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At least 487 records · Page 27

Protocols for Uranium Carbon Analysis - Testing Protocol Summary

In 1998, Sigma (then MST-6) purchased and installed a new carbon-sulfur analyzer in G105 as part of a substantial refresh of our analytic capabilities. Substantial effort was undertaken to establish analysis protocols for uranium and other materials. This summary describes the following protocols we put in place as a result of these studies: sample xize (for monolithic samples—chip and powder sample may need more current study), flux/accelerant recipe, and sample cleaning process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mitigating Damage to Hybrid Perovskites Using Pulsed-Beam TEM

Using a pulsed-beam transmission electron microscope, we discover a reduction in damage to methylammonium lead iodide (MAPbI3) as compared to conventional beams delivered at the same dose rates. For rates as low as 0.001 e·Å –2 ·s –1 , we find up to a 17% reduction in damage at a total dose of 10 e·Å –2 . We systematically study the effects of number of electrons in each pulse and the duration between pulse arrival. Damage increases for both, though the number of electrons per pulse has a larger effect. A crossover is identified, where a pulsed beam causes more damage than a conventional one. Although qualitatively similar to previous findings, the degree to which damage is reduced in MAPbI3 is less than that observed for other materials (e.g., C36H74), supporting the hypothesis that the effects are material- and damage-mechanism-dependent. Despite this, the observation here of damage reduction for relatively large electron packets (up to 200 electrons per pulse) suggests that MAPbI3 is in fact less susceptible to irradiation than C36H74, which may be related to reported self-healing effects. This work provides insights into damage processes and durability in hybrid perovskites and also illustrates the viability of using pulsed-beam TEM to explore the associated molecular-level routes to degradation, analogous to laser-accelerated energetic pulsed electron beams and the study of damage to biomolecules, cells, and tissues in radiobiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Skin factor and potential formation damage from chemical and mechanical processes in a naturally fractured carbonate aquifer with implications to CO 2 sequestration

Here, in this study, we investigate formation damage due to acidization and water injection tests into the naturally fractured carbonate Middle Duperow Formation at Kevin Dome, Montana, potentially diminishing the chance of a future successful Geological Carbon Sequestration (GCS) project. Multiple well-test analytical models, correlated with core description and lithology data, are used to determine flow behavior and communication between the water injection interval and surrounding formations. An improved three-dimensional (3D) geologic model with dual-continuum matrix and fracture properties is constructed based on most recent seismic, core, and water sample measurements. Brine injection is simulated to verify the interpretation from the analytical models, followed by CO 2 injection simulation. Geochemical calculations are performed to understand the in-situ processes that led to formation damage. Our findings suggest: (1) there are two possible scenarios that could lead to a positive total effective skin factor and permeability decline: partial penetration and formation damage; (2) analytical models indicate a positive total skin factor, contradicting results of a previous study suggesting that the well was mildly stimulated; (3) numerical simulation supports the formation damage hypothesis by matching the pressure buildup observed during the latter two brine injection tests; (4) several mechanical and chemical processes may have occurred during injection to clog the matrix/fracture system: anhydrite fines migration and/or calcite precipitation. We then make preventative suggestions for future GCS projects into carbonate reservoirs and remediation recommendations for GCS operation at the Kevin Dome.

54 ENVIRONMENTAL SCIENCES↗

Single-Molecule Chemical Reactions Unveiled in Molecular Junctions

Understanding chemical processes at the single-molecule scale represents the ultimate limit of analytical chemistry. Single-molecule detection techniques allow one to reveal the detailed dynamics and kinetics of a chemical reaction with unprecedented accuracy. It has also enabled the discoveries of new reaction pathways or intermediates/transition states that are inaccessible in conventional ensemble experiments, which is critical to elucidating their intrinsic mechanisms. Thanks to the rapid development of single-molecule junction (SMJ) techniques, detecting chemical reactions via monitoring the electrical current through single molecules has received an increasing amount of attention and has witnessed tremendous advances in recent years. Research efforts in this direction have opened a new route for probing chemical and physical processes with single-molecule precision. This review presents detailed advancements in probing single-molecule chemical reactions using SMJ techniques. We specifically highlight recent progress in investigating electric-field-driven reactions, reaction dynamics and kinetics, host–guest interactions, and redox reactions of different molecular systems. Finally, we discuss the potential of single-molecule detection using SMJs across various future applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Continuous recovery of phosphoric acid and Rare-Earths containing particles from phosphoric acid sludge using a decanter centrifuge

