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At least 541 records · Page 30

Using ASCEM Modeling and Visualization to Inform Stakeholders of Contaminant Plume Evolution and Remediation Efficacy at F-Basin Savannah River, SC – 15156

Communication with stakeholders, regulatory agencies, and the public is an essential part of implementing different remediation and monitoring activities, and developing site closure strategies at contaminated sites. Modeling of contaminant plume evolution plays a critical role in estimating the benefit, cost, and risk of particular options. At the same time, effective visualization of monitoring data and modeling results are particularly important for conveying the significance of the results and observations. In this paper, we present the results of the Advanced Simulation Capability for Environmental Management (ASCEM) project, including the discussion of the capabilities of newly developed ASCEM software package, along with its application to the F-Area Seepage Basins located in the U.S. Department of Energy Savannah River Site (SRS). ASCEM software includes state-of-the-art numerical methods for simulating complex flow and reactive transport, as well as various toolsets such as a graphical user interface (GUI), visualization, data management, uncertainty quantification, and parameter estimation. Using this software, we have developed an advanced visualization of tritium plume migration coupled with a data management system, and simulated a three-dimensional model of flow and plume evolution on a high-performance computing platform. We evaluated the effect of engineered flow barriers on a nonreactive tritium plume, through advanced plume visualization and modeling of tritium plume migration. In addition, we developed a geochemical reaction network to describe complex geochemical processes at the site, and evaluated the impact of coupled hydrological and geochemical heterogeneity. These results are expected to support SRS’s monitoring activities and operational decisions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

User Requirements Specification for the Universal Real Time Controller: Ergonomic Design for Placement of Three NI 9157/9159 Chassis

The Air Delivered Weapons Tester Development Group (2256) is currently designing a new type of test system, the Universal Real Time Controller (URTCON). The URTCON will provide the surveillance community with technologies that advance the state of knowledge in stockpile stewardship. Success for its implementation depends on whether the components comprising the URTCON can be reliably and accurately maintained in the field. A system is maintainable when the worker has visual and physical access to the components that need to be serviced. To address this need, a layout was determined that best facilitates the worker's ability to easily see and reach components. A list of user-system requirements was initially prepared to guide decisions affecting the layout. Then, five design options depicting various ways to lay out the components were evaluated. Based on this effort, a best design option was identified for implementation. It is expected that the design recommended herein will provide the workers with a workplace that permits unhindered access to the components requiring their attention.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

A risk-informed assessment tool to support review of license amendment requests for spent nuclear fuel dry cask storage systems

We report that while risk-informed safety evaluations are common methodologies used by the U.S. Nuclear Regulatory Commission (NRC), their application in the field of commercial spent nuclear fuel (SNF) dry storage has received limited attention. For instance, license amendment requests (LARs) for SNF dry cask storage systems (DCSSs) are evaluated according to U.S. NRC standard review plans (SRPs). However, risk-informed LAR review strategies could improve the understanding of critical system modifications, and lead to more predictable, efficient, and consistent LAR review processes. A methodology to develop a risk-informed LAR review tool is presented. Although focused on DCSSs, this methodology can be used to develop similar tools for a broad spectrum of safety-relevant systems. The end product (i.e., the tool) includes a tree diagram to help visualize the review risk significances assigned to a predefined set of system modifications and supporting rationale documentation. Instructions support correct tool application and provide the user with a path to incorporate currently unevaluated modifications in the tool structure. The demonstration of the methodology leads to the conclusion that risk-informed reviews of DCSS LARs are possible, although the tool precision could benefit from additional risk information. Further, the results indicate that many typical LARs involve low-risk or medium-risk modifications.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Recent progress in in-situ visualization of electrochemical reactions in electrochemical energy devices

A growing body of evidence indicates that bubble dynamics are crucial performance-limiting factors on electrochemical reactions and energy conversion efficiencies in both half-cell and full cell device levels. This review aims to summarize the recent progress in in-situ visualization characterizations of electrochemical reactions and reactant/product transport in electrochemical energy systems. The latest achievements of various new electrodes, transparent cell designs, and development for simultaneous visualization of reactions at both electrodes and electrolyte/electrode interface are highlighted. The new insights into the influence of bubbles on reaction sites and associated electrochemical performances are discussed. Lastly, some perspectives on potential strategies for mitigating the bubble-induced energy losses are proposed for future research directions and opportunities in water electrolysis and other electrochemical energy systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗

