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DOE OSTI2022

Control of Structural Hydrophobicity and Cation Solvation on Interlayer Water Transport during Clay Dehydration

Swelling clay hydration/dehydration is important to many environmental and industrial processes. Experimental studies usually probe equilibrium hydration states in an averaged manner and thus cannot capture the fast water transport and structural change in interlayers during hydration/dehydration. Using molecular simulations and thermogravimetric analyses, we observe a two-stage dehydration process. The first stage is controlled by evaporation at the edges: water molecules near hydrophobic sites and the first few water molecules of the hydration shell of cations move fast to particle edges for evaporation. The second stage is controlled by slow desorption of the last 1–2 water molecules from the cations and slow transport through the interlayers. The two-stage dehydration is strongly coupled with interlayer collapse and the coordination number changes of cations, all of which depend on layer charge distribution. Furthermore, this mechanistic interpretation of clay dehydration can be key to the coupled chemomechanical behavior in natural/engineered barriers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Source of Bright Near-Infrared Luminescence in Gold Nanoclusters

Gold nanoclusters with near-infrared (NIR) photoluminescence (PL) have great potential as sensing and imaging materials in biomedical and bioimaging applications. In this work, Au 21 (S-Adm) 15 and Au 38 S 2 (S-Adm) 20 are used to unravel the underlying mechanisms for the improved quantum yields (QY), large Stokes shifts and long PL lifetimes in gold nanoclusters. Both nanoclusters show decent PL QY. In particular, the Au 38 S 2 (S-Adm) 20 nanocluster shows a bright NIR PL at 900 nm with QY up to 15% in normal solvents (such as toluene) at ambient conditions. The relatively lower QY for Au 21 (S-Adm) 15 (4%) compared to Au 38 S 2 (S-Adm) 20 is attributed to the lowest-lying excited state being symmetry-disallowed, as evidenced by the pressure-dependent anti-spectral shift of the absorption spectra compared to PL. Yet, Au 21 (S-Adm) 15 maintains some emissive properties due to a nearby symmetry-allowed excited state. Furthermore, our results show that suppression of non-radiative decay due to the surface “lock rings” which encircle the Au kernel and the surface “lock atoms” which bridge the fundamental Au-kernel units (e.g., tetrahedra, icosahedra, etc.) is the key to obtain high QYs in gold nanoclusters. Here, the complicated excited-state processes and the small absorption coefficient of the band-edge transition lead to the large Stokes shifts and the long PL lifetimes that are widely observed in gold nanoclusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Developments for Novel Module Architecture for Lower CapEx and Improved Recyclability for c-Si PV Modules

Photovoltaic (PV) energy production is currently increasing at a significant rate. A novel module architecture has been demonstrated that has potential for reducing manufacturing cost while improving module reliability and recycling for c-Si PV which utilizes an edge-seal. Referred to as Edge Sealed Module (ESM), this architecture eliminates the vacuum lamination process and cross-linked encapsulants on the interior of the module. Functioning prototypes of c-Si have been fabricated for stress testing in collaboration with National Renewable Energy Laboratories (NREL). These modules are being tested and compared to traditionally manufactured modules. Based on preliminary results, this module architecture is a potentially viable solution for improving the manufacturing cost and recyclability of PV modules while enhancing module performance.

costs↗

DOE OSTI2024

Hybrid Composite Materials and Manufacturing: Fibers, Nano-Fillers and Integrated Additive Processes

This book explores the research and advancements in hybrid composite materials and manufacturing techniques. It encompasses a wide array of subjects, such as hybrid materials, advanced manufacturing processes, and nanocomposites. A distinctive feature of this book is its in-depth examination of recent trends in integrated processes, where traditional manufacturing methods are combined with cutting-edge techniques. Our aim is to equip readers with a comprehensive understanding of the current landscape and future potential of hybrid composites, ensuring they remain informed and up-to-date with the latest developments in the field.

