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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 OSTI2024

Probing the potential of type V Deep eutectic solvents as sustainable electrolytes

The increasing interest within the scientific community in environmentally friendly solvents has led to a focus on Deep Eutectic Solvents (DES), which have natural components. DES are viewed as alternatives to traditional organic solvents and have the potential to be used as electrolytes. For the first time, transport properties of four Type V Deep Eutectic Salt Solutions (DESS) were accessed to investigate the potential of this technology, selecting precursors ranked as excellent in Eco-Scale metrics. The DESS were composed of terpene and trioctylphosphine oxide (TOPO), and varying concentrations of lithium bis(trifluoromethane)sulfonimide (LiTFSI), and their properties were assessed through self-diffusion, viscosity, density, and conductivity measurements. While Type V DESS are capable of dissolving significant amounts of LiTFSI (up to 30 % molar), their ionic conductivity is low, with values ranging from 3.6·10 –3 to 9.3·10 –2 mS·cm –1 at 25 °C, thus limiting their suitability as electrolytes, for instance, for lithium-ions batteries applications. Similar diffusion coefficients for Li + and TFSI – ions suggest the formation of long-lived ion pairs moving as a neutral species. As a result, future research aims to introduce additives to disrupt contact ion pairs and enhance transport properties, leveraging the sustainable appeal of DES and their use in advanced energy storage technologies.

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

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Beneficial Use Impairments, Degradation of Aesthetics, and Human Health: A Review

In environmental programs and blue/green space development, improving aesthetics is a common goal. There is broad interest in understanding the relationship between ecologically sound environments that people find aesthetically pleasing and human health. However, to date, few studies have adequately assessed this relationship, and no summaries or reviews of this line of research exist. Therefore, we undertook a systematic literature review to determine the state of science and identify critical needs to advance the field. Keywords identified from both aesthetics and loss of habitat literature were searched in PubMed and Web of Science databases. After full text screening, 19 studies were included in the review. Most of these studies examined some measure of greenspace/bluespace, primarily proximity. Only one study investigated the impacts of making space quality changes on a health metric. The studies identified for this review continue to support links between green space and various metrics of health, with additional evidence for blue space benefits on health. No studies to date adequately address questions surrounding the beneficial use impairment degradation of aesthetics and how improving either environmental quality (remediation) or ecological health (restoration) efforts have impacted the health of those communities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

A Unified Analytical Method Greenness Score ( uAMGS ) Quantifies How Microscopic Imaging Is Greener Than Conventional Liquid Chromatography

Green chemistry is a set of principles for assessing, developing, and implementing methods that are safer, more efficient, and less detrimental to the environment. The analytical method greenness score (AMGS) is one of many metrics that attempt to evaluate traditional liquid chromatography (LC) based on the energy consumption of the instrument and the safety, health risks, and environmental impact of the solvents employed. Unfortunately, in practice, the AMGS is primarily focused on traditional separation methods in the pharmaceutical industry and is not amenable to cutting-edge separation science, including miniaturization. To broaden this scope, the unified Analytical Method Greenness Score (uAMGS) is presented here, which clarifies and expands on the underlying mathematics and incorporates both dimensional and uncertainty analysis, enabling its application to a broader range of analytical techniques. The uAMGS is used to compare the greenness of two distinct methods: single-molecule microscopy (SMM) and high-performance liquid chromatography (HPLC), which were used to collect equivalent data. uAMGS determines that SMM is significantly greener than HPLC due primarily to decreased solvent consumption. Overall, the uAMGS should allow chemists ranging from undergraduates to industrial PhDs to assess the greenness of a wide range of separations.

chemical separations↗
DOE OSTI2024

Role of Hydrogen as Fuel in Decarbonizing US Clinker Manufacturing for Cement Production: Costs and CO2 Emissions Reduction Potentials

As a low-carbon fuel, feedstock, and energy source, hydrogen is expected to play a vital role in the decarbonization of high-temperature process heat during the pyroprocessing steps of clinker production in cement manufacturing. However, to accurately assess its potential for reducing CO2 emissions and the associated costs in clinker production applications, a techno-economic analysis and a study of facility-level CO2 emissions are necessary. Assuming that up to 20% hydrogen can be blended in clinker fuel mix without significant changes in equipment configuration, this study evaluates the potential reduction in CO2 emissions (scopes 1 and 2) and cost implications when replacing current carbon-intensive fuels with hydrogen. Using the direct energy substitution method, we developed an Excel-based model of clinker production, considering different hydrogen–blend scenarios. Hydrogen from steam methane reformer (gray) and renewable-based electrolysis (green) are considered as sources of hydrogen fuel for blend scenarios of 5%–20%. Metrics such as the cost of cement production, facility-level CO2 emissions, and cost of CO2 avoided were computed. Results show that for hydrogen blends (gray or green) between 5% and 20%, the cost of cement increases by 0.6% to 16%, with only a 0.4% to 6% reduction in CO2 emissions. When the cost of CO2 avoided was computed, the extra cost required to reduce CO2 emissions is $229 to $358/ metric ton CO2. In summary, although green hydrogen shows promise as a low-carbon fuel, its adoption for decarbonizing clinker production is currently impeded by costs.