Recovery of rare earth elements (REEs) from various industrial and natural streams currently draws significant attention in efforts to meet the demands of the manufacturing industry. Among many industrial byproducts and waste streams, phosphoric acid sludge could be one of the most economically feasible resources for the recovery of REEs because solid particles in the sludge contain relatively concentrated REEs, up to 3,000 ppm, while the liquid component of the sludge is valuable phosphoric acid (P 2 O 5 ) that can be recovered and returned to the main product. Due to high viscosity and large solids content (e.g., 30–40 %), however, this byproduct stream requires multistep separation and purification processes. In this study, a single-step process involving a continuous-flow decanter centrifuge (CFDC) was employed to investigate its feasibility for continuous solid/liquid separation from real phosphoric acid sludge. High centrifugal forces generated from up to 1500 G gravity acceleration separate solid particles from the sludge, generating a liquid-rich stream and a solids-rich stream at the exit of the CFDC. A single pass of phosphoric-acid sludge through the CFDC yielded 95 % liquid recovery and 90 % recovery of REEs-containing solids from 20 to 34 wt% solids-containing sludge. Finally, a reduced order model developed for the CFDC operation showed good agreement with experimental data, and preliminary technoeconomic analysis revealed potential process feasibility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress

Tracing and quantifying water fluxes in the hydrological cycle is crucial for understanding the current state of ecohydrological systems and their vulnerability to environmental change. Especially the interface between ecosystems and the atmosphere that is strongly mediated by plants is important to meaningfully describe ecohydrological system functioning. Many of the dynamic interactions generated by water fluxes between soil, plant and the atmosphere are not well understood, which is partly due to a lack of interdisciplinary research. This opinion paper reflects the outcome of a discussion among hydrologists, plant ecophysiologists and soil scientists on open questions and new opportunities for collaborative research on the topic "water fluxes in the soil-plant-atmosphere continuum" especially focusing on environmental and artificial tracers. We emphasize the need for a multi-scale experimental approach, where a hypothesis is tested at multiple spatial scales and under diverse environmental conditions to better describe the small-scale processes (i.e., causes) that lead to large-scale patterns of ecosystem functioning (i.e., consequences). Novel in-situ, high-frequency measurement techniques offer the opportunity to sample data at a high spatial and temporal resolution needed to understand the underlying processes. Here we advocate for a combination of long-term natural abundance measurements and event-based approaches. Multiple environmental and artificial tracers, such as stable isotopes, and a suite of experimental and analytical approaches should be combined to complement information gained by different methods. Virtual experiments using process-based models should be used to inform sampling campaigns and field experiments, e.g., to improve experimental designs and to simulate experimental outcomes. On the other hand, experimental data are a pre-requisite to improve our currently incomplete models. Interdisciplinary collaboration will help to overcome research gaps that overlap across different earth system science fields and help to generate a more holistic view of water fluxes between soil, plant and atmosphere in diverse ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities

Abstract Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Space Charge Forces analytical model for emittance compensation

Space charge forces represent main induced effects in an RF-injector that degrade the beam quality. In this scenario the laser distribution sent on the photocathode acquires an important role in the emittance compensation process, as the slice analysis shows. Starting from the preliminary studies performed on [1], a novel semi-analytical model of space charge forces is proposed in detail for bunch with arbitrary charge distribution to derive expressions of self-induced forces. The performance of the fields at low energy regime (as the field has not expired RF forces) is under present analysis, we can investigate use of this model in low charge regime. Further, the model has been bench-marked with the behavior of the distributions present in the literature and studied for new ones. It has also been applied for the study of the optimization of a C-band hybrid photoinjector now being commissioned, thus explaining the factor two reduction of the emittance observed at the exit of the gun by changing the initial distribution at the cathode.