Kumar, Vipin [ORNL] (ORCID:0000000295807098)↗

DOE OSTI2022

GeoTGo: AI/ML software for development of community geothermal resources

For effective and equitable outcomes in achieving the national goal of net-zero carbon emissions, communities must be not only included, but even lead the implementation of innovative green-energy technologies. Collaborations with communities should happen through informed decision-making, community-centered research and engagement of stakeholders at the local, state, and regional levels. Community-led research and implementation are fundamental to achieving success. These collaborations include rule makers, environmental regulators, clean energy industries, and technology researchers and developers. Unfortunately, many green infrastructure initiatives still adhere to a top-down and expert-driven process of site selection and design without awareness and acknowledgment of public engagement needs. This can lead to costly delays, including lawsuits, and ultimately less than desired or lacking outcomes as well as missed opportunities1. Geothermal, like many new technologies whose social and economic impacts are not fully understood, often cause disproportionately high adverse effects on disadvantaged communities. These effects can be related to human health, environmental, climate, and other cumulative impacts, as well as the accompanying economic challenges of these impacts. We are focusing our work on the needs of the New Mexico Native American Pueblos and Tribes (NMP&T). To address these needs, we are developing a novel web-based interactive software and user friendly interface called GeoTGO (https://geotgo.com) that provides everything that is needed for communities to better understand and develop their geothermal resources. We will bridge the gap between technology advancements and community needs by facilitating the interactions between the geothermal industry, regulators, stakeholders, and end-users. GeoTGO will merge data, software (including data analysis, text mining, artificial intelligence, and modeling tools), knowledge, expertise, and experience to provide fast processing and dissemination of the latest information about cutting-edge geothermal technologies to users and communities. More information about the project is available at https://envitrace.com/projects/geotgo.html.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Simulations of Radiation Hydrodynamics in Strongly Coupled Plasmas (Final Report)

Radiation propagates throughout the Universe and is often the only information we have from distant astronomical objects. Additionally, radiation plays a key role in systems ranging from the interior of stars to the establishment of shocks at the edges of forming galaxies to neutron star mergers. Here radiative processes, absorption, emission, and scattering, are fundamental to transport of momentum and energy, as well as, the overall evolution of these and other astrophysical systems. In some cases, radiation-dominant systems are also sufficiently dense and ionized, such that, the ions are strongly coupled (ie electrostatic energy >> thermal energy or Γ >> 1), which will affect radiative processes and the transport of radiation. During this project, we developed a computational ray-tracing algorithm to study radiation transport through a simulated neutron star capsule implosion under conditions relevant to Omega-60 laser experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Ultra-high aspect ratio pores milled in diamond via laser, ion and electron beam mediated processes

Microfabrication techniques are critical for the rapid prototyping and development of applications for cutting edge materials. Recently diamond has gained considerable interest for quantum photonic, biosensing, inertial confinement fusion and magnetometer applications. In this article, ultra-high aspect ratio milling of diamond micropores by photon, ion and electron based methods is reported. A multiphoton absorption laser ablation approach is revealed to rapidly produce sub-10 μm diameter micropores in diamond with an aspect ratio of 14:1 and a tapered profile at the surface interface. Chemically active, oxygen focused ion beam milling produces high-aspect ratio pores in diamond with an aspect ratio of 65:1 and minimal tapering over the length of the pore, overcoming the physical interaction volume limitations imposed in conventional gallium based focused ion beam milling and laser ablation methods. Oxygen-mediated electron beam induced etching is revealed to negate the limitations imposed by physical sputtering mechanisms utilized in focused ion beam milling via the direct initiation of chemical reactions at the receding surface, producing aspect ratios on the order of 200:1. Numerical simulations reveal the physical basis for the superior aspect-ratio pore milling of the oxygen focused ion beam milling and electron beam induced etching methods. Our results demonstrate direct-write methods for the fabrication of ultra-high aspect micropores in diamond and provide insight into the underlying mechanisms of these physical processes. The three methods demonstrated here can be interchanged for applications based on the desired characteristic aspect ratio and process throughput.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Performance Characteristics of the BlueField-2 SmartNIC

