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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 sources NASA NTRS DOE OSTI

DOE OSTI2021

Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.

14 SOLAR ENERGY↗

NASA NTRS1976

OAST Space Theme Workshop. Volume 2: Theme summary. 5: Global service (no. 11). A. Statement. B. 26 April 1976 presentation. C. Summary

The benefits to be obtained from cost-effective global observation of the earth, its environment, and its natural and man-made features are examined using typical spacecraft and missions which could enhance the benefits of space operations. The technology needs and areas of interest include: (1) a ten-fold increase in the dimensions of deployable and erectable structures to provide booms, antennas, and platforms for global sensor systems; (2) control and stabilization systems capable of pointing accuracies of 1 arc second or less to locate targets of interest and maintain platform or sensor orientation during operations; (3) a factor of five improvements in spacecraft power capacity to support payloads and supporting electronics; (4) auxiliary propulsion systems capable of 5 to 10 years on orbit operation; (5) multipurpose sensors; and (6) end-to-end data management and an information system configured to accept new components or concepts as they develop.

Source record↗

NASA NTRS1984

Sensitivity of thermal inertia calculations to variations in environmental factors

The sensitivity of thermal inertia (TI) calculations to errors in the measurement or parameterization of a number of environmental factors is considered here. The factors include effects of radiative transfer in the atmosphere, surface albedo and emissivity, variations in surface turbulent heat flux density, cloud cover, vegetative cover, and topography. The error analysis is based upon data from the Heat Capacity Mapping Mission (HCMM) satellite for July 1978 at three separate test sites in the deserts of the western United States. Results show that typical errors in atmospheric radiative transfer, cloud cover, and vegetative cover can individually cause root-mean-square (RMS) errors of about 10 percent (with atmospheric effects sometimes as large as 30-40 percent) in HCMM-derived thermal inertia images of 20,000-200,000 pixels.

Kahle, A. B.↗

NASA NTRS1981

Design data package and operating procedures for MSFC solar simulator test facility

Design and operational data for the solar simulator test facility are reviewed. The primary goal of the facility is to evaluate the performance capacibility and worst case failure modes of collectors, which utilize either air or liquid transport media. The facility simulates environmental parameters such as solar radiation intensity, solar spectrum, collimation, uniformity, and solar attitude. The facility also simulates wind conditions of velocity and direction, solar system conditions imposed on the collector, collector fluid inlet temperature, and geometric factors of collector tilt and azimuth angles. Testing the simulator provides collector efficiency data, collector time constant, incident angle modifier data, and stagnation temperature values.

Source record↗

DOE OSTI2025

Front-of-Meter Model Results

These files contains aggregations of key variables from the NREL Distributed Wind Futures Study using full parcel level data. These variables describe total technical and economic potential for distributed wind turbine deployment. Aggregations are available at the (1) county, (2) zipcode (zip code tabulation area or zcta), and (3) US Census block group level. Each scenario is coded with the scenario name (e.g., baseline) and year (e.g., 2022). Those files postfixed with 'econpot' contain results for only those parcels that are economically viable while the files postfixed with 'techpot' include results for all parcels that are technically feasible. Hence these correspond to technoeconomic and technical potential respectively. The data are available as CSV or Geopackage. Columns in the files are as follows: * geoid: geographic identifier (FIPS code or similar) * min_techpot_sum_kw: technical potential for all parcels in kW using turbines downsized to demand when appropriate * max_techpot_sum_kw: technical potential for all parcels in kW without downsizing turbines * aep_sum_kwh: annual energy production estimate in kWh * cf_mean_ratio: mean capacity factor * lcoe_mean_cents_per_kwh: mean levelized cost of energy for parcels in geography in cents per kWh * lcoe_std_cents_per_kwh: standard deviation of the above * parcel_area_sum_acres: total area of viable parcels in acres * n_turbines: number of cited turbines (one per viable parcel currently) Note: These are preliminary results from the full-parcel 2024 update of the Distributed Wind Energy Futures study. Please take care when making use of the data, and feel free to contact the team with any questions. Full documentation in support of these data is in progress and will follow.

