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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Choosing the Best Modeling Platform for Radiological Risk Assessment Models - 20468

Radiological risk assessments, in the form of performance or safety assessments, are often required under regulations or guidance for remediation of contaminated land, decommissioning of contaminated buildings or structures, and radioactive waste disposal. These risk assessments are usually supported by fate and transport models that address decay and ingrowth of radionuclides, as well as their movement through engineered systems and the natural environment. These models are often projected thousands, or more, years into the future, largely because the radioactive species change through decay and ingrowth, and hence the magnitude of the radioactive effect changes with time. There are many computer codes that are available to address this type of modeling. They range from addressing specific pathways or processes such as infiltration of water, groundwater, surface water, air, biota, diffusion and advection of water and gases, to those that try to couple all processes together to evaluate the impact of fate and transport through the entire system to places in space and time to which access is assumed. These different types of codes are sometimes separated with the monikers process-level and systems-level codes, although it is often not clear that this separation does justice to the capabilities of the many codes that are available to evaluate fate and transport of radionuclides. The focus of this paper is the latter group of modeling codes. Several systems level modeling codes exist and are used. There are differences between these codes in terms of utility, flexibility, complexity and cost. The purpose of this paper is to compare a few of these codes in the context of work currently being performed by the International Atomic Energy Agency (IAEA) Modeling and Data for Radiological Impact Assessments (MODARIA) II Working Group 1 (WG1). The MODARIA II WG1's main focus is how stakeholder engaged decision analysis can, or should, be applied to radiological contamination problems so that better, longstanding, sustainable, solutions are reached. However, the WG1 also recognizes the potential impact of the modeling tools that are chosen to address radiological risk, which is often a primary objective of decision making for radiological problems. Other objectives might also be important, such as constraining costs, obtaining financing, minimizing impact on ecosystems, saving cultural resources, saving jobs, farmland, environmental justice, etc., in a full decision analysis for a given radiological contamination problem, but none of these other objectives have the same types of complex modeling needs as minimize radiological dose. Consequently, a further focus of WG1 is to evaluate the potential impacts on decision making of the choice of fate and transport, and risk assessment, modeling codes that are used to support decision making. The WG1 will produce a report at the end of 2020 that will focus on an approach to effective decision making and stakeholder engagement. The report will also consider the role that performance assessment modeling should play in the decision-making process, including the impact of the choice of modeling tools or computer codes on risk-informed decision making. Several sites around the World have been made available by Member States for these model comparisons, and several modeling tools have been considered. However, the focus of this paper is on two of the sites, one in Belgium and one in Ukraine, and on three of the tools: NORMALYSA (NORM And Legacy Site Assessment); GoldSim{sup C}, and AMBER{sup C}. The final report from this working group will also cover other modeling tools, including RESRAD, and PC-Cream{sup R}. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Role of inner solvation sheath within salt–solvent complexes in tailoring electrode/electrolyte interphases for lithium metal batteries

Functional electrolyte is the key to stabilizing the highly reductive lithium (Li) metal anode (LMA) and high voltage cathode for long life, high energy-density rechargeable Li metal batteries (LMBs). However, fundamental knowledge of the interaction principles between reactive electrodes and electrolytes is still limited. Recently localized high-concentration electrolytes (LHCEs) are emerging as promising electrolyte design strategies for LMBs. They can also serve as an ideal platform for understanding the reactivity characteristics of the inner solvation sheath on electrode surfaces due to their unique solvation structures. Here, we study the effects of a series of LHCEs with model electrolyte solvents (carbonate, sulfone, phosphate and ether) in high voltage LMBs. Varied electrode stabilities exhibited in different LHCEs indicate the intricate synergies between the salt and the solvent on electrode surfaces. Experimental and theoretical analyses reveal an intriguing general rule that the strong interactions between the salt and the solvent in the inner solvation sheath promote their intermolecular proton/charge transfer reactions, which dictates the properties of the electrode/electrolyte interphases and thus the battery performances.

