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DOE OSTI2024

Nanoconfined Interfaces for Highly Selective Separation of Critical Rare Earth Elements

Industrial demand for rare earth elements (REEs) has surged over the past three decades due to their unique properties that support sustainable energy and new technologies. Separating individual REEs is challenging and hazardous, typically done through liquid-liquid extraction. There is an urgent need for environmentally friendly and efficient separation technologies for REEs. Porous materials offer promising advances for sustainable REE separation via ion-selective capture. We hypothesize that REE separation can be efficiently achieved in reactive nanopores, such as Zr(IV) and Cr(III) metal-organic frameworks (MOFs), through surface functionalization. By integrating material synthesis, interfacial chemistry experiments, theory, computation, and machine learning, we gained insights into the chemical factors controlling REE speciation and their competitive adsorption on MOFs. Our findings show that these materials’ selectivity can be tuned by surface functionalization. The machine learning component addressed ion-specific diffusion based on MOF topology and chemistry.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Spin–Phonon Coupling and Magnetic Transition in an Organic Molecule Intercalated Cr 2 Ge 2 Te 6

The manipulation of spin–phonon coupling in both formations and explorations of magnetism in two-dimensional van der Waals ferromagnetic semiconductors facilitates unprecedented prospects for spintronic devices. The interlayer engineering with spin–phonon coupling promises controllable magnetism via organic cation intercalation. Here, in this study, spectroscopic evidence reveals the intercalation effect on the intrinsic magnetic and electronic transitions in quasi-two-dimensional Cr 2 Ge 2 Te 6 using tetrabutyl ammonium (TBA + ) as the intercalant. The temperature evolution of Raman modes, E g 3 and A g 1 , along with the magnetization measurements, unambiguously captures the enhancement of the ferromagnetic Curie temperature in the intercalated heterostructure. Moreover, the E g 4 mode highlights the increased effect of spin–phonon interaction in magnetic-order-induced lattice distortion. Combined with the first-principle calculations, we observed a substantial number of electrons transferred from TBA + to Cr through the interface. The interplay between spin–phonon coupling and magnetic ordering in van der Waals magnets appeals for further understanding of the manipulation of magnetism in layered heterostructures.

2D magnet↗

DOE OSTI2019

Controlling the stability of Pickering emulsions by pH-responsive nanoparticles

Electrostatic dissipative particle dynamics simulations were conducted to model the interactions between emulsion droplets stabilized by pH-sensitive polyelectrolyte-grafted nanoparticles. Using a steered molecular dynamics approach, a mechanistic study of forced coalescence was performed to probe the resistance between two particle-covered droplets. The degree of ionization of the grafted polyelectrolytes was adjusted to capture the pH responsiveness. The maximal resistance forces were measured to quantitatively discriminate the efficacy of particles in stabilizing emulsions at different degrees of ionization. Through analyzing droplet dynamics, resistance force variation, and electric field, we discovered that the resistance is attributed to direct electrostatic repulsion, the image charge effect near the water–oil interface, and steric hindrance among extended polymers. When the particle density on the droplet surface is relatively low, the increasing resistance forces at higher degrees of ionization can effectively prevent droplet coalescence. Oppositely, the ionization compromises emulsion stability when the particle surface coverage is high. Substantial desorption of particles from the interface was triggered as the degree of ionization increases. Finally, this in turn reduces resistance force and facilitates coalescence. Moreover, the nanoparticles prevent coalescence at high surface coverages by forming dense layers at individual interfaces, while the particle bridges straddling two interfaces were found at low surface coverages, which can also keep the droplets apart.

42 ENGINEERING↗

DOE OSTI2017

Tritium Management Loop Design Status

This report summarizes physical, chemical, and engineering analyses that have been performed to support development of a test loop to study tritium migration in FLiBe (2LiF-BeF 2 ) salts. The loop will operate under turbulent flow, and a schematic of the apparatus has been used to develop a model in Mathcad to suggest flow parameters that should be targeted in loop operation. The introduction of tritium into the loop has been discussed, as well as various means to capture or divert the tritium from egress through a test assembly. Permeation was calculated, starting with the development of a Modelica model for a transport through a nickel window into a vacuum, followed by modification of the model for a FLiBe system with an argon sweep gas on the downstream side of the permeation interface. Results suggest that tritium removal with a simple tubular permeation device will occur readily. Although this system is idealized, it suggests that rapid measurement capability in the loop may be necessary to study and understand tritium removal from the system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Exploring the Psychometrics of Common Post-Scenario Human Factors Questionnaires of Workload, Situation Awareness, and Perceived Difficulty

