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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental Constraints on Solid Nitride Phases in Rocky Mantles of Reduced Planets and Implications for Observable Atmosphere Compositions

Abstract Astronomical surveys have discovered thousands of transiting exoplanets, revealing that rocky planets are common in the galaxy. A planet's interior chemistry is frequently inferred by average density, described by mass‐radius (M‐R) relationships. However, M‐R relationships give rise to non‐unique interpretations of a planet's interior composition, an issue that limits our ability to characterize far‐away worlds. We present experimental and density functional theoretical results addressing the influence of an ultra‐reducing (oxygen‐poor) interior chemistry on rocky mantle phases and discuss the possible implications for atmospheric observables. We show that silicon carbide (SiC) and molecular nitrogen (N 2 ) react to form solid silicon nitride (γ‐Si 3 N 4 ) at high pressures and high temperatures in a laser‐heated diamond‐anvil cell, consistent with ab initio computations. Si 3 N 4 remains stable under extreme conditions and when quenched to ambient conditions. As SiC is a common compound found under very reducing conditions, these results indicate that nitrogen may form solid phases in an oxygen‐poor rocky planet. If, by sequestering nitrogen in a planet's mantle, the distribution of nitrogen between a planet's interior and atmosphere is altered (i.e., a nitrogen‐rich mantle and nitrogen‐poor atmosphere), these results indicate that there may be atmospheric observables connected to the mantle‐redox state of a rocky planet besides the oxygen‐containing phases ubiquitous in exoplanet literature.

Daviau, Kierstin↗
DOE OSTI2020

Functional Photoresists for Energy Applications. Final Report

Monolithic ultralow-density porous bulk materials have recently attracted much interest due to many emerging applications in the areas of catalysis, energy storage and conversion, and thermal insulation. They are also important components of high energy density (HED) and inertial confinement fusion (ICF) targets. However, despite tremendous progress that has been made in the synthesis of porous materials, deterministic and independent control over microscopic architecture, density and composition remain key issues, and their integration in high precision devices requires cost and time-intensive mechanical machining that not only reduces reproducibility by generating debris but also limits the complexity of the 3D shapes that can be realized. In this project, we overcame these limitations by developing a universal templating capability that provides deterministic and independent control over density, composition, architecture, and macroscopic sample shape. This was achieved by developing the technology to 1) 3D print ultrahigh resolution, ultra-high precision polymeric micro-lattice templates, 2) coat these templates with the desired materials, and 3) removing the template (Fig. 1a). Atomic layer deposition (ALD) provides the atomic scale coating thickness accuracy required for precisely controlling density. While this templating approach had been demonstrated in prior work, limitations in suitable photoresists, 3D print technologies, print design, and template removal techniques did not allow the fabrication of millimeter-sized high-precision parts with sub-micron resolution. To enable this technology, we developed 1) two-photon polymerization (TPP) print designs that enable the fabrication of millimeter-sized, mechanically robust polymeric templates with sub-micron resolution and 2) a continuum level TPP printing simulation capability for additional print design guidance; 3) atomistic models to study photoresist polymerization kinetics and network topography, 4) refractive index matched polymeric and preceramic TPP photoresists, and 5) functional TPP photoresists including porous voxel structures and self-immolative polymer photoresist chemistries; and 6) damage free template removal techniques that enable the fabrication of defect-free high-precision low-density foam components. We also developed a templating approach for pure carbon microlattices with a unique tube-in-tube ligament morphology. As a test platform, we pursued the fabrication of foam liners that promise to further increase the neutron yield in indirect drive ICF experiments by improving implosion symmetry control and coupling between the laser and the deuterium-tritium fuel. This application requires fabrication and integration of a ultra-high precision, millimeter-sized, thin-walled (200-400 micrometer thick), low-density (10-30 mg/cc), high atomic number (high Z) cylindrical foam tube into the gold hohlraum of an indirect drive ICF target (Fig. 1b). While our hohlraum liner test case will mainly find application in HED and ICF experiments, the underlying science will also directly apply to previously developed nanoparticle and additive manufacturing technologies and will advance those techniques as well.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PM2.5 Active Aerosol Collection Field Campaign Report