Dynamic Motions of Topological Defects in Nematic Liquid Crystals under Spatial Confinement

Abstract Topological defects (TDs) have become a sensational topic due to their significant influence on the unusual optical and physiochemical characteristics of the material. To facilitate their application across a wide range of disciplines it is desirable to analyze and gain fundamental understanding of TDs in both equilibrium and nonequilibrium systems. Liquid crystals (LCs) are considered an ideal system for study given the direct visualization of TDs and a straightforward analyzation process. In addition to the equilibrium morphology of LC TDs, it is also of great interest to track and control the formation and annihilation of defects during thermodynamic processes. However, controlling the dynamic behavior of formed defects remains a challenge. Here, nematic LCs confined in a cell exhibiting surfaces with periodic anchoring conditions containing surface topography are relied on. The effects of patterned surface characteristics such as width, periodicity, and degree of curvature on defects dynamic motion, stabilization, and annihilation are explored. The computational experiments recapitulate the TDs transition path and provide free energy‐based predictions of critical distances for defect annihilation. Taken together, this simple approach offers a promising opportunity to control the dynamics of TDs in LCs through chemical patterned surfaces with topography.

36 MATERIALS SCIENCE↗

GPUs as boosters to analyze scalar and vector fields in quantum chemistry

We report that the analysis of scalar and vector fields in quantum chemistry is an essential task for the computational chemistry community, where such quantities must be evaluated rapidly to perform a particular study. For example, the atoms in molecules approach proposed by Bader has become popular; however, this method demands significant computational resources to compute the involved tasks in short times. In this article, we discuss the importance of graphics processing units (GPU) to analyze electron density, and related fields, implementing several scalar, and vector fields within the graphics processing units for atoms and molecules (GPUAM) code developed by a group of the Universidad Autónoma Metropolitana in México City. With this application, the quantum chemistry community can perform demanding computational tasks on a desktop, where CPUs and GPUs are used to their maximum capabilities. Lastly, the performance of GPUAM is tested in several systems and over different GPUs, where a GPU installed in a workstation converts it to a robust high-performance computing system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Installation protocol for charge transfer dissociation mass spectrometry on ion trapping mass spectrometers

Abstract Rationale Charge transfer dissociation (CTD) is a novel fragmentation technique that demonstrates enhanced structural characterization for a wide variety of molecules compared to standard fragmentation techniques like collision‐induced dissociation (CID). Alternative fragmentation techniques, such as electron transfer dissociation, electron capture dissociation, and ultraviolet photodissociation, also overcome many of the shortfalls of CID, but none of them are a silver bullet that can adequately characterize a wide variety of structures and charge states of target compounds. Given the diversity of structural classes and their occasional obstinance towards certain activation techniques, alternative fragmentation techniques are required that rely on novel or alternative modes of activation. Methods Herein, we present a step‐by‐step protocol for the installation of CTD on a quadrupole ion trap mass spectrometer and best practices for optimizing the signal‐to‐noise ratio and acquisition times for CTD mass spectra. Results In addition to two CTD installations in the Jackson laboratory, CTD has also been installed, and is currently in operation, on two 3D ion trap mass spectrometers in France: one in the laboratory of Dr. David Ropartz and Dr. Hélène Rogneaux at INRAE in Nantes, and the other in the laboratory of Dr. Jean‐Yves Salpin at Université d'Évry Val‐d'Essonne, part of the Paris‐Saclay University system. Conclusions Here, we provide a visual protocol to help others accomplish the instrument modification.