Abstract: The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the primary mission of sustaining operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R&D). In this effort, the role of human factors engineering (HFE) has been an integral in ensuring new digital upgrades that are part of the overall main control room modernization strategy for various U.S. NPPs optimize human-system performance and do not introduce new human failure modes. In this process, testing and evaluation of concept (i.e., prototype) human machine interfaces (HMIs) are done in operator-in-the-loop studies at various stages of the HFE modification process, such as during design and verification and validation (V&V) [1]. During these studies, licensed operators go through select scenarios using a testbed, such as a full-scale simulator or at a NPP’s training simulator, to identify potential usability issues and provide general design feedback regarding the new digital HMIs. Human-system performance is measured and evaluated through a range of objective and self-report measures that capture aspects of plant performance, task performance, workload, situation awareness, and anthropometric and ergonomic concerns [2]. The use of post-scenario questionnaires is an acceptable method for gathering measures of workload and situation awareness. To this end, the NASA-Task Load Index (TLX) [e.g., 3, 4, & 5] and Situation Awareness Rating Technique (SART) [e.g., 6 & 7] are common standardized questionnaires used for evaluating workload and situation awareness, respectively. When combined, these questionnaires consist of a total of 15 questions, each using a different rating scale. Debriefs are also done through interviews with operators to gather design insight after each scenario. Since operator- and training simulator-time is limited, a concern during this post-scenario period of operator-in-the-loop studies is the limited time available to run scenarios, administer post-scenario questionnaires, and debrief within the allotted simulator time. To address this potential problem, this paper explores the psychometric characteristics of NASA-TLX, SART, and a common one-question post-scenario usability questionnaire (i.e., the single ease question – SEQ [8]) in effort to inform future development of a condensed questionnaire that captures aspects of workload and situation awareness. This exploratory work uses data from four different operator-in-the-loop studies completed under prior LWRS operator-in-the-loop studies. Findings from this paper should support future development of a consolidated questionnaire that may be used in operator-in-the-loop studies, when time is compressed, that conforms to the HFE scientific and regulatory requirements set for NPP control room modernization.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Probabilistic Modeling of Commercial Building Occupancy Patterns Using Location-Based Map Data: Preprint

Considering occupancy patterns is crucial to simulate buildings' energy use. Current energy models use inputs that simplify the actual diversity in occupancy into static occupancy patterns and are not able to represent the numerous variations in occupancy patterns between buildings and across different locations. Recently, inferring occupancy schedules from metered electricity consumption data was used to model occupancy in commercial buildings. However, the translation from metered data to occupancy schedules requires many assumptions that might not capture the reality, and the process is hindered by the availability of data from advanced metering infrastructure. With the development of information technologies, occupancy modeling should not be limited to traditional approaches. The prevalence of social networks and location services with real-time user feedback provides publicly accessible data via Maps Application Programming Interfaces (APIs) such as Google Maps, SafeGraph, Mapbox, Foursquare, etc. This paper presents an automated framework for modeling parametric occupancy patterns using such APIs to calibrate commercial district buildings' energy models. This process includes three main steps: data extraction and processing, parametric schedules generation, and schedules integration. We demonstrated this framework in districts where we used maps API to generate more accurate behavioral patterns for operations and electric vehicle charging events. We used these patterns to determine differences in energy use across key sociodemographic and spatial parameters. The presented method has the potential for worldwide applications. Users can utilize this framework to extract data for selected locations of interest to create more realistic behavioral patterns for commercial facilities across different districts.

building energy modeling↗

DOE OSTI2022

Synchrotron X-ray based particle image velocimetry to measure multiphase streamflow and densitometry