Carillo, M.↗

Transcriptomics-Based Points of Departure for Daphnia magna Exposed to 18 Per- and Polyfluoroalkyl Substances

Here, per- and polyfluoroalkyl substances (PFAS) represent a large group of contaminants of concern based on their widespread use, environmental persistence, and potential toxicity. Many traditional models for estimating toxicity, bioaccumulation, and other toxicological properties are not well suited for PFAS. Consequently, there is a need to generate hazard information for PFAS in an efficient and cost-effective manner. In the present study, Daphnia magna were exposed to multiple concentrations of 22 different PFAS for 24 h, in 96-well plate format. Following exposure, whole body RNA was extracted and extracts, each representing five exposed individuals, were subjected to RNA sequencing. Following analytical measurements to verify PFAS exposure concentrations, and quality control on processed cDNA libraries for sequencing, concentration-response modeling was applied to the data sets for 18 of the tested compounds, and the concentration at which a concerted molecular response occurred (transcriptomic point of departure; tPOD) was calculated. The tPODs, based on measured concentrations of PFAS, generally ranged from 0.03-0.58 µM (9.9-350 µg/L; interquartile range). In most cases, these concentrations were two orders of magnitude lower than similarly calculated tPODs for human cell lines exposed to PFAS. They were also lower than apical effect concentrations reported for seven PFAS for which some crustacean or invertebrate toxicity data were available, although there were a few exceptions. Despite being lower than most other available hazard benchmarks, Daphnia magna tPODs were, on average, four orders of magnitude greater than the maximum aqueous concentrations of PFAS measured in Great Lakes tributaries. Overall, this high throughput transcriptomics assay with Daphnia magna holds promise as a component of a tiered hazard evaluation strategy employing new approach methodologies.

59 BASIC BIOLOGICAL SCIENCES↗

Helium tribology of Inconel 617 subjected to laser peening for high temperature nuclear reactor applications

Inconel 617 is among the best candidates for utilization in high temperature gas cooled reactor tribo-components. However, the combined effects of sliding contact, along with intermittent idle times and very high temperature material degradation, deteriorates the alloy tribological performance, especially under a helium atmosphere. Laser peening is a surface treatment technique which can enhance the properties at the surface and subsurface by generating deep residual stresses and enhanced microstructure. In this work, we report the tribological behavior of regular laser peened as well as thermally-engineered laser peened Inconel 617 under helium and air atmospheres at 800 °C. In addition to friction and wear studies, the specimens are characterized by different analytical techniques to further understand the mechanisms involved in the peening process and sliding contact. Regardless of the peening process and post-process treatment types, it is observed that laser peening improves the tribological characteristics of Inconel 617. Interestingly, laser peening followed by helium thermal aging shows highly enhanced tribological behavior. This is attributed to the strengthening effect of the laser peening on the surface oxides providing an excellent and lasting protective and lubricating film under helium exposure.

42 ENGINEERING↗

Development and Implementation of a CTF Code Verification Suite.

CTF is a thermal hydraulic subchannel code developed to predict light water reactor (LWR) core behavior. It is a version of Coolant Boiling in Rod Arrays (COBRA) developed by Oak Ridge National Laboratory (ORNL) and North Carolina State University (NCSU) and used in the Consortium for the Advanced Simulation of LWRs (CASL). Here, the existing CTF code verification matrix is expanded, which ensures that the code is a faithful representation of the underlying mathematical model. The suite of code verification tests are mapped to the underlying conservation equations of CTF and significant gaps are addressed. As such, five new problems are incorporated: isokinetic advection, conduction, pressure drop, convection, and pipe boiling. Convergence behavior and numerical errors are quantified for each of the tests and all tests converge at the correct rate to their corresponding analytic solution. A new verification utility that generalizes the code verification process is used to incorporate these problems into the CTF automated test suite.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A simple model for short-range ordering kinetics in multi-principal element alloys

Short-range ordering (SRO) in multi-principal element alloys influences material properties such as strength and corrosion. While some degree of SRO is expected at equilibrium, predicting the kinetics of its formation is challenging. We present a simplified isothermal concentration-wave (CW) model to estimate an effective relaxation time of SRO formation. Estimates from the CW model agree to within a factor of five with relaxation times obtained from kinetic Monte Carlo (kMC) simulations when above the highest ordering instability temperature. Further, the advantage of the CW model is that it only requires mobility and thermodynamic parameters, which are readily obtained from alloy mobility databases and Metropolis Monte Carlo simulations, respectively. The simple parameterization of the CW model and its analytical nature makes it an attractive tool for the design of processing conditions to promote or suppress SRO in multicomponent alloys.