17 WIND ENERGY↗

DOE OSTI2022

Magnetostriction and damping of forced vibrations in Fe-Mo single and polycrystal alloys

Magnetostriction and damping of forced vibrations in Fe-Mo single and polycrystal alloys is studied and analyzed with respect to existing approaches and experimental data. In this work, our data suggest that a more complex dependence (compared with linear dependence predicted by Smith and Birchak) between damping capacity and magnetostriction takes place for this and possibly other Fe-based ferromagnetic alloys. We believe that more factors related to the structural changes, brought about by the additions (elements added to Fe), which can affect phase stability as well as magnetic and electric contributions influencing property response, should be considered to provide a better correlation between the magnetic and magnetomechanical characteristics of this alloy.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Economic dispatch of offshore renewable energy resources for islanded communities with optimal storage sizing

Coastal or isolated microgrids depend on diesel generators and could benefit from renewable energy resources, especially offshore wind and wave energy. Integrating these resources into microgrids is complicated by their high intermittency, which requires optimal economic dispatch to effectively evaluate. This study considers three coastal or islanded sites, and uses mid-fidelity models of wind and wave energy technologies, and local demand data to solve the optimal economic dispatch problem. An optimal storage sizing method is developed that finds the smallest capacity of energy storage required to meet the microgrid load during each season. The storage capacity decreases by a factor of two at most when adding wave energy converters to a system. Adding wave energy converters to a farm decreases cost by about 30%. Furthermore, the required storage size varies by two to three times from summer to winter. Compared with the state-of-the-art approaches that often overlook realistic offshore renewable energy technology in microgrid economic dispatch and optimal storage sizing, the proposed solution introduced in this study allows for better site selection, microgrid design, converter selection, and storage sizing considerations for isolated microgrids.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRS1975

ITOS VHRR on-board data compression study

Data compression methods for ITOS VHRR data were studied for a tape recorder record-and playback application. A playback period of 9 minutes was assumed with a nominal 18 minute record period for a 2-to-1 compression ratio. Both analog and digital methods were considered with the conclusion that digital methods should be used. Two system designs were prepared. One is a PCM system and the other is an entropy-coded predictive-quantization, sometimes called entropy-coded DPCM or just DPCM, system. Both systems use data management principles to transmit only the necessary data. Both systems use a medium capacity standard tape recorder from specifications provided by the technical officer. The 10 to the 9th power bit capacity of the recorder is the basic limitation on the compression ratio. Both systems achieve the minimum desired 2 to 1 compression ratio. A slower playback rate can be used with the DPCM system due to a higher compression factor for better link performance at a given CNR in terms of bandwidth utilization and error rate. The report is divided into two parts. The first part summarizes the theoretical conclusions of the second part and presents the system diagrams. The second part is a detailed analysis based upon an empirically derived random process model arrived at from specifications and measured data provided by the technical officer.

Gray, R. M.↗

DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗

DOE OSTI2026

Characterizing peak electricity demand for U.S. households: an assessment of end-use loads and demand factors

Understanding household peak electricity demand is critical to evaluate the technical need for electrical infrastructure upgrades. This study characterizes peak loads for existing and new equipment using metered data from a convenience sample of 11,940 U.S. dwellings from four sources, including 911 from two sources with end-use metering. After standardized data cleaning and labeling, we derived descriptive statistics for key metrics, such as maximum demand and demand factors, and developed predictive models relating 60- to 15-min demand for the National Electrical Code (NEC). Mean 15-min maximum demand was 9.7 kW (median 9.0 kW; IQR 7.0–11.5 kW, 95% CI 9.6–9.8 kW), indicating spare capacity in 98% of homes with hypothetical 100 A panels. Maximum demand increased with floor area and number of high-demand loads. Dwelling maximum demand was driven by higher-power, longer-duration heating appliances and vehicle charging, while most user-operated appliances contributed little. Demand factors are used to account for how most devices contribute less than their rated power to maximum demand. Existing load mean demand factors (28%; median 10%; IQR 0–58%; CI 28–29%) were higher than those for new loads (21%; median 7%; IQR 0–35%; CI 20–21%), because new loads changed the timing and magnitude of maximum demand. New high-demand loads had higher than average demand factors (40–60%). Whole dwelling demand factors support the NEC's 40% assumption, but they challenge its conservative 100% treatment of new HVAC. We propose a data-driven 50% demand factor for new equipment, which would align with metered data, improve affordability, and modernize electrical codes.

