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DOE OSTI2024

Materials for the Photoluminescence-Based Detection of Economically Critical Metals

Critical metals, such as rare earth elements (REEs), cobalt, lithium, aluminum, nickel, and others, are essential to advanced technologies and renewable energy in particular. Widespread global adoption of renewable energy technologies has spurred dramatic demand increases for these metals; however, the global supply of these metals is highly monopolistic and conventional mining poses economic and environmental challenges. As a result, there is increasing interest in domestic production from alternative resources such as coal and its utilization byproducts. Slow and expensive characterization costs remain a significant barrier for domestic production. Here, luminescent sensing materials and platforms are presented that provide an alternative to the current state-of-the-art characterization methods; highly sensitive and selective sensing materials for cobalt, aluminum, and rare earth elements are presented, as well as compact, inexpensive platforms capable of analyzing signal from these materials for rapid characterization of critical metal content.

Crawford, Scott↗

DOE OSTI2024

Materials for the Photoluminescence-Based Detection of Economically Critical Metals

Crawford, Scott↗

DOE OSTI2022

Characterization of the Interfacial Orientation and Molecular Conformation in a Glass-Forming Organic Semiconductor

The ability to control structure in molecular glasses has enabled them to play a key role in modern technology; in particular, they are ubiquitous in organic light-emitting diodes. While the interplay between bulk structure and optoelectronic properties has been extensively investigated, few studies have examined molecular orientation near buried interfaces despite its critical role in emergent functionality. Direct, quantitative measurements of buried molecular orientation are inherently challenging, and many methods are insensitive to orientation in amorphous soft matter or lack the necessary spatial resolution. To overcome these challenges, we use polarized resonant soft X-ray reflectivity (pRSoXR) to measure nanometer-resolved, molecular orientation depth profiles of vapor-deposited thin films of an organic semiconductor Tris(4-carbazoyl-9-ylphenyl)amine (TCTA). Our depth profiling approach characterizes the vertical distribution of molecular orientation and reveals that molecules near the inorganic substrate and free surface have a different, nearly isotropic orientation compared to those of the anisotropic bulk. Comparison of pRSoXR results with near-edge X-ray absorption fine structure spectroscopy and optical spectroscopies reveals that TCTA molecules away from the interfaces are predominantly planar, which may contribute to their attractive charge transport qualities. Buried interfaces are further investigated in a TCTA bilayer (each layer deposited under separate conditions resulting in different orientations) in which we find a narrow interface between orientationally distinct layers extending across ≈1 nm. Coupling this result with molecular dynamics simulations provides additional insight into the formation of interfacial structure. This study characterizes the local molecular orientation at various types of buried interfaces in vapor-deposited glasses and provides a foundation for future studies to develop critical structure–function relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Cloud Climate Data Records from Geostationary Satellites to Support CERES: Challenges and Recent Progress

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record at all times of day during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. A brief overview of the current CERES/GEO cloud property data record constructed from three generations of GEOsat imagers for CERES Edition-4 is presented. In this version, different algorithms were applied to different satellites to take advantage of as much spectral information as possible for each satellite. Under this approach, the derived cloud properties were found to become accurate (and more similar to MODIS) for the GEOsat imagers with more spectral information. However, this approach also led to some discontinuities in cloud properties across platforms. Significant differences were also found between daytime and nighttime cloud properties. This paper will highlight the major issues in the Edition-4 record and discuss new strategies and early results from the next version of the SatCORPS GEOsat cloud property time series in development for CERES Edition-5 with the goal of improving day-night and cross-platform consistency during the CERES record.

clouds↗

DOE OSTI2024

Fabricated devices for performing bacterial-fungal interaction experiments across scales

Diverse and complex microbiomes are found in virtually every environment on Earth. Bacteria and fungi often co-dominate environmental microbiomes, and there is growing recognition that bacterial-fungal interactions (BFI) have significant impacts on the functioning of their associated microbiomes, environments, and hosts. Investigating BFI in vitro remains a challenge, particularly when attempting to examine interactions at multiple scales of system complexity. Fabricated devices can provide control over both biotic composition and abiotic factors within an experiment to enable the characterization of diverse BFI phenotypes such as modulation of growth rate, production of biomolecules, and alterations to physical movements. Engineered devices ranging from microfluidic chips to simulated rhizosphere systems have been and will continue to be invaluable to BFI research, and it is anticipated that such devices will continue to be developed for diverse applications in the field. This will allow researchers to address specific questions regarding the nature of BFI and how they impact larger microbiome and environmental processes such as biogeochemical cycles, plant productivity, and overall ecosystem resilience. Devices that are currently used for experimental investigations of bacteria, fungi, and BFI are discussed herein along with some of the associated challenges and several recommendations for future device design and applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Multilateral Testing Workshop on Relocatable Portal Monitors