The long-range transport of aerosols can affect local air quality as well as contribute elements and constituents to mountain watersheds that have potentially positive (e.g., nitrate) and negative (e.g., heavy metals) effects to the local ecosystem. Isotopic analysis of aerosols can be a powerful tool for deconvolving the relative contributions of far-distant and local sources to the composition of collected aerosols. Our field campaign involved the week-long collection of PM2.5 (i.e., particulate matter with an aerodynamic diameter of about 2.5 microns) aerosols on filters, which were returned to the laboratories at Lawrence Berkeley National Laboratory (LBNL) for analysis. The sampling sites were located at the Gothic, Colorado Surface Atmosphere Integrated Field Laboratory (SAIL) Atmospheric Radiation Measurement (ARM) and the Mt. Crested Butte, Colorado SAIL ARM sites. The original intention was to measure the lead (Pb) and strontium (Sr) isotopic compositions of the collected aerosols at high precision to provide constraints on source portioning and attribution, as well as analyze the chemical compositions and nitrogen and carbon isotopic compositions. However, severe blank issues arose that prevented the planned isotopic analyses of Sr, Pb, C, and N and severely affected the analyses of the bulk chemical compositions of the collected aerosols, resulting in the failure of the study. The issue is described in Section 2.0.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploring density and strength variations in asteroid 16 Psyche’s composition with 3D hydrocode modeling of its deepest impact structure

Asteroid 16 Psyche is the largest metallic Main Belt Asteroid and is the subject of a forthcoming NASA mission. The composition of Psyche is still unknown and subject of recent debate. In particular, how much porosity is within Psyche, along with how much of Psyche consists of non-metallic versus metallic materials, are central questions to the issue of Psyche’s composition. If Psyche is indeed predominantly composed of metallic materials, it would need to have considerable porosity (~ 30%–50%) for a composition consistent with its expected bulk density (~ 3.7–4.1 g/cm). In this work, we vary the density and strength of Psyche by including uniform and layered fields of pseudo-microporosity, in addition to investigating the presence of macroscopic voids, i.e., spaces larger than the size of the simulation’s mesh cells, in rubble-pile configurations. Further, all configurations result in bulk densities within the uncertainties of measured values, however the strength of Psyche and the distribution of pseudo-pores are varied. Through 3D computational models of Psyche’s deepest impact structure, we show that Psyche’s composition is unlikely to contain only pseudo-microporosity. Rather, rubble pile structures, which include macroscopic voids, are shown to match the crater’s measured aspect ratio better than simulations of structures that included only pseudo-microporosity.

3D↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and interkingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accuracy of DFT computed oxygen-vacancy formation energies and high-throughput search of solar thermochemical water-splitting compounds

The enthalpy change involved in metal oxide reduction is a key quantity in various processes related to energy conversion and storage, and is of particular interest for computational prediction. Often this prediction involves the simulation of a high temperature reduction process with a 0K methodology like density functional theory (DFT), and it is not infrequent for the high temperature and 0K stable crystal structures to differ. This introduces a conundrum with regards to the choice of crystal structure to utilize in the computation, with approaches in the literature varying and experimental validation remaining scarce. In this work we address both the crystal structure conundrum and the experimental validation, and then apply the insights we gain to guide a high-throughput search for new materials for solar thermochemical water-splitting applications. By computing the DFT+U oxygen vacancy formation energy (ΔE vf ) of a selection of ABO 3 compounds and comparing different crystal structures for each composition, we highlight the issues that arise when the structure utilized in the computation is dynamically unstable at 0K, namely the presence of an artificial lowering of ΔE vf , and the lack of convergence of ΔE vf with cell size. We solve these limitations by identifying and employing a suitable surrogate dynamically stable structure. We then validate the predictive power of our calculations against appositely generated experimental measurements of reduction enthalpy for a series of Hubbard U values, finding an accuracy ranging between 0.2-0.6 eV/O. In light of such conclusions, we revise and expand a previous a high-throughput DFT study on ABO 3 perovskite oxides. As a result, we provide a list of candidate STCH materials, highlight trends with redox-active cation and structural distortion, and identify Mn 4+ , Mn 3+ and Co 3+ as the most promising redox-active cations.