Appliances↗

NASA NTRSDate not supplied

The Effects of Spaceflight and Microgravity Exposure on Female Astronaut Health and Performance

Over the past half century, our understanding of the physiological effects of space exploration and microgravity exposure have improved immensely. Microgravity causes incredible changes to the human body that increase risk of injury upon return to Earth and lunar/planetary egress scenarios. This is due to deconditioning of the cardiovascular and skeletal muscle systems that reduce aerobic capacity and muscular strength. With upcoming exploration class missions, such as NASA’s Artemis lunar exploration program which aims to send the first female astronaut to the lunar surface by 2030, as well as a long-term goal of Martian exploration, maintaining astronaut health during extended-duration space flight is critical for achieving mission objectives. However, our understanding of these physiological implications due to microgravity are based primarily on flight studies of male astronauts and 1g bedrest analog study participants, with few investigations focusing specifically on females. Innate physiologic differences in endocrine signaling and reproductive function impact sex-dependent responses to various health conditions, treatments, and environmental factors in nearly every system in the body. Therefore, to assume comparable alterations in females in response to microgravity exposure may be inappropriate and consequentially, could lead to lasting impacts on female astronaut health and impact mission success. Moreover, differences in sex hormones may also influence the regulation of cardiovascular control during egress activity after space flight-induced deconditioning and blood volume loss (i.e., risk for orthostatic intolerance). Other potential physiological systems and factors related to musculoskeletal health and aerobic capacity that warrant investigation with respect to microgravity include endocrine/reproductive function, vascular control, bone mineral density/microarchitecture, and soft-tissue health. The purpose of this investigation is two-fold: 1) to summarize the data available from space flight and simulated bedrest analog exposures to begin addressing these gaps in knowledge regarding impacts to female astronaut health, and 2) to describe differences in demographic health characteristics, injury prevalence, and aerobic capacity and muscular strength in NASA female and male astronauts. Female astronauts make up 50% of Artemis-specific astronaut corps and the extent to which microgravity exposure impacts female cardiovascular and musculoskeletal health, and whether these alterations are consistent with their male counterparts, is inconclusive. With the growing inclusion of female astronauts in the NASA space program and the increased duration of missions beyond low Earth orbit, a greater understanding of the sex-specific adaptation to space travel will help determine the development of appropriate countermeasures for minimizing risk and maintaining health of all astronauts.

Nicole Christine Strock↗

NASA NTRS2023

The Effects of Space Flight and Microgravity Exposure on Female Astronaut Health and Performance

Over the past half century, our understanding of the physiological effects of space exploration and microgravity exposure have improved immensely. Microgravity causes incredible changes to the human body that increase risk of injury upon return to Earth and lunar/planetary egress scenarios. This is due to deconditioning of the cardiovascular and skeletal muscle systems that reduce aerobic capacity and muscular strength. With upcoming exploration class missions, such as NASA’s Artemis lunar exploration program which aims to send the first female astronaut to the lunar surface by 2030, as well as a long-term goal of Martian exploration, maintaining astronaut health during extended-duration space flight is critical for achieving mission objectives. However, our understanding of these physiological implications due to microgravity are based primarily on flight studies of male astronauts and 1g bed rest analog study participants, with few investigations focusing specifically on females. Innate physiologic differences in endocrine signaling and reproductive function impact sex-dependent responses to various health conditions, treatments, and environmental factors in nearly every system in the body. Therefore, to assume comparable alterations in females in response to microgravity exposure may be inappropriate and consequentially, could lead to lasting impacts on female astronaut health and impact mission success. Moreover, differences in sex hormones may also influence the regulation of cardiovascular control during egress activity after space flight-induced deconditioning and blood volume loss (i.e., risk for orthostatic intolerance). Other potential physiological systems and factors related to musculoskeletal health and aerobic capacity that warrant investigation with respect to microgravity include endocrine/reproductive function, vascular control, bone mineral density/microarchitecture, and soft-tissue health. The purpose of this investigation is two-fold: 1) to summarize the data available from space flight and simulated bed rest analog exposures to begin addressing these gaps in knowledge regarding impacts to female astronaut health, and 2) to describe differences in demographic health characteristics, injury prevalence, and aerobic capacity and muscular strength in NASA female and male astronauts. Female astronauts make up 50% of Artemis-specific astronaut corps and the extent to which microgravity exposure impacts female cardiovascular and musculoskeletal health, and whether these alterations are consistent with their male counterparts, is inconclusive. With the growing inclusion of female astronauts in the NASA space program and the increased duration of missions beyond low Earth orbit, a greater understanding of the sex-specific adaptation to space travel will help determine the development of appropriate countermeasures for minimizing risk and maintaining health of all astronauts.

