MD, and once at Redstone Arsenal, Huntsville, AL. Multiple wells were sampled at both DoD sites, where a standard addition method was employed using the sensor to determine the ClO 4 - 4 - and possibly other energetics that are both important for environmental monitoring and of interest for national security. However, we point out that SERS technology is also prone to interferences due to its sensitivity and responses to other ionic species, such as NO 3 - , SO 4 2- , and dissolved organics or co-contaminants present in the groundwater, which could potentially mask the SERS signal of the target analyte (i.e., ClO 4 - ). As such, SERS analysis was subject to significant variations (e.g., ±20% or more), and its detection limit for ClO 4 - -8 M) and was substantially higher than what we anticipated from laboratory studies. However, despite these complications, the portable Raman sensor developed in this project could be used as a rapid screening tool for ClO 4 - at concentrations above 10 -6 M. Future studies are warranted to further develop the technology and to optimize its performance, and eventually to bring the technology to the market. With additional development and demonstration, the sensor has the potential to reduce analytical costs by eliminating shipping and typical costs associated with laboratory analysis. A cost savings of 30–45% may be realized during a typical sampling event and, more importantly, the technology could allow rapid turn-around of information to decision makers for site characterization and remediation.

47 OTHER INSTRUMENTATION↗

NASA NTRS1978

Photometry and polarimetry of Jupiter at large phase angles. I - Analysis of imaging data of a prominent belt and a zone from Pioneer 10

Photopolarimetric observations of a prominent bright zone and a dark belt of Jupiter in red and blue light are analyzed which were performed by Pioneer 10 at phase angles of 12, 23, 34, 109, 120, 127, and 150 deg. Geometric and photometric reductions of the imaging data are described, the instrument sensitivity at various times is evaluated, and the data are referred to an absolute scale. The observations are analyzed in detail by comparing the data with results of radiative-transfer calculations for specific scattering models of Jupiter's atmosphere. These models include those with a vertical structure consisting of a layer of Rayleigh-scattering gas above a semiinfinite mixture of cloud particles and gas, those having a small quantity of aerosols in the gas above either the diffuse cloud in a reflecting-scattering model or the top cloud of a two-cloud-layer model, those in which a forward-scattering haze is mixed uniformly with gas, and those containing dust layers. It is found that in both the belt and the zone in red as well as blue light, cloud phase functions are required which provide both strong forward scattering and some backscattering.

Tomasko, M. G.↗