08 HYDROGEN↗
DOE OSTI2021

Developing Practical Models of Complex Salts for Molten Salt Reactors

Molten salt reactors (MSRs) utilize salts as coolant or as the fuel and coolant together with fissile isotopes dissolved in the salt. It is necessary to therefore understand the behavior of the salts to effectively design, operate, and regulate such reactors, and thus there is a need for thermodynamic models for the salt systems. Molten salts, however, are difficult to represent as they exhibit short-range order that is dependent on both composition and temperature. A widely useful approach is the modified quasichemical model in the quadruplet approximation that provides for consideration of first- and second-nearest-neighbor coordination and interactions. Its use in the CALPHAD approach to system modeling requires fitting parameters using standard thermodynamic data such as phase equilibria, heat capacity, and others. A shortcoming of the model is its inability to directly vary coordination numbers with composition or temperature. Another issue is the difficulty in fitting model parameters using regression methods without already having very good initial values. The proposed paper will discuss these issues and note some practical methods for the effective generation of useful models.

Besmann, Theodore M. (ORCID:0000000155980550)↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Percheron Power Archimedes Screw Turbine Analysis (Abstract)

The development of the Percheron Power (Percheron) composite Archimedes screw turbine (AST) is based upon employing newly developed theoretical mathematical models for optimizing the water volume carried through the AST for a given diameter, combined with minimizing efficiency losses due to bypass flow and friction. The key feasibility issues associated with producing the composite blades will be to understand the strength, fatigue, and wear performance of the AST blades and associated components, and how these factors change along the length of the turbine. To get to this understanding of material property requirements, Percheron needs free-surface computational fluid dynamics (CFD) models of the AST designs and to perform interactive modeling between the turbine blade optimization and component design based on finite element method stress analysis (FEA). The workscope and analyses requested by Percheron in the Small Business Voucher (SBV) program are well aligned with PNNL capability and recent work experience in performing CFD and FEA analysis for hydropower and marine energy turbines. The CFD analysis will provide estimates of power, efficiency, and surface forces on the blades. The forces on the blade will be transferred to the FEA analysis that will be used to evaluate the material stresses. The CFD and FEA analyses will be repeated for different AST designs provided to PNNL by Percheron. The work conducted in this SBV project will complement the existing Percheron FOA project by providing better defined parameters for design and material selection for the turbine unit prior to manufacture of initial test units.

42 ENGINEERING↗
DOE OSTI2024

AFM Special Issue Summary - Integrating Surface Flux with Boundary Layer Measurements

To help bridge science topics related to land-atmosphere interactions, we organized a virtual special issue in this journal (Agricultural and Forest Meteorology [AFM]) entitled, “Land-Atmosphere Interactions: Integrating Surface Flux with Boundary Layer Measurements.” The motivation for the special issue was driven by existing disciplinary barriers between research areas that all address land-atmosphere interactions. In particular, it addressed research silos between those who study features of the land surface, surface fluxes (including water, energy, and trace gases), atmospheric boundary layer growth and thermodynamics, and atmospheric composition and aerosols. The special issue sought to bring these communities together to integrate multiple observations across the soil-vegetation-atmosphere continuum with the aim of 1) improving broader understanding of land-atmosphere interactions, feedbacks, and coupling, 2) fostering new collaborations between atmospheric and surface flux scientists, and 3) identifying new paths for integrative research. In this report we provide an overview and synthesis of the special issue.