Solvation sheath, salt-solvent complex, electrode/↗
DOE OSTI2022

3D Printed Parahydrophobic Surfaces as Multireaction Platforms

We report Parahydrophobic surfaces (PHSs) composed of arrays of cubic μ-pillars with a double scale of roughness and variable wettability were systematically obtained in one step and a widely accessible stereolithographic Formlabs 3D printer. The wettability control was achieved by combining the geometrical parameters (H = height and P = pitch) and the surface modification with fluoroalkyl silane compounds. Homogeneous distribution of F and Si atoms onto the pillars was observed by XPS and SEM-EDAX. A nano-roughness on the heads of the pillars was achieved without any post-treatment. The smallest P values lead to surfaces with static contact angles (CAs) >150° regardless of the H utilized. Interestingly, the relationship 0.6 ≤ H/P ≤ 2.6 obtained here was in good agreement with the H/P values reported for nano- and submicron pillars. Furthermore, experimental CAs, advancing and receding CAs, were consistent with the theoretical prediction from the Cassie–Baxter model. Structures covered with perfluorodecyltriethoxysilane with high H and short P lead to PHSs. Conversely, structures covered with perfluorodecyltrimethoxysilane exhibited a superhydrophobic behavior. Finally, several aqueous reactions, such as precipitation, coordination complex, and nanoparticle synthesis, were carried out by placing the reactive agents as microdroplets on the parahydrophobic pillars, demonstrating the potential application as chemical multi-reaction array platforms for a large variety of relevant fields in microdroplet manipulation, microfluidics systems, and health monitoring, among others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Scaling Arctic landscape and permafrost features improves active layer depth modeling

Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2026

Modeling rf sheath formation in turbulent tokamak boundary plasma

During ICRF antenna operation, complex interactions between turbulent density profiles, nonlinear RF sheaths, and RF-induced convective transport are observed to alter plasma density in the tokamak edge [D’Ippolito et al., Nucl. Fusion 38, 1543 (1998)]. In this work, we explore the physics of such interactions via numerical modeling, using a nonlinear EM/plasma/sheath code (VSim) and profiles obtained from a fluid plasma turbulence code (Hermes) in a 3D slab domain containing biased side-wall limiters. RF-rectified sheath formation on antenna and limiter surfaces is observed as electromagnetic waves launched by the antenna are refracted through the turbulent density profile. On transport timescales, such sheath potentials have been shown to influence both the mean species density and its RMS fluctuation spectrum [Smithe et al., these proceedings]. On the faster RF timescales, we demonstrate that the converse is also true – regions of high plasma density near material surfaces give rise to the highest sheath potential amplitudes. When density is turbulent and spatially nonuniform, localized regions of high sheath potential (hotspots) may develop where high-density filaments intersect material surfaces. Such hotspots are of particular concern as sources of impurity sputtering, and we explore their behavior in response to changes both to the local plasma density and to antenna operating parameters and structure. Related results exploring the role of Faraday shields and/or enclosing structures in suppressing high sheath potentials for other devices (e.g. SPARC) will also be shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Instrumentation development and testing of a wind turbine blade for sub-scale wake studies

Wind turbine blade aerodynamics and the resulting wake flow are complex, and wind tunnel testing of these flows can provide critical insight. The data from such tests are also valuable for validation of numerical models. For experiments using sub-scale turbines to be useful, the blade aerodynamics and wakes must exhibit the important features observed in utility-scale turbine flows. In this work, ~1 m blades designed to produce physics relevant to larger scale turbines were manufactured and affixed to an existing turbine. One of the blades was instrumented for blade surface pressure measurements. Time-dependent surface pressure measurements coupled with instantaneous measurements of the inflow with Cobra probes and the wake with particle image velocimetry allowed for characterization of the inflow, blade flow, and near wake. The results demonstrate that the instrumentation was effective in characterizing the blade loading and the flow field. For the one test case discussed in this paper, the measurements of inflow, blade loading, and wake properties facilitate understanding of the wake's behavior.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initialization

Obtaining lightweight and accurate approximations of discretized objective functional Hessians in inverse problems governed by partial differential equations (PDEs) is essential to make both deterministic and Bayesian statistical large-scale inverse problems computationally tractable. The cubic computational complexity of dense linear algebraic tasks, such as Cholesky factorization, that provide a means to sample Gaussian distributions and determine solutions of Newton linear systems is a computational bottleneck at large-scale. These tasks can be reduced to log-linear complexity by utilizing hierarchical off-diagonal low-rank (HODLR) matrix approximations. In this work, we show that a class of Hessians that arise from inverse problems governed by PDEs are well approximated by the HODLR matrix format. In particular, we study inverse problems governed by PDEs that model the instantaneous viscous flow of ice sheets. In these problems, we seek a spatially distributed basal sliding parameter field such that the flow predicted by the ice sheet model is consistent with ice sheet surface velocity observations. Here, we demonstrate the use of HODLR Hessian approximation to efficiently sample the Laplace approximation of the posterior distribution with covariance further approximated by HODLR matrix compression. Computational studies are performed which illustrate ice sheet problem regimes for which the Gauss–Newton data-misfit Hessian is more efficiently approximated by the HODLR matrix format than the low-rank (LR) format. We then demonstrate that HODLR approximations can be favorable, when compared to global LR approximations, for large-scale problems by studying the data-misfit Hessian associated with inverse problems governed by the first-order Stokes flow model on the Humboldt glacier and Greenland ice sheet.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Heflin, Stella↗
DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Effect of Laser Beam Profile on Rotating Lattice Single Crystal Growth in Sb2S3 Model Glass