Synchrotron-based radiation techniques are increasingly used in the field of flow condition measurements. As a complicated multiphase flow, cavitation has been investigated in a wide variety of industrial fields, to prevent damages on spillways at large dams or intensify the bacteria eradication rate during water treatments. To control the detrimental effects of cavitating flows, the dynamics of and mechanisms affecting cavitation development shall be visualized and identified in real-time. Due to the large reflection and scattering effects of multiphase flow, standard visible-light imaging technologies cannot penetrate vapor bubbles and provide extremely limited information. In this study, the synchrotron source emitted high-flux X-ray pulses are used to capture flow motion and visualize internal structures with sufficiently high spatial and temporal resolutions. The image processing procedures on raw x-ray images are developed using 2D Fourier transform (FT) and wavelet transform (WT) to implement the contrast enhancement and de-noising. Through the edge detection algorithm, tracking either seeded particles or phase interfaces inside the opaque multiphase flow can be achieved to perform the particle image velocimetry. Further, with decomposing particle-only and particle-eliminated subplots from the X-ray phase-contrast image, the time-resolved velocity and void fraction fields are obtained simultaneously, paving a way for further flow condition analysis such as densitometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Short-Term Groundwater Level Fluctuations Drive Subsurface Redox Variability

As global change processes modify the extent and functions of terrestrial-aquatic interfaces, the variability of critical and dynamic transitional zones between wetlands and uplands increases. However, it is still unclear how fluctuating water levels at these dynamic boundaries alter groundwater biogeochemical cycling. Here, we used high-temporal resolution data along gradients from wetlands to uplands and during fluctuating water levels at freshwater coastal areas to capture spatiotemporal patterns of groundwater redox potential (E h ). We observed that topography influences groundwater E h that is higher in uplands than wetlands; however, the high variability within TAI zones challenged the establishment of distinct redox zonation. Declining water levels generally decreased E h , but most locations exhibited significant E h variability, which is associated with rare instances of short-term water level fluctuations introducing oxygen. The E h -oxygen relationship showed distinct hysteresis patterns, reflecting redox poising capacity at higher E h , maintaining more oxidizing states longer than dissolved oxygen presence. Surprisingly, we observed more frequent oxidizing states in transitional areas and wetlands than in uplands. We infer that occasional oxygen entering specific wetland-upland boundaries acts as critical biogeochemical control points. Finally, high-resolution data can capture such rare yet significant biogeochemical instances, supporting redox-informed models and advancing the predictability of climate change feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Development of Intelligent Monitoring System (IMS) Modules for the Aquistore CO 2 Storage Project

The monitoring data generated at a CO 2 storage site can quickly result in large amounts of data, often requiring significant labor- and time-intensive efforts to process and analyze before being able to make operational decisions. Implementing an intelligent monitoring system (IMS) can reduce the associated data-processing and analysis costs, which can thereby improve the efficiency of the monitoring process and enable real-time operational decision-making. The Energy & Environmental Research Center (EERC) has completed a 3-year project focused on developing an IMS comprising new workflows, algorithms, and a user interface for the automation and integration of geologic carbon dioxide (CO 2 ) storage site-monitoring and simulation data. The design for the IMS was based on using injection and monitoring data acquired from Aquistore, an operating CO 2 storage project. Managed by the Petroleum Technology Research Centre, the Aquistore Project is injecting CO 2 captured from SaskPower’s coal-fired Boundary Dam Power Station into a deep saline formation for geologic storage. Using data from the Aquistore Project provided a real-world basis to develop an IMS.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Computer Simulation of Proton Transport in Fuel Cell Membranes (Final Report)

This DOE-supported research grant focused on understanding the nature of proton transport in complex systems such as proton exchange membranes (PEMs). The most unique aspect of the research was the development and implementation of a novel multiscale reactive molecular dynamics (MS-RMD) methodology. In this approach, covalent bonds can dynamically break and form, allowing one to accurately treat the proton hopping process essential to capturing the physics of proton transport. The Voth group applied this method to proton exchange membrane systems, providing insight into their proton transport mechanism. They found that protons can diffuse most rapidly in the water-rich regions, but that protons actually spend so little time in such regions that transport along the hydrophobic – hydrophilic interface controls the membrane performance. In addition, the group worked to increase understanding of acidic solutions, developing methods for simulating and interpreting experimental infrared vibrational spectroscopy for excess protons (acidic solutions). The group also implemented novel tools for developing proton transport reactive MD models using a relative entropy minimization scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Hybrid Particle Method for Computational Shock Physics

