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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of Scalable Reactive Transport Framework for PFAS

Research on PFAS chemicals is extensive and covers all aspects, including analytical quantification, determination of properties, toxicology, ex situ treatment, and in situ remediation. PFAS chemicals are very stable and thus persistent/recalcitrant in the environment. Although there are many unknowns about PFAS chemicals, degradation pathways, reaction rates, etc., many different sorption, oxidation, reduction, biological, and innovative treatment approaches are being developed. Sorption with activated carbon is currently the only fully available in situ treatment technology for PFAS-impacted groundwater. Given the wide array of PFAS chemicals and transformation products, remediation may need multi-step treatment trains to fully address the PFAS contamination. The work here provides kinetic reaction modules that represent an initial cut at functionality representing PFAS migration and reaction in groundwater aquifer flow and transport models. One reaction kinetics module provides a method to model kinetically limited adsorption using a mass transfer model. The second reaction module represents biological transformation of 8:2 FTOH and daughter species, illustrating how a complex reaction pathway network can be represented. Both reaction modules allow for spatially variable parameter values so that a variety of remediation approaches (e.g., a PRB or volumetric treatment or variations in geochemical conditions) can be simulated. The intent with these PFAS reaction modules is to provide tools for practitioners to aid in the selection, design, and assessment of potential in situ PFAS remediation strategies. It is anticipated that, as PFAS remediation technologies and scientific understanding advances, these modules would be refined or replaced to match new knowledge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Comparison of AGR-3/4 Fission Product Transport Model to Measurements and Extraction of Diffusivities via Analytical Fits

A one-dimensional finite-element model based on the MOOSE framework has been constructed, and overall transport estimates are predicted based on available diffusivities and sorption isotherms in the extant literature. The predicted transport profiles are compared to experimental results, where such results are available, including total inventory analysis of gamma emitters, liquid scintillation results for Sr-90 concentrations, radial deconsolidation leach-burn-leach within compacts, tomographic reconstructions based on detailed gamma scanning and destructive physical sampling of several rings. The magnitude of expected transport to the sink ring is generally predicted by the model for silver and cesium, however, modeled detailed concentration profiles are not consistent with experimental results. Strontium and europium transport is not well characterized by model results.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Unraveling Dimensional Tuning: From 2D to 3D in Covalent Organic Frameworks for Enhanced 2e – Oxygen Reduction Reaction

Covalent organic frameworks (COFs) with a two-dimensional (2D) topology have recently emerged as promising catalyst systems for the electrosynthesis of hydrogen peroxide (H 2 O 2 ) from oxygen (O 2 ). However, designing 2D catalysts to achieve higher H 2 O 2 selectivity presents a significant challenge because of the extensive layer stacking and the aggregated active sites located in the basal planes. It results in lower atom utilization, which requires attention. In this study, we present two functionally similar COFs: one with a 2D rhombus topology (2D@BT_TPA-COF) and another with a three-dimensional (3D) noninterpenetrated pts topology (3D@BT_TPA-COF). Both COFs were utilized for the 2e – oxygen reduction reaction (2e – ORR). Tunning the dimensionality from 2D to 3D resulted in an increase in H 2 O 2 selectivity from approximately ~56% to approximately ~96% (at 0.4 V) and a rise in the turnover frequency (TOF) from 0.05 to 0.08 s –1 at 0.3 V. Nonaggregated active site distribution over 3D topology, featuring higher active site exposure, provides better access to the O 2 /electrolyte and facilitates electron transfer leading to higher 2e – ORR activity and selectivity compared to the 2D counterpart.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Manipulating Pore Topology and Functionality to Promote Fluorocarbon-Based Adsorption Cooling