Laser heating of chalcogenide glasses has successfully produced rotating lattice single crystals through a solid-solid transformation. To better understand the nature of complex, orientation-dependent lattice rotation, we designed heat profiles of the continuous wave laser by beam shaping, fabricated larger Sb2S3 crystal dots in Sb2S3 glass, and investigated the lattice rotation where the crystal could grow in all radial directions under a circular thermal gradient. The results show that the rate of lattice rotation is highly anisotropic and depends on crystallographic direction. The nature of this rotation is the same in crystals of different orientation relative to the surface. The growth directions that align with the slip planes show the highest rate of rotation and the rotation rate gradually decreases away from this direction. Additionally, the presence of multiple growth directions results in a complicated rotation system. We suggest that the growth front influences the density of dislocations introduced during growth under confinement and thus affects the lattice rotation rate in these crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterizing the Geometry and Quantifying the Impact of Nanoscopic Electrocatalyst/Semiconductor Interfaces under Solar Water Splitting Conditions

The materials that are receiving the most attention in photoelectrochemical water splitting are metallic nanoparticle electrocatalysts (np-EC) attached to the surface of a semiconductor (SC) light absorber. In these multicomponent systems, the interface between the semiconductor and electrocatalysts critically affects performance. However, the np-EC/SC interface remains poorly understood as it is complex on atomic scales, dynamic under reaction conditions, and inaccessible to direct experimental probes. This contribution sheds light on how the electrocatalyst/semiconductor interface evolves under reaction conditions by investigating the behavior of nickel electrocatalysts (as nanoparticles and films) deposited on silicon semiconductors. Rigorous electrochemical experiments, interfacial atomistic characterization, and computational modeling are combined to demonstrate critical links between the atomistic features of the interface and the overall performance. It is shown that electrolyte-induced atomistic changes to the interface lead to (1) modulation of the charge carrier fluxes and a dramatic decrease in the electron/hole recombination rates and (2) a change in the barrier height of the interface. Additionally, the critical roles of nonidealities and electrocatalyst coverage due to interfacial geometry are explored. Each of these factors must be considered to optimize the design of metal/semiconductor interfaces which are broadly applicable to photoelectrocatalysis and photovoltaic research.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pitting Corrosion in 316L Stainless Steel Fabricated by Laser Powder Bed Fusion Additive Manufacturing: A Review and Perspective

Abstract 316L stainless steel (316L SS) is a flagship material for structural applications in corrosive environments, having been extensively studied for decades for its favorable balance between mechanical and corrosion properties. More recently, 316L SS has also proven to have excellent printability when parts are produced with additive manufacturing techniques, notably laser powder bed fusion (LPBF). Because of the harsh thermo-mechanical cycles experienced during rapid solidification and cooling, LPBF processing tends to generate unique microstructures. Strong heterogeneities can be found inside grains, including trapped elements, nano-inclusions, and a high density of dislocations that form the so-called cellular structure. Interestingly, LPBF 316L SS not only exhibits better mechanical properties than its conventionally processed counterpart, but it also usually offers much higher resistance to pitting in chloride solutions. Unfortunately, the complexity of the LPBF microstructures, in addition to process-induced defects, such as porosity and surface roughness, have slowed progress toward linking specific microstructural features to corrosion susceptibility and complicated the development of calibrated simulations of pitting phenomena. The first part of this article is dedicated to an in-depth review of the microstructures found in LPBF 316L SS and their potential effects on the corrosion properties, with an emphasis on pitting resistance. The second part offers a perspective of some relevant modeling techniques available to simulate the corrosion of LPBF 316L SS, including current challenges that should be overcome.

Voisin, T. (ORCID:0000000287462659)↗
DOE OSTI2022

Controls and process planning strategies for 5-axis laser directed energy deposition of Ti-6Al-4V using an 8-axis industrial robot and rotary motion

