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

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Battery aging mode identification across NMC compositions and designs using machine learning

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing durable batteries and optimizing use protocols. Although battery lifetime prediction methods are flourishing, diagnosis of the root causes of aging and degradation have not yet been well developed nor studied for a broad mixture of designs and use cases. Here, we create a machine-learning (ML)-based framework that distinguishes aging modes using multiple electrochemical signatures recorded cycle-by-cycle. The predominant aging behaviors include a combination of loss of active materials in cathode (LAMPE) and a loss of Li inventory (LLI) in Li plating or solid electrolyte interphase (SEI) formation, manifested from 44 batteries representing two cathode chemistries, two electrode loadings, and five charging rates. Here, the aging mode classification accuracy is 86% using features within the first 50 cycles and increases to 88% beyond 225 cycles. The same features can quantify the percentage of end-of-life LAMPE with only 4.3% of error.

25 ENERGY STORAGE↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A 24-channel ultra-low-noise preamplifier for dN/dx measurements with drift tube detectors

Cluster counting $(dN/dx)$ is a promising method to enhance particle identification for gaseous detectors, especially in next-generation collider experiments like the FCC-ee, where good $π/K$ separation over a broad momentum range is essential. However, its implementation in large-scale systems has been limited by the challenging requirements for high-resolution signal amplification and readout. This paper presents a 24-channel ultra-low-noise preamplifier board designed for drift tube detectors to enable $dN/dx$ measurements. The three-stage amplification topology employs SiGe transistors and integrates dedicated noise-minimization techniques, achieving a charge gain of 21.11 mV/fC from 0.3 fC to 50 fC, a bandwidth of 542 MHz, and a voltage gain of 47.8 dB. The measured voltage noise density is 0.35 nV/$\sqrt{\textrm{Hz}}$ , surpassing most of the state-of-the-art preamplifiers for gaseous and silicon detectors. Validation tests conducted on the sMDT chambers at the CERN Proton Synchrotron test beam facility demonstrate that the proposed design meets the stringent preamplifier requirements for implementing the $dN/dx$ method in drift-tube detector systems, achieving an equivalent noise charge of 0.14 fC and a signal-to-noise ratio of 73 when operated with a He:iC 4 H 10 (90:10) gas mixture. The design also shows promise for broader application in other gaseous or semiconductor detectors.

Drift tube detector↗
DOE OSTI2023

Heuristics for Molten-Salt Synthesis of Single-Crystalline Ultrahigh-Nickel Layered Oxide Cathodes

In pursuit of Li-ion batteries with higher energy density, ultrahigh-nickel layered oxides are a leading candidate for next-generation cathode materials. Single-crystalline morphology offers a neat solution to the poor stability of ultrahigh-Ni cathodes; a lower active surface area mitigates electrolyte decomposition at high voltages, and the elimination of grain boundaries improves mechanical resilience and increases volumetric energy density. However, single-crystal cathodes possess their own challenges, several of which originate from synthesis at elevated temperatures meant to induce grain growth. Molten-salt synthesis is an alternative method for obtaining single crystals, accelerating grain growth through the presence of a molten flux without the need for increased temperature. Herein, we offer heuristic guidelines for molten-salt synthesis, discussing key factors for designing reaction mixtures and the necessary exploratory research for novel molten salt/cathode systems. Here, the influence of different salts and synthesis conditions on the morphology and properties of single-crystal LiNiO 2 is presented. It is found that oxidative salts, such as Li 2 O 2 and LiNO 3 , are crucial to supplementing dissolution of gaseous oxygen into the molten phase. Through these discussions, this work aims to provide a set of overarching principles for obtaining higher-quality single-crystal layered oxide cathodes and engender more rigorous and impactful investigation into their fundamental nature and applications.