58 GEOSCIENCES↗
DOE OSTI2022

Lepidocrocite Titanate–Graphene Composites for Sodium-Ion Batteries

To overcome electronic transport issues of layered titanates in sodium-ion batteries, we have designed and synthesized composites of lepidocrocite titanates with reduced graphene oxide through a solution-based self-assembly approach. The parent lepidocrocite titanate (K 0.8 [Ti 1.73 Li 0.27 ]O 4 ) was exfoliated by a soft-chemical approach and mechanical shaking. Exfoliated layered titania sheets (LTO) were then combined with reduced graphene oxide (rGO) layers to assemble into composites through flocculation. Countercations (i.e., Mg 2+ ) were used for the self-assembly of negatively charged titania and rGO nanosheets via flocculation. The carbon content in the composites was tuned from 1 to 17% by changing the ratio of titania and rGO sheets in the mixed colloidal suspensions. Electrodes were processed with as-prepared LTO-rGO composites without any carbon additives and tested in sodium half-cell configurations. Mg + -coagulated LTO-rGO composite electrodes deliver higher capacities than electrodes prepared with coagulated titania sheets and 10% acetylene black in sodium half-cells and display good capacity retention after 50 cycles. Electrochemical impedance spectroscopy results indicate lower charge transfer resistance for LTO-14.5%rGO composites than that of coagulated titania sheets with 10% acetylene black. A power law analysis of cells containing the composites indicate a hybrid mechanism consisting of both surface and diffusional processes. A comparison with a similar system, that of dopamine-derived LTO-C heterostructures, reveal significant differences. While capacities showed a strong dependence on carbon content for the dopamine-derived materials, this was not true for the LTO-rGO composites. Instead, the highest capacity was obtained for the 14.5% rGO sample, with a lower value obtained for the 17% rGO sample. A greater proportion of the redox processes were surface rather than diffusional in nature for the LTO-rGO composites as well.

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

Heterostructured Lepidocrocite Titanate-Carbon Nanosheets for Electrochemical Applications

Lepidocrocite-type titanates that reversibly intercalate sodium ions at low potentials (~0.6 V vs Na/Na + ) are promising anode candidates for sodium-ion batteries. However, large amounts of carbon additives are often used to improve their electrical conductivity and overcome poor cycling performance in the electrode composites. To ameliorate electronic transport issues of lepidocrocite titanate (K 0.8 Ti 1.73 Li 0.27 O 4 , KTL) in sodium-ion batteries, we have designed and synthesized heterostructures of exfoliated lepidocrocite-type titanium oxide (LTO) nanosheets with alternating carbon layers via a solution-based self-assembly approach. Positively charged dopamine (Dopa) was used as the carbon precursor and intercalated between negatively charged exfoliated titania nanosheets through electrostatic interaction. Dopa-intercalated LTO was then annealed under argon to form conductive carbon layers between titania sheets. The carbon content in the heterostructures was controlled by modifying the self-assembly conditions (i.e., pH, stirring duration, and Dopa-to-LTO ratio). Electrodes were prepared using carbonized heterostructures (LTO-C) without adding more carbon to the composites and tested in sodium half-cell configurations. Further, higher capacities and improved capacity retention over 250 cycles and lower impedance were observed, as the carbon content of LTO-C heterostructures was increased from 0% (LTO nanosheets) to 30%. These results indicate that the self-assembly approach for 2D heterostructured electrode materials is a promising strategy to overcome electronic transport limitations of layered transition-metal oxides and improve their electrochemical performance for next-generation energy storage applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Bardeen-Cooper-Schrieffer pairing of composite fermions

Topological pairing of composite fermions has led to remarkable ideas, such as excitations obeying non-Abelian braid statistics and topological quantum computation. Here, we construct a p-wave paired Bardeen-Cooper-Schrieffer (BCS) wave function for composite fermions in the torus geometry, which is a convenient geometry for formulating momentum space pairing as well as for revealing the underlying composite-fermion Fermi sea. Following the standard BCS approach, we minimize the Coulomb interaction energy at half filling in the lowest and the second Landau levels, which correspond to filling factors ν = 1/2 and ν = 5/2 in GaAs quantum wells, by optimizing two variational parameters that are analogous to the gap and the Debye cut-off energy of the BCS theory. Our results show no evidence for pairing at ν = 1/2 but a clear evidence for pairing at ν = 5/2. To a good approximation, the highest overlap between the exact Coulomb ground state at ν = 5/2 and the BCS state is obtained for parameters that minimize the energy of the latter, thereby providing support for the physics of composite-fermion pairing as the mechanism for the 5/2 fractional quantum Hall effect. We discuss the issue of modular covariance of the composite-fermion BCS wave function, and calculate its Hall viscosity and pair correlation function. By similar methods, we look for but do not find an instability to s-wave pairing for a spin-singlet composite-fermion Fermi sea at half-filled lowest Landau level in a system where the Zeeman splitting has been set to zero.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Elucidating Compositional Differences in Halide Perovskites for Normal and Inverted Perovskite Solar Cells

