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NASA NTRS2023

Eastern Indigo Snake (Drymarchon couperi) Shelter Site Use in Peninsular Florida and Implications for Habitat Conservation

Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS) is endemic to the southeastern United States and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial generalized linear models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Cool season tortoise burrow use across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.

Bayesian hierarchical modeling↗

DOE OSTI2022

Integrating multimodal data through interpretable heterogeneous ensembles

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1996

Noninvasive Determination of Bone Mechanical Properties Using Vibration Response: A Refined Model and Validation in vivo

Accurate non-invasive mechanical measurement of long bones is made difficult by the masking effect of surrounding soft tissues. Mechanical response tissue analysis (MRTA) offers a method for separating the effects of the soft tissue and bone; however, a direct validation has been lacking. A theoretical analysis of wave propagation through the compressed tissue revealed a strong mass effect dependent on the relative accelerations of the probe and bone. The previous mathematical model of the bone and overlying tissue system was reconfigured to incorporate the theoretical finding. This newer model (six-parameter) was used to interpret results using MRTA to determine bone cross-sectional bending stiffness, EI(sub MRTA). The relationship between EI(sub MRTA) and theoretical EI values for padded aluminum rods was R(sup 2) = 0.999. A biological validation followed using monkey tibias. Each bone was tested in vivo with the MRTA instrument. Postmortem, the same tibias were excised and tested to failure in three-point bending to determine EI(sub 3-PT) and maximum load. Diaphyseal bone mineral density (BMD) measurements were also made. The relationship between EI(sub 3-PT) and in vivo EI(sub MRTA) using the six-parameter model is strong (R(sup 2) = 0.947) and better than that using the older model (R(sup 2) = 0.645). EI(sub MRTA) and BMD are also highly correlated (R(sup 2) = 0.853). MRTA measurements in vivo and BMD ex vivo are both good predictors of scaled maximum strength (R(sup 2) = 0.915 and R(sup 2) = 0.894, respectively). This is the first biological validation of a non- invasive mechanical measurement of bone by comparison to actual values. The MRTA technique has potential clinical value for assessing long-bone mechanical properties.

Roberts, S. G.↗

DOE OSTI2021

Modeling and Analysis of the Transport and Disposal of Beryllium Moderator Blocks and Greater than Class C (GTCC) Waste