With the worldwide demand for refrigeration and cooling expected to triple, it is increasingly important to search for alternative energy resources to drive the refrigeration cycles with reduced electricity consumption. Recently, adsorption cooling has gained increased attention since energy reallocation in such systems is based on gas adsorption/desorption, which can be driven by waste/natural heat sources. Eco-friendly sorption-based cooling relies on the cyclic transfer of refrigerant gas from a high to low energy state by the pseudo-compression effect resulting from adsorption and desorption. The driving force for energy transfer relies on heat rather than electricity. The performance of a sorption chiller is primarily influenced by this cyclic sorption behavior, which is characterized as the working capacity of the porous sorbent. Thus increases in this working capacity directly translate to a more compact and efficient cooling system. How-ever, a lack of highly effective sorbent/refrigerant pairs lowers cooling performance, and therefore has limited applicability. To this end, synthetic metal-organic frameworks (MOFs) and covalent organic polymers (COPs) possess higher porosity and greater tunability leading to more substantial potential benefits for adsorption, compared to traditional sorbent materials. Similarly, hydrofluorocarbon refrigerants have more favorable applicability given ease of operation above atmospheric pressures due to suitable saturated vapor pressures and boiling points. For these reasons, our work focuses on an ongoing strategy to promote sorption cooling via improvements in the sorbent/refrigerant pair. Specifically, we target the interaction of hydrofluorocarbon refrigerants with MOF/COP materials at a molecular level by interpreting the host-guest chemistry and the role of framework pore topology. These molecular level differences translate to cooling performance, which is described herein. These strategies include engineering framework porosity (i.e., pore size, pore volume) by using elongated organic linkers and stereochemistry control during synthesis; manipulating the sorb-ate/sorbent interaction by introducing functional moieties or unsaturated metal centers to enhance working capacities in narrow pressure ranges; varying pore topology/morphology to impact adsorption isotherm behavior; and leveraging defective sites within the frameworks to further enhance adsorption capability. Here this atomic level understanding of sorb-ate-sorbent interactions is conducted using various in situ experimental techniques such as synchrotron-based X-ray diffraction, X-ray absorption spectroscopy, in situ Fourier transform infrared spectroscopy, and direct sorption energies determinization with calorimetry. Moreover, the experimentally studied interactions and the corresponding adsorption mechanism are corroborated by computational studies using density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations. Using this approach, we envision the capability to engineer designed frameworks with precise molecular control to target refrigerant molecules and thereby enhance the performance of desired working pairs for sorption-based cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Modeling inclusive electron-nucleus scattering with Bayesian artificial neural networks

We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the field, which directly parameterize the cross sections, our approach employs artificial neural networks to represent the longitudinal and transverse response functions. In contrast to cross sections, which depend on the incoming energy, scattering angle, and energy transfer, the response functions are determined solely by the energy and momentum transfer to the system, allowing the angular component to be treated analytically. We assess the accuracy and predictive power of our framework against the extensive data in the quasielastic inclusive electron-scattering database. Additionally, we present novel extractions of the longitudinal and transverse response functions and compare them with previous experimental analysis and nuclear ab-initio calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Ultrastable Zirconium-Based Cationic Metal–Organic Frameworks for Perrhenate Removal from Wastewater

The effective removal of radioactive 99 TcO 4 – anion from nuclear wastewater remains a very difficult unsolved problem. Functional adsorbent materials with high stability, anion-exchange capacity, excellent selectivity, and recyclability are much needed to solve this problem. In this work, we designed two stable cationic metal–organic frameworks (MOFs)—Zr-tcbp-Me and Zr-tcpp-Me—for possible use as adsorbent materials to remove 99 TcO 4 – . Both compounds were synthesized by solvothermal reactions of the tetracarboxylate ligand with zirconium salt, followed by postsynthetic modification (N-methylation). Here, the crystallinity of both zirconium-based MOFs can be well retained under harsh conditions, and they exhibit high adsorption capacity and selectivity toward ReO 4 – anion, a nonradioactive analogue of 99 TcO 4 – . Zr-tcbp-Me and Zr-tcpp-Me demonstrate the highest framework stability toward acidity among all previously reported cationic MOFs that have been tested for perrhenate removal from wastewater.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Can the Rate of a Catalytic Turnover Be Altered by Ligands in the Absence of Direct Binding Interactions?