The procurement and deployment of radiation detection equipment is a multi-faceted challenge across the globe. Knowledge sharing of the testing methods used to evaluate equipment for deployment scenarios helps improve the understanding of deployed systems by the global community. The United States Department of Energy’s Nuclear Smuggling Detection and Deterrence Office (NSDD) performed a series of characterization measurements in 2019, focused on radiation detection systems that could function as relocatable portal monitors. The capstone activity for this project was a multilateral and cross-disciplinary workshop demonstrating, performing, and refining the techniques used in the characterization measurements with International Atomic Energy Agency (IAEA) and global partners. A group of technical experts including representatives from nine countries, the IAEA, and NSDD gathered at Sandia National Laboratories in Albuquerque, NM during September of 2019 for the Operational and Performance Testing and Evaluation of Relocatable Portals Workshop. Over the course of a week, the group discussed the necessity for and process of developing operational and performance requirements, challenges related to deploying radiation detection systems for the countries in attendance, use cases for deploying relocatable radiation detection equipment, and considerations related to testing equipment for appropriate deployment scenarios. All participants were given the opportunity to have hands-on experience with eleven relocatable systems in executing a limited scope characterization test comprised of six test scenarios. At the conclusion of testing, participants reviewed and presented their findings, and the group as a whole compared and contrasted their experiences. Suggested revisions to the test methods and ideas for further collaborations were discussed. This report discusses the logistical and planning considerations for bringing together experts across disciplines in a multilateral workshop on testing of radiation detectors, as well as presenting the testing methods performed, results, and paths forward. Success of workshops related to technical knowledge transfer and improvement of test methods is key to global technical and scientific support of radiation detection deployments.

Erchinger, Jennifer L.↗

DOE OSTI2021

Separate-Effects Tests for Studying Temperature-Gradient-Driven Cracking in UO 2 Pellets

We report a variety of normal operation and accident scenarios can generate thermal stresses large enough to cause cracking in light-water reactor (LWR) fuel pellets. Cracking of fuel pellets can lead to reduced heat removal, higher centerline temperatures, and localized stress in cladding, all of which impact fuel performance. It is important to experimentally characterize the thermal and mechanical behavior in the pellet before and after cracking to improve cracking models in fuel performance codes. However, in-reactor observation and measurement of cracking is very challenging due to the harsh environment and logistics. Recently, an experimental pellet cracking test stand was developed for separate effects testing of normal operations and accident temperature conditions, using thermal imaging to capture the pellet surface temperature for evaluation of thermal stresses and optical imaging to capture the evolution of cracking in real time. Experiments were performed using depleted uranium dioxide (UO 2 ) pellets, which are useful for collecting data valuable for development and validation of cracking models. A combination of induction and resistance heating was used to create an average temperature gradient of 236°C/cm and 193°C/cm before and after cracking respectively. Characterization of the pellets were done before as well as after cracking. The cracking patterns are somewhat different than those expected in a typical reactor because of the differences in thermal conditions and pellet microstructure. However, if the actual conditions of these experiments are reproduced in computational models, these out-of-pile tests on UO 2 pellets provide relevant data for modeling purposes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Characterizing Quantum Classifier Utility in Natural Language Processing Workflows

Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.

Hamilton, Kathleen↗

DOE OSTI2024

The Urban Deployment Model: A Toolset for the Simulation and Performance Characterization of Radiation Detector Deployments in Urban Environments

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Synthesis, Characterization, and Utilization of a Lignin-Based Adsorbent for Effective Removal of Azo Dye from Aqueous Solution

How to effectively remove toxic dyes from the industrial wastewater using a green low-cost lignocellulose-based adsorbent, such as lignin, has become a topic of great interest but remains quite challenging. In this study, cosolvent-enhanced lignocellulosic fractionation (CELF) pretreatment and Mannich reaction were combined to generate an aminated CELF lignin which is subsequently applied for removal of methylene blue and direct blue (DB) 1 dye from aqueous solution. 31P NMR was used to track the degree of amination, and an orthogonal design was applied to determine the relationship between the extent of amination and reaction parameters. The physicochemical, morphological, and thermal properties of the aminated CELF lignin were characterized to confirm the successful grafting of diethylenetriamine onto the lignin. The aminated CELF lignin proved to be an effective azo dye-adsorbent, demonstrating considerably enhanced dye decolorization, especially toward DB 1 dye (>90%). It had a maximum adsorption capacity of DB 1 dye of 502.7 mg/g, and the kinetic study suggested the adsorption process conformed to a pseudo-second-order kinetic model. The isotherm results also showed that the modified lignin-based adsorbent exhibited monolayer adsorption. The adsorbent properties were mainly attributed to the incorporated amine functionalities as well as the increased specific surface area of the aminated CELF lignin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Multigene engineering in plants: Technologies, applications, and future prospects

The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

AI-aided plant engineering↗

DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗

DOE OSTI2023

Organizational System Resilience to Disinformation: A Viable Systems Model Exploration

This paper explores the utility of organizational system modeling frameworks to provide valuable insight into information flows within organizations and subsequently the opportunities for increasing resilience against disinformation campaigns targeting the system's ability to utilize information within its decision making. Disinformation is a growing challenge for many organizations and in recent years has created delay in decision making. Here the paper has utilized the viable systems model (VSM) to characterize organizational systems and used this approach to outline potential subsystem requirements to promote resilience of the system. The results of this paper can support the development of simulations and models considering the human elements within the system as well as support the development of quantitative measures of resilience.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

An emerging class of carbon materials: Synthesis and applications of carbon flowers

Porous carbon materials are widely applied in energy storage, gas adsorption, and catalysis applications because of their advantageous properties, such as high surface areas, tunable surface properties, chemical stability, high conductivity, and low cost. Recently, a flower-shaped carbon material, referred to as carbon flowers (CFs), has received attention as an emerging porous carbon material because of its unique shape. In this review, we summarize the synthetic strategies, characterization techniques, and formation mechanisms of CFs from various precursors. We also review the applications of CFs and their pros and cons. Last, we discuss challenges and perspectives for future development of CFs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

New Views of Old Proteins: Clarifying the Enigmatic Proteome

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide provocative projections for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we offer potential futures for a next generation of disruptive approaches to define, quantify and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