Female Astronaut↗

NASA NTRSDate not supplied

The Effects of Space Flight and Microgravity Exposure on Female Astronaut Health and Performance

Over the past half century, our understanding of the physiological effects of space exploration and microgravity exposure have improved immensely. Microgravity causes incredible changes to the human body that increase risk of injury upon return to Earth and lunar/planetary egress scenarios. This is due to deconditioning of the cardiovascular and skeletal muscle systems that reduce aerobic capacity and muscular strength. With upcoming exploration class missions, such as NASA’s Artemis lunar exploration program which aims to send the first female astronaut to the lunar surface by 2030, as well as a long-term goal of Martian exploration, maintaining astronaut health during extended-duration space flight is critical for achieving mission objectives. However, our understanding of these physiological implications due to microgravity are based primarily on flight studies of male astronauts and 1g bed rest analog study participants, with few investigations focusing specifically on females. Innate physiologic differences in endocrine signaling and reproductive function impact sex-dependent responses to various health conditions, treatments, and environmental factors in nearly every system in the body. Therefore, to assume comparable alterations in females in response to microgravity exposure may be inappropriate and consequentially, could lead to lasting impacts on female astronaut health and impact mission success. Moreover, differences in sex hormones may also influence the regulation of cardiovascular control during egress activity after space flight-induced deconditioning and blood volume loss (i.e., risk for orthostatic intolerance). Other potential physiological systems and factors related to musculoskeletal health and aerobic capacity that warrant investigation with respect to microgravity include endocrine/reproductive function, vascular control, bone mineral density/microarchitecture, and soft-tissue health. The purpose of this investigation is two-fold: 1) to summarize the data available from space flight and simulated bed rest analog exposures to begin addressing these gaps in knowledge regarding impacts to female astronaut health, and 2) to describe differences in demographic health characteristics, injury prevalence, and aerobic capacity and muscular strength in NASA female and male astronauts. Female astronauts make up 50% of Artemis-specific astronaut corps and the extent to which microgravity exposure impacts female cardiovascular and musculoskeletal health, and whether these alterations are consistent with their male counterparts, is inconclusive. With the growing inclusion of female astronauts in the NASA space program and the increased duration of missions beyond low Earth orbit, a greater understanding of the sex-specific adaptation to space travel will help determine the development of appropriate countermeasures for minimizing risk and maintaining health of all astronauts.

Female Astronaut↗

DOE OSTI2025

Enhanced Performance of Silicon Anodes through Hybrid Surface Engineering

Silicon (Si) is a promising anode material for nextgeneration lithium-ion batteries (LIBs) due to its high theoretical capacity. However, Si-containing anodes typically suffer from unacceptably short lives because of the unrestricted growth of the solid electrolyte interphase (SEI). Here, in this study, hybrid surface coatings are developed to stabilize the SEI in high loading pure Si anodes using atomic and molecular layer deposition. The coatings, consisting of LiF paired with lithicone, create an ionically conductive surface that enhances the capacity retention, rate performance, and longevity. Careful binder selection helps demonstrate the full utility of the coatings by enabling high loading electrodes to cycle continuously at current densities of 1200 mA/g Si . Uncoated controls, in comparison, fail within just 10 cycles at lower loadings. X-ray photoelectron spectroscopy and electrochemical impedance spectroscopy are used to provide supporting evidence of the coating composition and efficacy. The data indicate that our lithicone coating is converted to Li 2 CO 3 upon cycling contributing to favorable LiF/Li 2 CO 3 interfaces that enhance the space-charge effect at the active material’s surface. When applied to electrodes made with thermally stable binders and increasingly higher loadings (approaching 5 mAh/cm 2 ), ion transport through the bulk electrode, rather than SEI growth, is shown to be the limiting factor. Furthermore, data suggests a favorable interaction between lithicone precursors and poly(acrylic acid) binders mitigates thermal decomposition at higher temperatures. The work presented here represents the successful realization of composite coatings containing LiF/Li 2 CO 3 components to stabilize high-loading Si anodes. This work helps inform advanced surface engineering strategies to achieve highly reversible, high-capacity Si anodes capable of fast charging for high-performance LIBs.

atomic layer deposition↗

NASA NTRS1977

Measurement of wavelength-dependent extinction to distinguish between absorbing and nonabsorbing aerosol particulates

Measurements of spectral transmission factors in smoky optical transmission paths reveal a difference between wavelength exponents of the extinction cross section of high absorption capacity and those of low absorption capacity. A theoretical explanation of this behavior is presented. In certain cases, it is possible to obtain data on the absorption index of aerosol particles in the optical path by measuring the spectral decadic extinction coefficient at, at least, two wavelengths. In this manner it is possible, for instance, to distinguish smoke containing soot from water vapor.

Portscht, R.↗