Radioactive waste in the United states is categorized based on its source, radioactivity, and security risk. The categorization method employed by the Nuclear Regulatory Committee (NRC) for low-level radioactive waste is not currently applied to any waste generated by the Department of Energy (DOE) or disposed of in DOE facilities. As a result, there is a large volume of DOE-generated waste with characteristics similar to the NRC’s “Greater than Class C” (GTCC) waste category which presently do not have a path for long-term disposal. Much of this waste cannot undergo the standard commercial disposition process due to it being categorized as Transuranic (TRU) waste under DOE guidelines, due to elevated concentrations of key fission products. Possible solutions to this dilemma are explored in the Environmental Impact Statement for the “Disposal of Greater-Than-Class-C Low Level Radioactive Wave” (EIS-0375). This paper analyzes several EIS possible paths for disposal for this “orphaned” waste. One example that highlights this categorization issue is the spent beryllium cladding that has been extracted from the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) throughout its operational lifetime. These beryllium blocks surround the reactor’s main chambers and components and serve as neutron moderators. This paper will use this particular waste form to analyze the economic and technological viability of transporting and storing this material under the storage and transportation criteria of the Waste isolation Pilot Plant (WIPP), which was deemed the most feasible disposition path according to the EIS. The example of this waste form used in this paper that highlights this categorization issue is the spent beryllium cladding extracted from Idaho National Laboratory’s (INL) Advanced Test Reactor (ATR). These beryllium blocks surround the reactor’s components and serve as neutron moderators, and thus have accumulated high concentration of high-activity transuranic isotopes. This paper analyzed the viability of transporting and storing this material under the storage and transportation criteria of the five primary disposition paths covered in EIS-0375. An array of calculations was carried out to assess the viability of the various disposition paths for the beryllium shipments as well as other waste shipments that fall within the GTCC category. Modeling with MCNP 6.2 was conducted to determine whether the beryllium blocks could be safely stored and transported within a 72-B cask; the standard shipping container for remote-handled, transuranic waste, while also meeting regulatory limits at various disposition paths. A cost analysis of the transportation and long-term disposal paths mentioned in the EIS was also carried out using available data and information from similar waste shipments. Lastly, a geochemical analysis of the various geological repository discussed in the EIS was also carried out using the Geochemists Workbench Release 14. Long-term disposal of the beryllium blocks at the Waste Isolation Pilot Plant (WIPP) proved to be the most cost-effective long-term disposition option of the ones considered in the EIS. A dose rate calculation at both contact and remote-handling distances indicate that the analyzed beryllium shipments should not exceed the exposure limits at any of the considered locations. Greater-than-class-C waste has been left in a regulatory state of limbo for years, resulting in backlogs of inventory across several research sites in the United States. Due to its high activity and presence of transuranic isotopes, it is imperative to ensure that it remains inaccessible and sequestered both in its short-term interim as well as a long-term geologic time scale. The information and assessments done in the paper could potentially serve as a reference for any future shipments of this waste form at the WIPP facility as well as other disposition paths that may be considered in the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Electrochemical impedance spectroscopy (EIS) is an experimental technique ubiquitously used to study electrochemical systems. However, conventional EIS data interpretation through physical and equivalent circuit models is challenging because physical models are problem-specific, and equivalent circuits are often just lumped-element analogs lacking physical meaning. The distribution of relaxation times (DRT) has emerged as a complementary approach to resolve these issues. One drawback of conventional DRT deconvolution is that the EIS data is understood to be (only) a function of frequency (i.e. 1D data) and deconvolved accordingly. This work proposes a novel deconvolution method based on deep neural networks (DNNs), allowing the analysis of multidimensional EIS spectra to bridge data dependency on both frequencies and experimental conditions. Two particularly appealing traits of the deep-DRT method developed in this article are that neither regularization nor specific spacing on the state variables defining the experiment are required. Finally, leveraging DNN to examine complex EIS spectra and their dependence on experimental conditions, this work opens a new research direction in the area of EIS analysis and DRT deconvolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Oak Ridge Response to Versatile Test Reactor Environmental Impact Statement Data Request

The Versatile Test Reactor (VTR) is a fast-spectrum test reactor being developed in the United States under the direction of the US Department of Energy Office of Nuclear Energy (DOE-NE). The VTR mission is to enable accelerated testing of advanced reactor fuels and materials required for advanced reactor technologies. The conceptual design of the 300 MWth sodium-cooled metallic-fueled pool-type fast reactor has been led by the US National Laboratories in collaboration with General Electric–Hitachi and Bechtel National, Inc. In support of the VTR project, DOE issued a Notice of Intent (NOI) in the Federal Register on August 5, 2019, announcing the intent to prepare an Environmental Impact Statement (EIS) in accordance with the National Environmental Policy Act (NEPA) and its implementing regulations. The EIS will evaluate alternatives for a versatile reactor–based fast-neutron source facility and associated facilities for the preparation, irradiation, and post-irradiation examination (PIE) of test/experimental fuels and materials. Specifically, the NOI identified two siting alternatives for the VTR reactor facility: Idaho National Laboratory (INL) or Oak Ridge National Laboratory (ORNL). In addition, the NOI also specified two siting alternatives for VTR fuel fabrication: INL and the Savannah River Site (SRS). This report provides information in response to data requests made to ORNL to fill in site-specific knowledge gaps to develop a high-quality EIS. The responses provided are not required to provide full details in every aspect; instead, they adequately bound possible environmental impacts or provide sufficient information to adequately assess likely environmental impacts. This work is being performed under a subcontract from INL to ORNL using DOE-NE funds and is directed by DOE-NE and DOE-ID. Leidos has been contracted by DOE-NE to write the VTR EIS, so most data requests have come from Leidos but were often routed through INL or DOE-ID. DOE-ID is overseeing the NEPA and EIS processes for the VTR project. Leidos will use the information provided in this report to inform the VTR EIS and will also cite this document to establish a clear, publicly available source of the information. Section 2 of this report briefly describes the proposed ORNL VTR Alternative and illustrates the location of the proposed site for the ORNL VTR Alternative. Sections 3 through 7 provide direct responses to data requests received by ORNL. These sections use a tabular format in which data requests are divided into separate items to be addressed; the items are numbered, the data requests are restated with more topical information included, and then the responses are provided. Initial data requests and follow-on requests for additional information (RAIs) are combined under the original data request fields. Finally, Section 8 presents summarized conclusions and describes future work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1996