Interest in multi-axis additive manufacturing has grown significantly in recent years due to associated benefits that range from improved quality to support for greater geometric complexity. A method of 5-axis Laser Hot-Wire Directed Energy Deposition is developed and demonstrated in this study using an 8-axis industrial robot, comprised of a 6-axis serial link robot and a 2 degree-of-freedom positioner. Coordinated 5-axis motion is used to print Ti-6Al-4V objects predominantly through build plate rotation. Methods of slicing and toolpath generation are presented, including the use of an alternating toolpath direction philosophy and the manipulation of the CAD model surface normal vectors for determining the required build plate tilt angles for maintaining consistent deposition orientations relative to gravity. Another objective of the study was to determine the limits of conventional slicing and layer height control techniques for printing geometries with significant unsupported overhangs. A comparison is made between the behavior of normal and conformal bead alignment in this regard, and the performance of the layer height controller is reported as it relates to process stability and the limits of printing overhang geometries. Unsupported overhangs were successfully printed at 35° without build plate tilt and 45° with build plate tilt. Toolpath velocity and net-shape printing performance are reported as well, with 3D scans of the printed surfaces used for net-shape characterization. In all, the printing of three geometries is presented, including the overhang geometry, a 5-axis toolpath geometry, and a large-scale, multi-bead demonstrator part, which exceeded 500 mm in height and weighed 44 kg (97 lbs).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Epitope topography of agonist antibodies to the checkpoint inhibitory receptor BTLA

B and T lymphocyte attenuator (BTLA) is an attractive target for a new class of therapeutics that attempt to rebalance the immune system by agonizing checkpoint inhibitory receptors (CIRs). Herpesvirus entry mediator (HVEM) binds BTLA in both trans- and cis-orientations. We report here the development and structural characterization of three humanized BTLA agonist antibodies, 22B3, 25F7, and 23C8. We determined the crystal structures of the antibody-BTLA complexes, showing that these antibodies bind distinct and non-overlapping epitopes of BTLA. While all three antibodies activate BTLA, 22B3 mimics HVEM binding to BTLA and shows the strongest agonistic activity in functional cell assays and in an imiquimod-induced mouse model of psoriasis. 22B3 is also capable of modulating HVEM signaling through the BTLA-HVEM cis-interaction. The data obtained from crystal structures, biochemical assays, and functional studies provide a mechanistic model of HVEM and BTLA organization on the cell surface and informed the discovery of a highly active BTLA agonist.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantifying Errors in Effective Cluster Interactions of Lattice Gas Cluster Expansions

The promise of lattice gas (LG) cluster expansions (CEs) is that they can describe a given system property to any level of accuracy since the orthogonal “cluster basis functions” span the complete space of available configurations. Such an approach can be constructed to an arbitrarily large surface of a finite number of distinct adsorption sites. Unfortunately, this is only true for the case of an ideal, fixed lattice decorated with components at precise lattice points (the lattice “sites”) with no distortions or relaxations subsequently allowed. Since most systems, and surfaces specifically, do not conform to such an ideal set of constraints, errors in LG CEs must be expected or CE convergence severely hampered. Beyond this, numerical errors in the provided data can complicate the proper construction of a truly predictive and/or physically significant CE. In this work, we show here how reliance on typical statistical tools like confidence intervals cannot be expected to provide an accurate representation of the uncertainty of effective cluster interactions (ECIs) in the CE due to the nature of the target ab initio data and the nature of CEs themselves. We develop a method for estimating these errors that does not rely on statistical assumptions about the model or data. We then use these ECI errors to quantify fundamental consequences on the uncertainty of ECIs in CEs built from O/Fe(100) data whose surface and adsorbates have been allowed to relax in a typical manner and from O/Fe(100) data whose surface and adsorbates are fixed in ideal lattice positions. We also quantify the effect of using a different density functional theory exchange–correlation functional, using these ECI errors to assess the significance in any deviations. In both cases, our method is shown to have remarkable utility in the quantification of errors in the ECIs of CEs. While we stick to the lattice gas convention in this work, the method is equally applicable to the Ising convention or, in principle, any linear model of sufficient complexity.

08 HYDROGEN↗
DOE OSTI2020

Nonclassical Behavior in Competitive Ion Adsorption at a Charged Solid–Water Interface

Ion adsorption at solid-water interfaces is commonly described by interactions between specific surface sites and adsorbed ions in classical models. However, energetic contributions from non-site-specific ion-ion interactions have not been explored systematically. Here, we report non-classical behaviors observed during competitive adsorption between Sr 2+ and Na + /Rb + at the negatively-charged muscovite mica (001)-water interface, revealing apparent controls of adsorbed ion speciation over the interfacial reactivity. In the absence of competing cations, Sr 2+ adsorbs in approximately equivalent proportions of inner-sphere and outer-sphere complexes, whereas it adsorbs predominantly as an outer-sphere complex in the presence of Na + /Rb + . This transformation of adsorbed Sr 2+ speciation significantly decreases its adsorption strength, as indicated by ~15-fold shift in the Sr 2+ adsorption edge concentration, compared to that calculated from a classical Langmuir isotherm model developed based on site-specific interactions. Finally, these observations highlight the importance of non-site-specific interactions in controlling the energetics of chemical reactions at the charged interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