47 OTHER INSTRUMENTATION↗

SimCCS: An open-source tool for optimizing CO 2 capture, transport, and storage infrastructure

Commercial-scale carbon capture and storage (CCS) technology will involve deploying infrastructure on a massive and costly scale. This effort will require careful and comprehensive planning to ensure that capture locations, storage sites, and the dedicated CO 2 distribution pipelines are selected in a robust and cost-effective manner. Presented in 2009, SimCCS is an optimization model for integrated system design that enables researchers, stakeholders, and policy makers to design CCS infrastructure networks. SimCCS 2.0 is a complete, ground-up redesign that is now a portable software package, useable and shareable by the CCS research, industrial, policy, and public communities. SimCCS 2.0 integrates multiple new capabilities including a refined optimization model, novel candidate network generation techniques, and optional integration with high-performance computing platforms. Accessing user-provided CO 2 source, sink, and transportation data, SimCCS 2.0 creates candidate transportation routes and formalizes an optimization problem that determines the most cost-effective CCS system design. This optimization problem is further solved either through a high-performance computing interface, or through third-party software on a local desktop computing platform. Finally, SimCCS 2.0 employs an open-access geographic information system framework to enable analysis and visualization capabilities. SimCCS 2.0 is written in Java and is publicly available via GitHub to encourage collaboration, modification, and community development.

42 ENGINEERING↗

Canister valve and actuator deposition in metered dose inhalers formulated with low-GWP propellants

A challenge in pressurised metered-dose inhaler (pMDI) formulation design is management of adhesion of the drug to the canister wall, valve and actuator internal components and surfaces. Wall-material interactions differ between transparent vials used for visual inspection and metal canister pMDI systems. This is of particular concern for low greenhouse warming potential (GWP) formulations where propellant chemistry and solubility with many drugs are not well understood. In this study, we demonstrate a novel application of X-ray fluorescence spectroscopy using synchrotron radiation to assay the contents of surrogate solution and suspension pMDI formulations of potassium iodide and barium sulphate in propellants HFA134a, HFA152a and HFO1234ze(E) using aluminium canisters and standard components. Preliminary results indicate that through unit life drug distribution in the canister valve closure region and actuator can vary significantly with new propellants. For solution formulations HFO1234ze(E) propellant shows the greatest increase in local deposition inside the canister valve closure region as compared to HFA134a and HFA152a, with correspondingly reduced actuator deposition. This is likely driven by chemistry changes. For suspension formulations HFA152a shows the greatest differences, due to its low specific gravity. These changes must be taken into consideration in the development of products utilising low-GWP propellants.

60 APPLIED LIFE SCIENCES↗

Outcomes of Proton Therapy for the Treatment of Uveal Metastases

Purpose/Objective(s): Radiation therapy can be used to treat uveal metastases with the goal of local control and improvement of quality of life. Proton therapy can be used to treat uveal tumors efficiently and with expectant minimization of normal tissue injury. Here, we report the use of proton beam therapy for the management of uveal metastases. Methods and Materials: A retrospective chart review was made of all patients with uveal metastases treated at our institution with proton therapy between June 2002 and June 2012. Patient and tumor characteristics, fractionation and dose schemes, local control, and toxicities are reported. Results: Ninety patients were identified. Of those, 13 were excluded because of missing information. We report on 77 patients with 99 affected eyes with available data. Patients were 68% female, and the most common primary tumor was breast carcinoma (49%). The median age at diagnosis of uveal metastasis was 57.9 years. Serous retinal detachment was seen in 38% of treated eyes. The median follow-up time was 7.7 months. The median dose delivered to either eye was 20 Gy(relative biological effectiveness [RBE]) in 2 fractions. Local control was 94%. The median survival after diagnosis of uveal metastases was 12.3 months (95% confidence interval, 7.7-16.8). Death in all cases was secondary to systemic disease. Radiation vasculopathy, measured decreased visual acuity, or both was observed in 50% of evaluable treated eyes. The actuarial rate of radiation vasculopathy, measured decreased visual acuity, or both was 46% at 6 months and 73% at 1 year. The 6 eyes with documented local failure were successfully salvaged with retreatment. Conclusions: Proton therapy is an effective and efficient means of treating uveal metastases. Acutely, the majority of patients experience minor adverse effects. For longer-term survivors, the risk of retinal injury with vision loss increases significantly over the first year.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Pass-efficient methods for compression of high-dimensional turbulent flow data

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. Additionally, a particular emphasis is placed on using coarse representation of the data – compatible with the PDE discretization grid – to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Spatial and Temporal Variations in Characteristic Ratios of Elemental Carbon to Carbon Monoxide and Nitrogen Oxides across the United States