Noninvasive Determination of Bone Mechanical Properties using Vibration Response: A Refined Model and Validation in vivo

Accurate non-invasive mechanical measurement of long bones is made difficult by the masking effect of surrounding soft tissues. Mechanical Response Tissue Analysis (MRTA) offers a method for separating the effects of the soft tissue and bone; however, a direct validation has been lacking. A theoretical analysis of wave propagation through the compressed tissue revealed a strong mass effect dependent on the relative accelerations of the probe and bone. The previous mathematical model of the bone and overlying tissue system was reconfigured to incorporate the theoretical finding. This newer model (six-parameter) was used to interpret results using MRTA to determine bone cross-sectional bending stiffness, EI(sub MRTA). The relationship between EI(MRTA) and theoretical EI values for padded aluminum rods was R(exp 2) = 0.999. A biological validation followed using monkey tibias. Each bone was tested in vivo with the MRTA instrument. Postmortem, the same tibias were excised and tested to failure in three-point bending to determine EI(sub 3-PT) and maximum load. Diaphyseal Bone Mineral Density (BMD) measurements were also made. The relationship between E(sub 3-PT) and in vivo EI(sub MRTA) using the six-parameter model is strong (R(exp 2) = 0.947) and better than that using the older model (R(exp 2) = 0.645). EI(MRTA) and BMD are also highly correlated (R(exp 2) = 0.853). MRTA measurements in vivo and BMD ex vivo are both good predictors of scaled maximum strength (R(exp 2) = 0.915 and R(exp 2) = 0.894, respectively). This is the first biological validation of a non-invasive mechanical measurement of bone by comparison to actual values. The MRTA technique has potential clinical value for assessing long-bone mechanical properties.

Roberts, S. G.↗

NASA NTRS1993

Influence of recreational activity and muscle strength on ulnar bending stiffness in men

Bone bending stiffness (modulus of elasticity [E] x moment of inertia [I]), a measure of bone strength, is related to its mineral content (BMC) and geometry and may be influenced by exercise. We evaluated the relationship of habitual recreational exercise and muscle strength to ulnar EI, width, and BMC in 51 healthy men, 28-61 yr of age. BMC and width were measured by single photon absorptiometry and EI by mechanical resistance tissue analysis. Maximum biceps strength was determined dynamically (1-RM) and grip strength isometrically. Subjects were classified as sedentary (S) (N = 13), moderately (M) (N = 18), or highly active (H) (N = 20) and exercised 0.2 +/- 0.2; 2.2 +/- 1.3; and 6.8 +/- 2.3 h.wk-1 (P < 0.001). H had greater biceps (P < 0.0005) and grip strength (P < 0.05), ulnar BMC (P < 0.05), and ulnar EI (P = 0.01) than M or S, who were similar. Amount of activity correlated with grip and biceps strength (r = 0.47 and 0.49; P < 0.001), but not with bone measurements, whereas muscle strength correlated with both EI and BMC (r = 0.40-0.52, P < 0.005). EI also correlated significantly with both BMC and ulnar width (P < 0.0001). Ulnar width and biceps strength were the only independent predictors of EI (r2 = 0.67, P < 0.0001). We conclude that levels of physical activity sufficient to increase arm strength influence ulnar bending stiffness.