High-performance computing (HPC) researchers have long envisioned scenarios where application workflows could be improved through the use of programmable processing elements embedded in the network fabric. Recently, vendors have introduced programmable Smart Network Interface Cards (SmartNICs) that enable computations to be offloaded to the edge of the network. There is great interest in both the HPC and high-performance data analytics (HPDA) communities in understanding the roles these devices may play in the data paths of upcoming systems. This paper focuses on characterizing both the networking and computing aspects of NVIDIA’s new BlueField-2 SmartNIC when used in a 100Gb/s Ethernet environment. For the networking evaluation we conducted multiple transfer experiments between processors located at the host, the SmartNIC, and a remote host. These tests illuminate how much effort is required to saturate the network and help estimate the processing headroom available on the SmartNIC during transfers. For the computing evaluation we used the stress-ng benchmark to compare the BlueField-2 to other servers and place realistic bounds on the types of offload operations that are appropriate for the hardware. Our findings from this work indicate that while the BlueField-2 provides a flexible means of processing data at the network’s edge, great care must be taken to not overwhelm the hardware. While the host can easily saturate the network link, the SmartNIC’s embedded processors may not have enough computing resources to sustain more than half the expected bandwidth when using kernel-space packet processing. From a computational perspective, encryption operations, memory operations under contention, and on-card IPC operations on the SmartNIC perform significantly better than the general-purpose servers used for comparisons in our experiments. Therefore, applications that mainly focus on these operations may be good candidates for offloading to the SmartNIC.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Lost alpha Faraday cup foil noise characterization during Joint European Torus plasma post-processing analysis

Capacitive plasma pickup is a well-known and difficult problem for plasma-facing edge diagnostics. This problem must be addressed to ensure an accurate and robust interpretation of the real signal measurements vs noise. The Faraday cup fast ion loss detector array of the Joint European Torus (JET) is particularly prone to this issue and can be used as a testbed to prototype solutions. The issue of separation and distinction between warranted fast ion signal and electromagnetic plasma noise has traditionally been solved with hardware modifications, but a more versatile post-processing approach is of great interest. This work presents post-processing techniques to characterize the signal noise. While hardware changes and advancements may be limited, the combination with post-processing procedures allows for more rapid and robust analysis of measurements. The characterization of plasma pickup noise is examined for alpha losses in a discharge from JET’s tritium campaign. In addition to highlighting the post-processing methodology, the spatial sensitivity of the detector array is also examined, which presents significant advantages for the physical interpretation of fast ion losses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Ultrasound waveform tomography with spatial and edge regularization

Synthetic-aperture ultrasound tomography systems and methods using scanning arrays and algorithms configured to simultaneously acquire ultrasound transmission and reflection data, and process the data for improved ultrasound tomography imaging, wherein the tomography imaging comprises total-variation regularization, or a modified total variation regularization, particularly with edge-guided or spatially variant regularization.

Huang, Lianjie↗

DOE OSTI2024

Neural network methods for radiation detectors and imaging

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

edge computing↗

DOE OSTI2022

A Double-Edged Sword: The Role of Prior Knowledge in Memory Aging

People accumulate knowledge throughout their lifespan and the accumulated knowledge influences how we encode and retrieve information in memory processing. This study aims to investigate the role of knowledge in associative memory across the adult lifespan, and specifically examines the effects of two material properties that interact with prior knowledge: congruency – whether the material is congruent with people’s prior knowledge, and ambiguity – whether the material is ambiguous to interpret based on prior knowledge. 273 participants (aged 22–70 years old) completed an incidental memory task online. Participants were shown pictures depicting an object in a scene and judged if the object was likely or unlikely to be in the particular scene. Later, in the recognition test, participants were asked to identify if the exact picture was presented earlier. The pictures were manipulated to have varying levels of congruency, meaning that some depicted likely object–scene pairs and some unlikely. We also measured how different the likely/unlikely judgment for each object–scene pair was across all participants to determine the ambiguity level of the object–scene pair: some were more likely to receive diverse responses across people, whereas others are unambiguously consistent (or inconsistent) with common knowledge shared by most people. We used mixed-effects logistic regressions to predict memory outcome for each trial as a function of age, age 2 , congruency/ambiguity, and their interactions. The object–scene pairs perceived as congruent had higher hit rates than incongruent ones, as well as higher false alarm rates, especially in middle-aged and older people. Higher ambiguity was also related to both greater true and false memory, independent of age. Finally, the effect of ambiguity only emerged when the object–scene pair was perceived incongruent. The results suggest that people rely on prior knowledge to process new information and that this reliance improves hit responses, but also induces false memories particularly for middle-aged and older people, suggesting a double-edged role of knowledge in associative memory and its disproportionate influence on memory aging. Over-reliance on knowledge in older adults, which has been suspected in other cognitive processes, may be one of the mechanisms underlying associative memory decrease in aging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