Second sphere coordination effects ubiquitous in enzymatic catalysis occur through direct interactions, either covalent or non-covalent, with reaction intermediates and transition states. Here, we present herein evidence of indirect second sphere coordination effects in which ligation of water/alkanols far removed from the primary coordination sphere of the active site nevertheless alter energetic landscapes within catalytic redox cycles in the absence of direct physicochemical interactions with surface species mediating catalytic turnovers. Density functional theory, in situ X-ray absorption and infrared spectroscopy, and a wide array of steady-state and transient CO oxidation rate data suggest that the presence of peripheral water renders oxidation half-cycles within two-electron redox cycles over μ m -oxo-bridged trimers in MIL-100(M) more kinetically demanding. Communication between ligated water and the active site appears to occur through the Fe-O-Fe backbone, as inferred from spin density variations on the central μ m -oxygen 'junction'. Evidence is provided for the generality of these second sphere effects in that they influence different types of redox half-cycles or metals, and can be amplified or attenuated through choice of coordinating ligand. Specifically in the case of MIL-100(M) materials, the Cr isostructure can be made to kinetically mimic the Fe variant by disproportionately hindering oxidation half-cycles relative to the reduction half-cycles. Kinetic and spectroscopic inferences presented here significantly expand both the conceptual definition of second sphere effects as well as the palette of synthetic levers available for tuning catalytic redox performance through chemical ligation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bulk and boundary quantum phase transitions in a square Rydberg atom array

Motivated by recent experimental realizations of exotic phases of matter on programmable quantum simulators, we carry out a comprehensive theoretical study of quantum phase transitions in a Rydberg atom array on a square lattice, with both open and periodic boundary conditions. In the bulk, we identify several first-order and continuous phase transitions by performing large-scale quantum Monte Carlo simulations and develop an analytical understanding of the nature of these transitions using the framework of Landau-Ginzburg-Wilson theory. Remarkably, we find that under open boundary conditions, the boundary itself undergoes a second-order quantum phase transition, independent of the bulk. These results explain recent experimental observations and provide important insights into both the adiabatic state preparation of novel quantum phases and quantum optimization using Rydberg atom array platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗
DOE OSTI2024

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multisensor Agile Adaptive Sampling of Convective Storms Driven by Real-time Analytics

Convective storms vertically transport water vapor and condensate from Earth’s surface to the upper troposphere. Life on Earth is fundamentally linked to this transport which determines the hydrological cycle, and the intensity of severe weather responsible for the destruction of life and property. Despite advances in high-resolution modeling and better observational capabilities, the scientific community continues to be confronted with knowledge gaps about convective storms that limit our predictive capabilities. The ongoing developments in the high-resolution Energy Exascale Earth System Model (E3SM), large eddy simulations, and AI-based analytics to evaluate uncertainties are expected to provide a comprehensive framework for new scientific discovery. The model-experiment (MODEX) approach suggests that the aforementioned advancements in model development and AI-based inference techniques should be complemented by similar advancements in the experimental (observational) side so that the former does not outstrip the ability of the latter to provide meaningful constraints. What are the recent advancements in observations that will provide the necessary leap forward in improving our predictive capabilities? To address this question, we propose a new experimental paradigm called Multisensor Agile Adaptive Sampling (MAAS) that capitalizes on advancements in communications (5G), computational resources (edge/fog computing), sensor capabilities, and machine learning (ML) and AI techniques (Kollias et al., 2020). The MAAS framework allows for the collection of higher spatiotemporal resolution and quality observations of convective storms than is traditionally possible. The MAAS framework is scalable and applicable to atmospheric observatories such as those operated by the Department of Energy (DoE) Atmospheric Radiation Measurement (ARM) facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate Interventions Risk Framework White Paper

This work describes a structured, risk-based approach for analyzing questions related to the potential deployment of a climate intervention in any user-established future scenario, and further provides a structured means for considering what the potential consequences of such interventions may be. Because the climatological, technological, and geopolitical conditions are currently not believed to be supportive of climate intervention use, the framework establishes a three-pronged approach to frame key analytical questions predicated on future scenarios of interest to the user. These three pieces are: (1) Building a scenario. (2) Assessing the likelihood of that scenario based on assumptions about an actor and their motivations for deploying a climate intervention. (3) Analyzing the potential consequences of that climate intervention. Finally, results from this consequence analysis are compared against projected consequences of unabated climate change as a complete risk analysis of deploying a climate intervention.

54 ENVIRONMENTAL SCIENCES↗