Here, a ratio-based method is used to characterize anthropogenic elemental carbon (EC a ) using in situ measurements and emissions of carbon monoxide (CO) and nitrogen oxides (NO x ). We use long-term records of ground-based measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System and Interagency Monitoring of Protected Visual Environments to assess the patterns in anthropogenic combustion ratios (ΔEC a /ΔCO and ΔEC a /ΔNO x ) across the U.S. Petroleum Administration for Defense Districts (PADD) regions for the years 2000-2015. We investigate the change in these ratios between the periods 2000-2007 and 2008-2015. Overall, ΔEC a /ΔCO ratios increase by 0.7-82% and ΔEC a /ΔNO x by 6.8-104% across the East and West PADD regions. The urban West showed the largest increase relative to other regions. This is mainly attributed to a 13-23% increase in ΔEC a during the winter and fall seasons and significant reductions in urban ΔNO x (except in winter). We also find that emission ratios derived from the EPA's National Emission Inventory (NEI) overestimate (underestimate) the increase in the observed enhancement ratios in the East (West). Analyses of changes in NEI emissions in the West reveal (a) smaller reductions in NEI emissions for NO x from the off-road sector and (b) an increase in PM 2.5 (particulate matter 2.5 μm or less in diameter) emissions from commercial/residential combustion and smaller reductions in nonroad emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Molecular Cursor Caliper: A Fluorescent Sensor for Dicarboxylate Dianions

We report herein the fluorescent sensing of both aromatic and linear saturated dicarboxylate anions (DC 2- ) (as their tetrabutylammonium salts) with different lengths and shapes in acetonitrile using a single fluorescent probe, i.e., the bis-calix[4]pyrrole-appended 9,14-diphenyl-9,14-dihydrodibenzo[a,c]phe¬nazine (DPAC-bisC4P) incorporating a vibration-induced emission (VIE) phenazine core. Fluorescence titration studies revealed that treating DPAC-bisC4P with dicarboxylate guests capable of forming pseudo-macrocyclic host–guest complexes via multiple hydrogen bonding interactions between the dicarboxylates and calix[4]pyrrole moieties, led to a blue-shift in the emission of the phenazine core. The binding-based fluorescence tuning features of DPAC-bisC4P allows the underlying binding events and inferred structural changes to be monitored in the form of different chromaticity outputs. The analyte-induced dif-ferences in the fluorescence response to DC 2- cover a wide range within the chromaticity diagram and can be visualized readily. The pre-sent system thus functions as a rudimentary dicarboxylate anion sensor. It illustrates the potential benefits associated with combining a tunable VIE core with non-covalent binding interactions and thus sets the stage for the development of new fluorescent chemosensors where a single chemical entity responds to different analytes with a high level of tunability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nonlinear encoding in diffractive information processing using linear optical materials

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

42 ENGINEERING↗

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗

Rheological dynamics and structural characteristics of supramolecular assemblies of β-cyclodextrin and sulfonic surfactants

Cyclodextrins are highly functional compounds with a hydrophobic cavity capable of forming supramolecular inclusion complexes with various classes of molecules including surfactants. The resultant rich nanostructures and their dynamics are an interesting research problem in the area of soft condensed matter and related applications. In this paper, we report novel dynamical supramolecular assemblies based on the complexation of β-cyclodextrin with 3 different sulfonic surfactants, which are sodium hexadecylsulfate, sodium dodecylbenzenesulfonate, and myristyl sulfobetaine. It was observed that a β-cyclodextrin : surfactant/2 : 1 molar ratio was ideal for inducing axial growth and imparting large viscosities in the suspensions. Such complexation processes were accompanied by intriguing nanostructural phase behaviors and rheological properties that were very sensitive to the molecular architecture of sulfonic surfactants. The presence of an amino group in the head group of the surfactant allowed for large viscosities that reached 2.4 × 10 4 Pa s which exhibited gel-like behavior. In contrast, smaller viscosity values with a lower consistency index were observed when a bulky aromatic ring was present instead. DIC microscopy was used to visually probe the microstructure of the systems with respect to sulfonate molecular architecture. Additionally, surface tension measurements, and FTIR and NMR spectroscopies were used to gain insights into the nature of interactions that lead to the complexation and nanostructural characteristics. Finally, mechanics correlating the supramolecular morphologies to the rheological properties were proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