25 ENERGY STORAGE↗
DOE OSTI2025

Polar Bear™ – Innovative Capture of Storage Tank Vapors

Polar Bear™ is a patented technology developed by the Energy & Environmental Research Center (EERC) to capture storage tank vapors and eliminate methane emissions from upstream oil- and gas-producing facilities. Sparked by early commercial investment, the EERC licensed the technology and extended the intellectual property to storage tanks. Polar Bear™ is uniquely engineered and adapted to individual lower-producing facilities where there is otherwise no economic alternative for capturing tank vapors. A high number of small producing oil and gas wells are distributed across the country. The aggregate contributes to a significant volume of emissions. Because of the lack of economy of scale, gas volumes from these facilities are typically not recovered and contribute to methane emissions. Polar Bear™ provides a fit-for-purpose compression solution that addresses cost by reducing complexity with respect to conventional vapor recovery units and eliminating oil changes. Unique to Polar Bear™ is the capability to separate oxygenated gas from storage tank vapors. Storage tanks are designed to “breathe,” allowing gas to enter and escape during internal level and temperature changes. This infiltration of air into the tank headspace imparts undesirable oxygen content with respect to pipeline gathering. Polar Bear™ separates the vapor stream, allowing oxygen-rich gas to be used as fuel on-site while recovering the liquids-rich portion of the gas where oxygen content is minimized. A prototype system was tested to verify process models, evaluate operational performance, and advance the technology readiness level from 5 to 6. Results provide a good match between experimental measurements and process models, indicating the models are useful for future scale-up and field design. Various mixtures of nitrogen and liquefied petroleum gas were tested to understand the mass balance of nitrogen and how it relates to the potential control of oxygen content. Findings indicate that less than 2000 ppm of oxygen is likely to remain in the liquid portion of the gas in field applications. The research and development prepare the technology for field implementation to eliminate routine and fugitive methane emissions from storage tanks.

02 PETROLEUM↗
DOE OSTI2023

Comparative Genomics and Transcriptomics Analyses Reveal Divergent Plant Biomass-Degrading Strategies in Fungi

Plant biomass is one of the most abundant renewable carbon sources, which holds great potential for replacing current fossil-based production of fuels and chemicals. In nature, fungi can efficiently degrade plant polysaccharides by secreting a broad range of carbohydrate-active enzymes (CAZymes), such as cellulases, hemicellulases, and pectinases. Due to the crucial role of plant biomass-degrading (PBD) CAZymes in fungal growth and related biotechnology applications, investigation of their genomic diversity and transcriptional dynamics has attracted increasing attention. In this project, we systematically compared the genome content of PBD CAZymes in six taxonomically distant species, Aspergillus niger, Aspergillus nidulans, Penicillium subrubescens, Trichoderma reesei, Phanerochaete chrysosporium, and Dichomitus squalens, as well as their transcriptome profiles during growth on nine monosaccharides. Considerable genomic variation and remarkable transcriptomic diversity of CAZymes were identified, implying the preferred carbon source of these fungi and their different methods of transcription regulation. In addition, the specific carbon utilization ability inferred from genomics and transcriptomics was compared with fungal growth profiles on corresponding sugars, to improve our understanding of the conversion process. This study enhances our understanding of genomic and transcriptomic diversity of fungal plant polysaccharide-degrading enzymes and provides new insights into designing enzyme mixtures and metabolic engineering of fungi for related industrial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Review on Metallic Alloys Fabrication Using Elemental Powder Blends by Laser Powder Directed Energy Deposition Process

The laser powder directed energy deposition process is a metal additive manufacturing technique, which can fabricate metal parts with high geometric and material flexibility. The unique feature of in-situ powder feeding makes it possible to customize the elemental composition using elemental powder mixture during the fabrication process. Thus, it can be potentially applied to synthesize industrial alloys with low cost, modify alloys with different powder mixtures, and design novel alloys with location-dependent properties using elemental powder blends as feedstocks. This paper provides an overview of using a laser powder directed energy deposition method to fabricate various types of alloys by feeding elemental powder blends. At first, the advantage of laser powder directed energy deposition in manufacturing metal alloys is described in detail. Then, the state-of-the-art research and development in alloys fabricated by laser powder directed energy deposition through a mix of elemental powders in multiple categories is reviewed. Finally, critical technical challenges, mainly in composition control are discussed for future development.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Adhesives for bonding RSI tile to Gr/Pi structure for advanced space transportation systems

An adhesive was developed having improved high-temperature capability while retaining the ideal processing characteristics of RTV silicones. After evaluating several possibilities, mixtures of RTV with glass resins were selected as most promising. While results are not conclusive, tests of the final mixture evaluated, designated RA59, indicated capability of performing as a tile bonding agent to temperatures approaching 370C (700F) during repeated cycling.