NASA Discipline Musculoskeletal↗

NASA NTRS2018

The Use of Gridded Fossil Fuel CO2 Emissions (FFCO2) Inventory for Climate Mitigation Applications: Errors, Uncertainties, and Current and Future Challenges

Emission Inventory (EI) is a fundamental tool to monitor global compliance of greenhouse gases (GHGs) emissions reduction actions. Inventory guidelines provide a best practice to help EI compilers to make comparable national emission estimates, in spite of the differences in data availability across countries and regions. There are a variety of sources of errors and uncertainties, however, that originate beyond what the inventory guidelines define. For example, spatially-explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes, and there are no specific guidelines to disaggregate emission estimates from country scale. On top of that, EIs are fundamentally prone to systematic biases due to the simple calculation methodology and thus an objective evaluation (e.g. atmospheric top-down estimates) is needed to assure the accuracy of the estimates. ODIAC is a global high-resolution (1x1 km) fossil fuel carbon dioxide (CO2) gridded EI that is now often used in atmospheric CO2 modeling. ODIAC is based on disaggregation of national emission estimates made by CDIAC, which is the well accepted standard in the community. The ODIAC emission data product is updated on an annual basis using best available statistical data. Subnational spatial emission patterns are estimated using power plant profiles and satellite-observations of nighttime lights. In addition to the conventional CDIAC gridded data product, ODIAC carries international bunker emissions (shipping and aviation), which allows flux inversion modelers to accurately impose the global total fossil fuel emissions and their horizontal and vertical distribution. We have extensively evaluated ODIAC emissions using fine-grained EIs as well as a high-resolution atmospheric model simulation across different scales (national, subnational/regional, and urban policy relevant) with a focus on the uncertainties associated with the emission disaggregation. We have examined the use of NASA's Black Marble Suomi-NPP/VIIRS nightlight data.

Oda, Tomohiro↗

NASA NTRS2022

Scattered Light in the Hinode/EIS and SDO/AIA Instruments Measured from the 2012 Venus Transit

Observations from the 2012 transit of Venus are used to derive empirical formulae for long- and short-range scattered light at locations on the solar disk observed by the Hinode Extreme ultraviolet Imaging Spectrometer (EIS) and the Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA) instruments. Long-range scattered light comes from the entire solar disk, while short-range scattered light is considered to come from a region within 50” of the region of interest. The formulae were derived from the Fe XII 195.12 Å emission line observed by EIS and the AIA 193 Å channel. A study of the weaker Fe XIV 274.20 Å line during the transit and a comparison of scattering in the AIA 193 Å and 304 Å channels suggests the EIS scattering formula applies to other emission lines in the EIS wavebands. Both formulae should be valid in regions of fairly uniform emission such as coronal holes and quiet Sun, but may be less accurate for faint areas close (around 100”) to bright active regions. The formula for EIS is used to estimate the scattered light component of Fe XII λ195.12 for seven on-disk coronal holes observed between 2010 and 2018. Scattered light contributions of 56%–100% are found, suggesting that these features are dominated by scattered light, consistent with earlier work of Wendeln & Landi. Emission lines from the S X and Si X ions—formed at the same temperature as Fe XII and often used to derive the first ionization potential bias from EIS data—are also expected to be dominated by scattered light in coronal holes.

Transits↗

DOE OSTI2022

Improved Understanding of Zircaloy Hydrogen Pickup Mechanisms in BWRs. NSUF Report (Final Report)