A long-standing area of research for Eulerian shock wave physics codes has been the treatment of strength and damage for materials. Here we present a method that will aid in the analysis of strength and failure in shock physics applications where excessive diffusion of critical variables can occur and control the solution outcome. Eulerian methods excel for large deformation simulations in general but are inaccurate in capturing structural behavior. Lagrangian methods provide better structural response, but finite element meshes can become tangled. Therefore, a technique for merging Lagrangian and Eulerian treatments of material response, within a single numerical framework, was implemented in the Multiple Component computational shock physics hydrocode. The capability is a Lagrangian/Eulerian Particle Method (LEPM) that uses particles to interface a Lagrangian treatment of material strength with a more traditional Eulerian treatment of the Equation of State (EOS). Lagrangian numerical methods avoid the advection diffusion found in Eulerian methods, which typically strongly affects strength constitutive law internal variables, such as equivalent plastic strain, porosity and/or damage. The Lagrangian capability enhances existing capabilities and permits accurate predictions of high rate, large deformation and/or shock of mechanical structures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗

DOE OSTI2020

Deposition pressure dependent structural and optoelectronic properties of ex-situ boron-doped poly-Si/SiO x passivating contacts based on sputtered silicon

Among common methods to form polycrystalline silicon (poly-Si) films for passivating-contact solar cells, physical vapor deposition, in particular sputtering, is the safest one as it does not require any toxic gaseous precursors. One of the critical parameters to control the properties of sputtered silicon films is their deposition pressure. In this work, structural and optoelectronic characteristics of ex-situ boron-doped poly-Si/SiO x passivating contacts, formed from sputtered intrinsic amorphous silicon (a-Si) deposited at different pressures on top of SiO x /c-Si substrates and subjected to a high-temperature boron diffusion step, are investigated. The deposition rate and density of the as-deposited a-Si films increase with reducing pressure. Low-temperature photoluminescence spectra captured from the as-deposited samples at different pressures do not show typical emissions from hydrogenated a-Si. Meanwhile, their Fourier-transform infrared absorption spectra all show Si–H stretching modes, indicating that hydrogen had been initially incorporated into the chemical SiO x layers and eventually hydrogenated the a-Si/SiO x interfaces during the sputtering process. After the high-temperature boron-diffusion step, all hydrogen-related peaks disappear. Lower pressure films (1.5 and 2.5 mTorr) show more consistent improved performance after hydrogen treatments, compared to higher pressure films (4 and 5 mTorr). Overall, the resultant passivating contacts at 2.5 mTorr achieve a low single-side recombination current density Jo of ~9 fA/cm 2 , whereas their contact resistivity is still low at 15 mO cm 2 .

14 SOLAR ENERGY↗

DOE OSTI2025

Three-Dimensional Bubble Fluidics in Architected Porous Media

Gas bubble flows in porous media often exhibit complex and seemingly unpredictable behaviors that are difficult to control. This lack of control limits the ability to design effective devices which manage multiphase flows. Here, we show how the design of 3D printed pores can deterministically control the flow path of an injected gas stream. Open cell structures can be designed to shape the gas/liquid interface with fidelity to control how the two phases are distributed throughout a porous material. The distributed gas volume is free to interact physically and chemically with the surrounding liquid phase, an effect we exploit to create a logical control gate to redirect flows within a lattice. This also allows us to design architectures for reactive capture and aerating bioreactors, resulting in patterned boundaries which can make more effective use of the liquid and gas reagents.

3D microfluidics↗

DOE OSTI2022

Charge density wave activated excitons in TiSe 2 –MoSe 2 heterostructures

Layered materials enable the assembly of a new class of heterostructures where lattice-matching is no longer a requirement. Interfaces in these heterostructures therefore become a fertile ground for unexplored physics as dissimilar phenomena can be coupled via proximity effects. In this article, we identify an unexpected photoluminescence (PL) peak when MoSe 2 interacts with TiSe 2 . A series of temperature-dependent and spatially resolved PL measurements reveal that this peak is unique to the TiSe 2 –MoSe 2 interface, is higher in energy compared to the neutral exciton, and exhibits exciton-like characteristics. The feature disappears at the TiSe 2 charge density wave transition, suggesting that the density wave plays an important role in the formation of this new exciton. We present several plausible scenarios regarding the origin of this peak that individually capture some aspects of our observations but cannot fully explain this feature. These results therefore represent a fresh challenge for the theoretical community and provide a fascinating way to engineer excitons through interactions with charge density waves.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗

DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