Smith, K. E.↗
NASA NTRS2004

Freeze Tolerant Radiator for an Advanced EMU

During an Extravehicular Activity (EVA), the astronaut s metabolic heat and the heat produced by the Portable Life Support Unit (PLSS) must be rejected. This heat load is currently rejected by a sublimator, which vents up to eight pounds of water each EVA. However, for advanced space missions of the future, water venting to space needs to be minimized because resupply impacts from earth will be prohibitive. If this heat load could be radiated to space from the PLSS, which has enough surface area to radiate most of the heat, the amount of water now vented could be greatly reduced. Unfortunately, a radiator rejects heat at a relatively constant rate, but the astronauts generate a variable heat load depending on how hard they are working. Without a way to vary the heat removal rate, the astronaut would experience cold discomfort or even frostbite. A proven method allowing a radiator to be turned-down is to sequentially allow tubes that carry the heat transfer fluid to the radiator to freeze. A drawback of current freezable radiators using this method is that they are far to heavy for use on a PLSS, because they use heavy construction to prevent the tubes from bursting as they freeze and thaw. This creates the need for a large radiator to reject most of the heat but with a lightweight tube that doesn t burst as it freezes and thaws. The new freezable radiator for the Extravehicular Mobility Unit (EMU) has features to accommodate the expansion of the radiator fluid when it freezes, and still have the high tube to fin conductance needed to minimize the number and weight of the tubes. Radiator fluid candidates are water and a propylene glycol-water mixture. This design maintains all materials within their elastic limits so that large volume changes can be achieved without breaking the tube. This concept couples this elastic expansion with an extremely lightweight, extremely high conductivity carbon fiber fin that can carry the heat needed to thaw a frozen tube. By using most of the exposed surface area of the PLSS as a radiator, the system can reject about 75% of the highest heat load, and reduce the loss of water through sublimation by a factor of four. The proposed radiator and a small water tank can be no heavier than the current system.

Copeland, Robert J.↗
NASA NTRS2018

Experimental Effervescence and Freezing Point Depression Measurements of Nitrogen in Liquid Methane-Ethane Mixtures

NASA is designing an unmanned submarine to explore the depths of the hydrocarbon-rich seas on Saturn's moon Titan. Data from Cassini indicates that the Titan north polar environment sustains stable seas of variable concentrations of ethane, methane, and nitrogen, with a surface temperature near 93 K. The submarine must operate autonomously, study atmosphere/sea exchange, interact with the seabed, hover at the surface or any depth within the sea, and be capable of tolerating variable hydrocarbon compositions. Currently, the main thermal design concern is the effect of effervescence on submarine operation, which affects the ballast system, science instruments, and propellers. Twelve effervescence measurements on various liquid methane-ethane compositions with dissolved gaseous nitrogen are thus presented from 1.5 bar to 4.5 bar at temperatures from 92 K to 96 K to simulate the conditions of the seas. After conducting effervescence measurements, two freezing point depression measurements were conducted. The freezing liquid line was depressed more than 15 K below the triple point temperatures of pure ethane (90.4 K) and pure methane (90.7 K). Experimental effervescence measurements will be used to compare directly with effervescence modeling to determine if changes are required in the design of the thermal management system as well as the propellers.

Submarine↗
DOE OSTI2024

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Design and assembly of a catalyst bed gas generator for the catalytic decomposition of high concentration hydrogen peroxide propellants and the catalytic combustion of hydrocarbon/air mixtures

A method for designing and assembling a high performance catalyst bed gas generator for use in decomposing propellants, particularly hydrogen peroxide propellants, for use in target, space, and on-orbit propulsion systems and low-emission terrestrial power and gas generation. The gas generator utilizes a sectioned catalyst bed system, and incorporates a robust, high temperature mixed metal oxide catalyst. The gas generator requires no special preheat apparatus or special sequencing to meet start-up requirements, enabling a fast overall response time. The high performance catalyst bed gas generator system has consistently demonstrated high decomposition efficiency, extremely low decomposition roughness, and long operating life on multiple test articles.

Lohner, Kevin A.↗
NASA NTRS1993

Design of an Advanced Expander Test Bed

The final design of the Advanced Expander Test Bed (AETB) is discussed. The AETB is a cryogenic rocket ground test unit being designed and built for NASA to enable validation of mission-focused technologies for advanced space engines. Based on the split expander cycle, it will operate at a nominal thrust of 20,000 lbf, a chamber pressure of 1200 psia, and may be operated off-design over a wide range of throttling conditions and mixture ratios. The design approach and configuration of the major components are described.