Okeke, Ikenna J.↗
DOE OSTI2022

ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3

A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Environmental and economic analyses of chemical recycling via dissolution of waste polyethylene terephthalate

Globally, more than 1000 organizations and 175 nations are facing the plastic waste problem and have realized the need to transition from “linear-to-circular” economy of plastics. While the current mechanical recycling technologies for plastics are struggling to increase the U.S. plastic recycling rates beyond 9%, chemical recycling technologies become important complementary technologies to the predominant mechanical recycling that are needed to realize the circular economy in plastics supply chains. Dissolution is one such chemical recycling technology that can recycle waste plastic back into high-quality virgin grade plastic. However, the environmental and economic impacts of chemical recycling of waste polyethylene terephthalate (PET) via dissolution technology using a green solvent are unknown. Our study evaluated environmental metrics such as greenhouse gas (GHG) emissions and cumulative energy demand, and economic metrics such as net present value (NPV), minimum selling price, payback period, return on investment, and discounted internal rate of return for three dissolution processes with polymer recovery via anti-solvent, evaporation, and cooling precipitation techniques. The dissolution process with evaporation technique was the most economically favorable, whereas that with cooling technique was the most environmentally favorable. The anti-solvent approach had low economic performance and the highest environmental impacts. The NPV for all of these technologies ranged from $2.67 MM to $10.93 MM for a capacity of 8,400 MT/year and was found to be the highest for dissolution with evaporation approach and the least for anti-solvent approach. The cradle-to-gate GHG emissions and energy demand for PET dissolution processes ranged from 1.33-3.77 kg CO2-eq/kg of chemically recycled (CR) PET and18.9-56.1 MJ/kg of CR-PET, respectively. These economic and environmental metrics will be helpful in evaluating the sustainability of circular PET supply chains in the U.S.

09 BIOMASS FUELS↗
DOE OSTI2020

Advanced Laboratory and Field Arrays: Evaluating Sampling Techniques for MHK Biological Monitoring (Task 6)

The overall goal of the task was to identify cost effective biological sampling techniques for MHK environmental monitoring. Protected, demersal, and pelagic fish and selected nektonic invertebrates were surveyed using capture and remote sensing techniques at the PacWave sites. The performance of capture and remote sensing monitoring techniques were evaluated and generic nekton monitoring indices were developed for MHK technologies and sites. In parallel to data collections at the PacWave site, the ability of regression models to characterize, detect, and predict change in acoustic data was evaluated using acoustic data collected in Admiralty Inlet, WA, during a biological monitoring study of the proposed SnoPud tidal turbine project site. Expected outcomes of these efforts included an evaluation of instrumentation and techniques used to monitor biological variability; identification of data streams that can be used to detect and quantify change; and sampling requirements to ensure detection of change in monitored variables.

13 HYDRO ENERGY↗
DOE OSTI2020

Cybersecurity Methods for Grid-Connected Power Electronics

The present work shows a secure-by-design process, defense-in-depth method, and security techniques for a secure distributed energy resource. The distributed energy resource is a cybersecure, solar inverter and battery energy storage system prototype, collectively called the Cybersecure Power Router. Consideration is given to the use of the Smart Green Power Node for a foundation of the present work. Metrics for controller security are investigated to evaluate firmware security techniques. The prototype's ability to mitigate, respond to, and recover from firmware integrity degradation is examined. The prototype shows many working security techniques within the context of a grid-connected, distributed energy resource. Further work is expected in the Cybersecure Power Router project. Consideration is also provided for the migration of the present research and the Smart Green Power Node to realize a pre-production prototype.

Moquin, Stephen Joe↗
DOE OSTI2022

Spatial patterns of snow distribution in the sub-Arctic

Abstract. The spatial distribution of snow plays a vital role in sub-Arctic and Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019; ∼ 2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22 000 data points), we developed simple models of the spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was random forest, using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, TPI), while slope, wind, and fine microtopography factors were less important. The characterization of the SWE spatial distribution patterns will be used to validate and improve snow distribution modeling in the Department of Energy's Earth system model and for improved understanding of hydrology, topography, and vegetation dynamics in the sub-Arctic and Arctic regions of the globe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hybrid Energy System to H2 to Green Steel/Ammonia

Green hydrogen is a vital pathway to decarbonize industrial sectors responsible for the largest portion of greenhouse gas emissions, including ammonia/fertilizer and steel. It takes approximately a GW-scale renewables hybrid-H2 system to fully convert one average U.S. steel plant to a green steel plant, with an annual production capacity of 100 million metric tons per year. The optimized cost-effective operation of renewables-H2 plants are highly dependent on location and the specific end use designs. This project focused on the analysis of GW-scale off-grid, tightly-coupled, co-located systems to reduce overall cost compared with steam methane reform with and without carbon capture and grid connected systems (see Figure 1).