Using an instrumented autoclave operating at 288°C and 1100 psi, electrical impedance spectroscopy (EIS) measurements were taken from three neutron irradiated fuel channel samples (Zircaloy-2 and Zircaloy-4). The three high priority fuel channel samples were tested at 288°C using a simulated PWR water with the AC frequency range extending down through 10 -4 Hz, with corresponding DC measurements to validate the resistance measurements. EIS measurements were also taken at room temperature using a simulated PWR water from four neutron irradiated fuel channel samples (Zircaloy-2 and Zircaloy-4). and four water rod samples (Zircaloy-2). EIS measurements were also performed on unirradiated Zircaloy-2 tubes that were previously oxidized in autoclaves at various temperatures for different time durations to obtain oxide layers of different thicknesses. These experiments proved to be more challenging than anticipated because of the small size of the samples, which required development of fixtures to allow valid testing of the materials, some of which are radioactive, under pressure and temperature. Equivalent circuits were used to model the data. Some samples resulted in Nyquist plots where the arcs were too close to each other resulting in the inability to separate the different oxide morphological thicknesses. Microstructural characterization in the form of optical microscopy, scanning electron microscopy and (scanning) transmission electron microscopy was performed to document the microstructural features of the oxidized Zr-2 and Zr-4 samples (oxide and metal/oxide interface) and to interpret the EIS results. At the start of this work, it was assumed that despite the laminar cracks and porosity visible in both optical and SEM, the oxide scale is dense near the metal, effectively preventing the water from contacting the metal directly. While it is generally accepted that the large laminar cracks pores allow the water to percolate into the oxide film, it is unknown just how deep it can go, and how interconnected is the porosity, which will dictate how much the porosity is connected electrically to the water within the oxide scale. The EIS results demonstrated that there is in fact a distribution of oxide thicknesses over the various samples, and within each material condition, that are communicating to the liquid. This is derived from the fact that Nyquist plots have an arc radius whose center point is often below the Z’ axis, implying that instead of a single oxide thickness, these oxides have a distribution of thicknesses. This distribution of thicknesses produces arcs that are not clearly separable in most of the samples.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Probing transport energies and defect states in organic semiconductors using energy resolved electrochemical impedance spectroscopy

Abstract Determining the relative energies of transport states in organic semiconductors is critical to understanding the properties of electronic devices and in designing device stacks. Futhermore, defect states are also highly important and can greatly impact material properties and device performance. Recently, energy‐resolved electrochemical impedance spectroscopy (ER‐EIS) is developed to probe both the ionization energy (IE) and electron affinity (EA) as well as sub‐bandgap defect states in organic semiconductors. Herein, ER‐EIS is compared to cyclic voltammetry (CV) and photoemission spectroscopies for extracting IE and EA values, and to photothermal deflection spectroscopy (PDS) for probing defect states in both polymer and molecular organic semiconductors. The results show that ER‐EIS determined IE and EA are in better agreement with photoemission spectroscopy measurements as compared to CV for both polymer and molecular materials. Furthermore, the defect states detected by ER‐EIS agree with sub‐bandgap features detected by PDS. Surprisingly, ER‐EIS measurements of regiorandom and regioregular poly(3‐hexylthiophene) (P3HT) show clear defect bands that occur at significantly different energies. In regioregular P3HT the defect band is near the edge of the occupied states while it is near the edge of the unoccupied states in regiorandom P3HT.

14 SOLAR ENERGY↗

DOE OSTI2022

Predicting battery capacity from impedance at varying temperature and state of charge using machine learning

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

25 ENERGY STORAGE↗

DOE OSTI2020

Understanding resistances in capacitive deionization devices

The desalination characteristics of capacitive deionization (CDI) are significantly impacted by the cell resistance. Here we apply electrochemical impedance spectroscopy (EIS) to analyze the resistive properties of a CDI device, which include EIS measurements on a complete assembled cell (in two wire mode) and EIS measurements with additional contacts to decouple electrode/electrolyte and contact impedances (four wire mode). These measurements shed light on the interface between current collector and electrode as well as the internal capacitive and resistive elements of the cell. With two-wire and four-wire EIS measurements, we find that the often observed high-frequency arc(s) in the impedance spectra can be due to contact effects and/or an internal ionic-transfer resistance that is locally in parallel with macropore wall capacitance. In some cases, the two associated arcs in the Nyquist plot are overlapping and present simultaneously, making their identification in the EIS spectra difficult unless both two-wire and four-wire measurements are made. Despite causing a large high-frequency arc in the Nyquist plot, we find that the apparent internal ionic-transfer resistance is negligible compared to the resistances of the electrode, separator, and external electronic components. Here, an investigation of the contact impedance between the electrode and current collector reveals a parallel RC arc that can grow over time if an improper material is used (e.g., titanium). In our system, the electrode ionic resistance dominates the total Ohmic power dissipation, which has implications for scale-up of CDI systems and future improvements of cell design.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Strong long-wavelength electron-phonon coupling in Ta 2 ⁢Ni⁡(Se,S) 5