In-situ/operando X-ray absorption spectroscopic investigation of the electrode/electrolyte interface on the molecular scale

A method for performing X-ray absorption spectroscopy (XAS) in the soft X-ray region was developed to investigate the surface-sensitive electron signal of electrode/electrolyte interfaces in common multivalent based organic solvents under in situ/operando conditions. Our approach enables us to probe the molecular-scale structure of electrode interfaces by measuring total electron yield and is suitable for redox systems exhibiting low intrinsic electrochemical current. In-situ F K-edge XAS measurements in a 0.5 M Magnesium bis(trifluoromethanesulfonimide)/2-Methyltetrahydrofuran (Mg(TFSI) 2 /2-MeTHF) electrolyte were carried out to determine the evolution of interfacial species during the electrochemical charging/discharging process. Time-dependent density-functional theory (TD-DFT) simulation indicate that the F K-edge evolution is the result of interfacial chemical environment change driven by electrochemical potential. In addition, we performed the “operando XAS” which runs the CV and collects spectra simultaneously. Furthermore, by using this method, the non-equilibrium state of condensed matter interfaces and interfacial dynamical transient process can be revealed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).

image↗

DOE OSTI2024

Spectroscopic Signature of Metal-hydroxo and Peroxo Species in K-edge X-ray Absorption Spectra

Metal-dioxygen species are important intermediates formed during dioxygen activations by metalloenzymes in various biological processes, by catalysts in fuel cells, and prior to O 2 evolution by photosystem II. In this work, we focus on manganese-porphyrin complexes using tetramesitylporphyrin ligand (TMP) to explore changes in Mn K-edge X-ray absorption spectroscopy (XAS) associated with the formation of Mn-hydroxide and Mn–O 2 peroxide species. With limited spectroscopic characterization of these compounds, Mn K β X-ray emission spectroscopy (XES), XAS, density functional theory (DFT), and time-dependent DFT (TD-DFT) analysis will enhance our understanding of their complex electronic structure. We show that the shape of the pre-edge in the K-edge Mn X-ray absorption near-edge structure (XANES) can serve as a spectroscopic signature of the Mn III -peroxo formation and thus can be used to track the presence of the side-on peroxide as an intermediate in time-resolved or in situ experiments. In conclusion, our results will help to further summarize the spectroscopic fingerprints for peroxo and hydroxo species, addressing the challenge of identifying the reactive metal species in catalytic reactions.

Extended X-ray absorption fine structure↗

DOE OSTI2021

Spectral Signatures of Ultrafast Excited-State Intramolecular Proton Transfer from Computational Multi-edge Transient X-ray Absorption Spectroscopy

Excited state intramolecular proton transfer (ESIPT) is a fundamental chemical process with several applications. Ultrafast ESIPT involves coupled electronic and atomic motions and has been primarily studied using femtosecond optical spectroscopy. X-ray spectroscopy is particularly useful because it is element-specic and enables direct, individual probes of the proton donating and accepting atoms. Here, we report a computational study to resolve the ESIPT in 10-Hydroxybenzo[h]quinoline (HBQ), an intramolecularly hydrogen bonded compound, using time-dependent density functional theory combined with ab initio molecular dynamics (AIMD) and time-resolved X-ray absorption spectroscopy (XAS) computations to track the ultrafast excited-state dynamics. Overall, our results reveal clear X-ray spectral signatures of coupled electronic and atomic motions during and following ESIPT at the oxygen and nitrogen K-edge paving the way for future experiments at X-ray free electron lasers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

58 GEOSCIENCES↗