Over the recent few years, extensive research efforts have shifted from normal (n-i-p) to inverted (p-i-n) perovskite solar cells (PSCs), owing to their promising efficiency and operational stability, enabled by low-temperature processing. Despite a fundamentally identical operation principle (only structurally inverted), the optimized perovskite compositions for normal and inverted PSCs differ significantly across the literature, suggesting an underlying design principle for perovskite composition. Here, we unveil the role of cesium cation in enhancing interfacial contact between the perovskite layer and the underlying hole-transporting layer (HTL) in inverted PSCs. Comprehensive in situ and device characterization reveal that cesium incorporation promotes the formation of initial nucleation seeds for heterogeneous nucleation at the perovskite/hydrophobic HTL interface, thereby improving their contact. The resulting compositional heterogeneity explains the focus of recent studies on resolving this issue. This study provides mechanistic insight into designing perovskite compositions to further enhance the performance and longevity of PSCs.

Park, Keonwoo↗
DOE OSTI2023

Harnessing the Hybridization of a Metal-Organic Framework and Superbase-Derived Ionic Liquid for High-Performance Direct Air Capture of CO 2

Direct air capture (DAC) of CO 2 has emerged as the most promising “negative carbon emission” technologies. Despite being state-of-the-art, sorbents deploying alkali hydroxides/amine solutions or amine-modified materials still suffer from unsolved high energy consumption and stability issues. Here, in this work, composite sorbents are crafted by hybridizing a robust metal-organic framework (Ni-MOF) with superbase-derived ionic liquid (SIL), possessing well maintained crystallinity and chemical structures. The low-pressure (0.4 mbar) volumetric CO 2 capture assessment and a fixed-bed breakthrough examination with 400 ppm CO 2 gas flow reveal high-performance DAC of CO 2 (CO 2 uptake capacity of up to 0.58 mmol g -1 at 298 K) and exceptional cycling stability. Operando spectroscopy analysis reveals the rapid (400 ppm) CO 2 capture kinetics and energy-efficient/fast CO 2 releasing behaviors. The theoretical calculation and small-angle X-ray scattering demonstrate that the confinement effect of the MOF cavity enhances the interaction strength of reactive sites in SIL with CO 2 , indicating great efficacy of the hybridization. The achievements in this study showcase the exceptional capabilities of SIL-derived sorbents in carbon capture from ambient air in terms of rapid carbon capture kinetics, facile CO 2 releasing, and good cycling performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Ultralight and fire-extinguishing current collectors for high-energy and high-safety lithium-ion batteries

Inactive components and safety hazards are two critical challenges in realizing high-energy lithium-ion batteries. Metal foil current collectors with high density are typically an integrated part of lithium-ion batteries yet deliver no capacity. Meanwhile, high-energy batteries can entail increased fire safety issues. Here we report a composite current collector design that simultaneously minimizes the ‘dead weight’ within the cell and improves fire safety. An ultralight polyimide-based current collector (9 μm thick, specific mass 1.54 mg cm-2) is prepared by sandwiching a polyimide embedded with triphenyl phosphate flame retardant between two superthin Cu layers (~500 nm). Compared to lithium-ion batteries assembled with the thinnest commercial metal foil current collectors (~6 µm), batteries equipped with our composite current collectors can realize a 16-26% improvement in specific energy and rapidly self-extinguish fires under extreme conditions such as short circuits and thermal runaway.

25 ENERGY STORAGE↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