The search for intrinsic excitonic insulators (EI) has long been confounded by coexisting electron–phonon coupling in bulk materials. Although the ground state of an EI may be difficult to differentiate from density-wave orders or other structural instabilities, excited states offer distinctive signatures. One way to provide clarity is to directly inspect the phonon spectral function for long wavelength broadening caused by phonon interaction with the high velocity EI phason. Here, in this study, we report that the quasi-one-dimensional (quasi-1D) EI candidate Ta 2 NiSe 5 shows extremely anisotropic phonon broadening and softening in the semimetallic normal state. In contrast, such behavior is completely absent in the broken symmetry state of Ta 2 NiSe 5 and in the isostructural Ta 2 NiS 5 , where the latter has a fully gapped normal state. By contrasting the expected phonon lifetimes in the BCS and BEC limits of a putative EI, our results suggest that the phase transition in Ta 2 Ni(Se,S) 5 family is closely related to strong interband electron–phonon coupling. We experimentally determine the dimensionless coupling $\frac{g}{ω_0}$ ∼ 10, revealing Ta 2 Ni(Se,S) 5 as a rare “ultrastrong coupling” material.

Kang, Zhibo [Yale University, New Haven, CT (Unite↗

DOE OSTI2024

High throughput battery failure experimental platform

Addressing the need to increase the sample set to understand the causes of lithium battery thermal runaway, we conceived of an experimental platform with capability to increase the number of runaway experiments (currently 3-5 per week), while also collecting detailed electrochemical impedance spectroscopy measurements (EIS). Once expanded, the platform would enable data collection on 10s to 100s of cells that all experience runaway, thereby creating a statistical database necessary to identify early indication of risk. A primary containment unit to house cylindrical cells of variety NMC811 and of size 21700 (21 mm diameter by 70 mm length) was designed with features such as debris containment, preloaded cells in an exchangeable port, nitrogen ventilation, and exhaust containment. We performed the first overcharge abuse experiments of several 21700 cells, handpicked because of different initial EIS, and demonstrated that EIS changes dramatically during early stages of overcharge, but in a different manner than previous pouch cell experiments. The abuse experiments also revealed the discharge pattern and debris field created during runaway, as well as the cell temperature control and overheat, that must be considered in the primary containment apparatus. We designed and built a switching relay system to permit measurement of EIS without an active charging circuit, and created instrument control software for charging, EIS, and cell temperature control. The late-start funding was insufficient to fully construct the primary containment unit, but the foundational design and knowhow is available for any future work.

25 ENERGY STORAGE↗

NASA NTRS1998

X-33 Environmental Impact Statement: A Fast Track Approach

NASA is required by the National Environmental Policy Act (NEPA) to prepare an appropriate level environmental analysis for its major projects. Development of the X-33 Technology Demonstrator and its associated flight test program required an environmental impact statement (EIS) under the NEPA. The EIS process is consists of four parts: the "Notice of Intent" to prepare an EIS and scoping; the draft EIS which is distributed for review and comment; the final ETS; and the "Record of Decision." Completion of this process normally takes from 2 - 3 years, depending on the complexity of the proposed action. Many of the agency's newest fast track, technology demonstration programs require NEPA documentation, but cannot sustain the lengthy time requirement between program concept development to implementation. Marshall Space Flight Center, in cooperation with Kennedy Space Center, accomplished the NEPA process for the X-33 Program in 13 months from Notice of Intent to Record of Decision. In addition, the environmental team implemented an extensive public involvement process, conducting a total of 23 public meetings for scoping and draft EIS comment along with numerous informal meetings with public officials, civic organizations, and Native American Indians. This paper will discuss the fast track approach used to successfully accomplish the NEPA process for X-33 on time.

McCaleb, Rebecca C.↗