ammonia↗
DOE OSTI2024

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

Nazaralizadeh, Solmaz (ORCID:0009000737004039)↗
DOE OSTI2024

Single crystal growth, chemical defects, magnetic and transport properties of antiferromagnetic topological insulators ( Ge 1 – δ – x Mn x ) 2 Bi 2 Te 5 ( x ≤ 0.47 , 0.11 ≤ δ ≤ 0.20 )

Magnetic topological insulators provide a platform for emergent phenomena arising from the interplay between magnetism and band topology. Here we report the single crystal growth, crystal structure, magnetic and transport properties, as well as the neutron scattering studies of topological insulator series (Ge 1–δ–x Mn x ) 2 ⁢Bi 2 ⁢Te 5 (x ≤ 0.47, 0.11 ≤ δ ≤ 0.20). Upon doping up to x = 0.47, the lattice parameter c decreases by 0.8%, while the lattice parameter a remains nearly unchanged. Significant Ge vacancies and Ge/Bi site mixing are revealed via elemental analysis as well as refinements of the neutron and x-ray diffraction data, resulting in holes dominating the charge transport. At x = 0.47, below 10.8 K, a bilayer A-type antiferromagnetic ordered state emerges, featuring an ordered moment of 3.0(3) μ B /Mn at 5 K, with the c axis as the easy axis. Magnetization data unveils a much stronger effective interlayer antiferromagnetic exchange interaction and a much smaller uniaxial anisotropy compared to MnBi 2 ⁢Te 4 . We attribute the former to the shorter nearest-neighbor Mn-Mn interlayer superexchange path and the latter to the smaller ligand-field splitting in (Ge 1–δ–x ⁢Mn x ) 2 ⁢Bi 2 ⁢Te 5 . Finally, our study demonstrates that this series of materials holds promise for the investigation of the layer Hall effect and quantum metric nonlinear Hall effect.

36 MATERIALS SCIENCE↗
DOE OSTI2025

What regulates decomposition in agroecosystems? Insights from reading the tea leaves

Litter decomposition is a critical Earth process, recycling nutrients and setting a portion of plant tissue on a path toward soil organic matter. Despite this importance, we still lack a good understanding of local factors that regulate decomposition, especially in agroecosystems where management plays an outsized role. Using a narrow range of climate and soils, we buried 1,308 pre-manufactured “litter bags” of differing residue quality (i.e., green and rooibos tea leaves) in 109 plots across several management practices to (1) explore the local controls on decomposition in agroecosystems and (2) test the robustness of the Tea Bag Index (TBI). We found that management practices intended to increase soil ecosystem services, that is, soil health, altered the decomposition of both teas. For example, adding nitrogen fertilizer and implementing perennial cropping decreased the extent of green tea decomposition (carbon-to-nitrogen ratio, or C:N = 12.8). No-tillage increased, but perennial cropping decreased, the rate of rooibos tea decomposition (C:N = 50.1). Cropped prairie accelerated green tea decomposition and increased the extent of red tea decomposition. A random forest regression model showed that soil temperature was the strongest predictor of green tea decomposition, but a soil health score also played a significant role in predicting the mass remaining. Soil texture and nutrient availability best predicted rooibos tea decomposition. Finer textured soils seemed to decelerate rooibos decomposition but increased the extent of decomposition. Furthermore, we demonstrated that the TBI metrics correlated somewhat well with empirically derived decomposition constants and were similarly sensitive to the effects of management. Still, the green tea stabilization factor had a substantial prediction bias. Our study increased our basic understanding of what regulates decomposition in agroecosystems. It also showed that the TBI can be a scientifically rigorous citizen science approach to monitoring changes in soil health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Gauge invariants of linearized gravity with a general background metric

In linearized gravity with distributed matter, the background metric has no generic symmetries, and decomposition of the metric perturbation into global normal modes is generally impractical. This complicates the identification of the gauge-invariant part of the perturbation, which is a concern, for example, in the theory of dispersive gravitational waves (GWs) whose energy–momentum must be gauge-invariant. Here, we propose how to identify the gauge-invariant part of the metric perturbation and the six independent gauge invariants per se for an arbitrary background metric. For the Minkowski background, the operator that projects the metric perturbation on the invariant subspace is proportional to the well-known dispersion operator of linear GWs in vacuum. For a general background, this operator is expressed in terms of the Green’s operator of the vacuum wave equation. Further, if the background is smooth, it can be found asymptotically using the inverse scale of the background metric as a small parameter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Limited Role of Absolute Humidity in Intraurban Heat Variability

Abstract Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. Significance Statement Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

Meteorology & Atmospheric Sciences↗