Mitchell, John C.↗
DOE OSTI2022

Hot-melt extrudability of amorphous solid dispersions of flubendazole-copovidone: An exploratory study of the effect of drug loading and the balance of adjuvants on extrudability and dissolution

The FDA-approved anthelmintic flubendazole has shown potential to be repositioned to treat cancer and dry macular degeneration; however, its poor water solubility limits its use. Amorphous solid dispersions may overcome this challenge, but the balance of excipients may impact the preparation method and drug release. Here, we evaluate the influence of adjuvants and drug loading on the development of an amorphous solid dispersion of flubendazole-copovidone by hot-melt extrusion. The drug, copovidone, and adjuvants (magnesium stearate and hydroxypropyl cellulose) mixtures were statistically designed, and the process was performed in a twin-screw extruder. The study showed that flubendazole and copovidone mixtures were highly extrudable, except when drug loading was high (>40%). Furthermore, magnesium stearate positively impacted the extrusion and was more effective than hydroxypropyl cellulose. The extruded materials were evaluated by modulated differential scanning calorimetry and X-ray powder diffraction, obtaining positive amorphization and physical stability results. Pair distribution function analysis indicated the presence of drug rich domains with medium-range order structure and no evidence of polymer-drug interaction. All extrudates presented faster dissolution (HCl, pH 1.2) than pure flubendazole, and both adjuvants had a notable influence on the dissolution rate. In conclusion, hot-melt extrusion may be a viable option to obtain stable flubendazole: copovidone amorphous dispersions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1998

Design of Life Extending Controls Using Nonlinear Parameter Optimization

This report presents the conceptual development of a life extending control system where the objective is to achieve high performance and structural durability of the plant. A life extending controller is designed for a reusable rocket engine via damage mitigation in both the fuel and oxidizer turbines while achieving high performance for transient responses of the combustion chamber pressure and the O2/H2 mixture ratio. This design approach makes use of a combination of linear and nonlinear controller synthesis techniques and also allows adaptation of the life extending controller module to augment a conventional performance controller of a rocket engine. The nonlinear aspect of the design is achieved using nonlinear parameter optimization of a prescribed control structure.

Lorenzo, Carl F.↗
NASA NTRS1998

Nonlinear Control of a Reusable Rocket Engine for Life Extension

This paper presents the conceptual development of a life-extending control system where the objective is to achieve high performance and structural durability of the plant. A life-extending controller is designed for a reusable rocket engine via damage mitigation in both the fuel (H2) and oxidizer (O2) turbines while achieving high performance for transient responses of the combustion chamber pressure and the O2/H2 mixture ratio. The design procedure makes use of a combination of linear and nonlinear controller synthesis techniques and also allows adaptation of the life-extending controller module to augment a conventional performance controller of the rocket engine. The nonlinear aspect of the design is achieved using non-linear parameter optimization of a prescribed control structure. Fatigue damage in fuel and oxidizer turbine blades is primarily caused by stress cycling during start-up, shutdown, and transient operations of a rocket engine. Fatigue damage in the turbine blades is one of the most serious causes for engine failure.

Lorenzo, Carl F.↗
DOE OSTI2025

Using Neural Networks to Identify Mixture Components in Hyperspectral Reflectance Data

Neural networks have been employed to identify materials of interest from hyperspectral data (generally imagery) based on their unique spectral signatures. This approach assumes that there is a single material that is standing out from the rest of the spectrum to be identified. However, pixels often contain more than one material, or a material of interest may itself be a mixture of multiple materials. Neural networks are only as good as the data used to train them, and it takes a great deal of work in the laboratory to identify, make, and measure all potential mixtures of interest. Thus, researchers often calculate synthetic spectra using algorithms with varying degrees of fidelity to the physics that govern the interactions between light and multiple materials. In this work, we have (1) adapted a neural network designed to identify mixture components from Raman spectroscopy to work with visible to near‐infrared reflectance data and (2) tested three common mixture algorithms to determine the most accurate and least computationally expensive method to build synthetic training datasets. With our initial test dataset, we have achieved accuracies of > 90% and found that the synthetic training dataset produced using the Hapke mixture model provides the best results.

99 GENERAL AND MISCELLANEOUS↗