36 MATERIALS SCIENCE↗

Predicting battery capacity from impedance at varying temperature and state of charge using machine learning

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

25 ENERGY STORAGE↗

A new and alternative look at nonlinear Alfvénic states

The formulation for studying nonlinear Alfvenic states, sustainable in Hall Magnetohydrodynamics (HMHD), becomes considerably simpler and more tractable when circularly polarized Beltrami vectors (the eigenstates of linear HMHD) are used as the basis functions. Nonlinear HMHD is, then, reduced to a rather simple looking set of scalar equations from which a model problem of three interacting Beltrami modes is formulated and analytically solved. The triplet interactions span a variety of familiar nonlinear processes leading to a redistribution as well as periodic exchange of energy. The energy exchange processes (whose strength is measured by an energy exchange/depletion time) will, perhaps, play a dominant role in determining the spectral content of an eventual Alfvenic state. All nonlinearities (sensitive functions of the interacting wave vectors) operate at par, and none is dominant over any substantial region of k-space; their intricate interplay prevents a “universal” picture from emerging; few generalizations on the processes that may, for instance, lead to a turbulent state, are possible. However, the theory can definitely claim: (1) the energy tends to flow from lower to higher k and (2) the higher k z (in the direction of the ambient magnetic field) components of a mode with a given k are depleted/oscillate faster—in some cases much faster. It is noteworthy that the mode coupling is the strongest (with the shortest depletion time) when the participating wave vectors are nearly perpendicular; perhaps, an expected consequence of the curl (cross product) nonlinearities. Numerical simulations will be necessary to help create a fully reliable picture.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Synchrotron self-Compton in a radiative-adiabatic fireball scenario: modelling the multiwavelength observations in some Fermi /LAT bursts

Energetic GeV photons expected from the closest and the most energetic Gamma-ray bursts (GRBs) provide a unique opportunity to study the very-high-energy emission as well as the possible correlations with lower energy bands in realistic GRB afterglow models. In the standard GRB afterglow model, the relativistic homogeneous shock is usually considered to be fully adiabatic, however, it could be partially radiative. Based on the external forward-shock scenario in both stellar wind and constant-density medium, we present a radiative-adiabatic analytical model of the synchrotron self-Compton (SSC) and synchrotron processes considering an electron energy distribution with a power-law index of $1\lt p\lt 2$ and $2\le p$. We show that the SSC scenario plays a relevant role in the radiative parameter $\epsilon$, leading to a prolonged evolution during the slow cooling regime. In a particular case, we derive the Fermi/LAT light curves together with the photons with energies $\ge 100$ MeV in a sample of nine bursts from the second Fermi/LAT GRB catalogue that exhibited temporal and spectral indices with $\gtrsim 1.5$ and $\approx 2$, respectively. These events can hardly be described with closure relations of the standard synchrotron afterglow model, and also exhibit energetic photons above the synchrotron limit. We have modelled the multiwavelength observations of our sample to constrain the microphysical parameters, the circumburst density, the bulk Lorentz factor, and the mechanism responsible for explaining the energetic GeV photons.

acceleration of particles↗

A Sparse Tensor Benchmark Suite for CPUs and GPUs

Tensor computations present significant performance chal- lenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the perfor- mance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state- of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view. This benchmark suite along with the synthetic tensor generator is publicly available.

Li, Jiajia↗

Emerging Threats and Technology Investigation: Industrial Internet of Things - Risk and Mitigation for Nuclear Infrastructure

Industries supporting the global nuclear infrastructure striving for cost savings, expansions in efficiency, and convenience are likely to adopt components (e.g., hardware, software) that comprise the Internet of Things (IoT) and Industrial Internet of Things (IIoT). These devices offer potential improvements along with security challenges. Modern conveniences achieved through application of technology have propagated through society in the form of interconnected devices, from doorbells to microwave ovens, commonly referred to as IoT. IoT devices are often Internet-connected devices that are designed to send data back to a cloud-based server, where a smart phone application then presents device status and control options. Home-based IoT applications carry a different set of risks when compared to a business or security environment, where there is also a history of convenience and interconnection. Industrial settings have long relied on specifically designed Supervisory Control and Data Acquisition (SCADA) systems for process control where IIoT devices are intended to inform business decisions and augment traditional processes. A recent National Institute of Standards and Technology (NIST) report provides a distinction between process control and IIoT in that traditional process control is not replaced by IIoT, but rather IIoT devices are intended to enhance industrial processes through additional monitoring of various sensors and application of data analytics models using artificial intelligence (AI) and machine learning (ML) (Fagan, Marron, et al. 2021) (Ross, et al. 2021).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