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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 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↗
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↗
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↗
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↗
DOE OSTI2025

Inverse mapping of properties to composition through generative modeling for designing molten salts

Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Generative Electrolyte Solvent and Formulation Discovery

Molecular mixtures and/or formulations are of great importance in fields ranging from materials science to pharmaceuticals to chemistry. In batteries, electrolytes are complex molecular mixtures consisting of multiple salts and solvents and additives at different concentrations that dictate battery capacity, safety, and cycle life, among others. Unfortunately, due to the complex composition and infinite design space as well as the conflicting property requirements, electrolyte design is the rate-determining step in the design of next generation battery chemistries. In this work, we develop a transformer-based generative AI model − ElectrolyteGPT − capable of generating solvents and electrolyte formulations to satisfy a wide range of desired property requirements. First, we curate an electrolyte-relevant database and develop a new line notation for formulations. Then, we show that ElectrolyteGPT can generate solvents and formulations conditioned on a wide range of important electrolyte properties such as ionic conductivity, oxidative stability, Coulombic efficiency, viscosity, and more. Finally, we experimentally synthesize the generated solvents and fabricate the electrolyte formulations and show that they can meet the desired property requirements and enable longterm cycling in energy-dense anode-free lithium metal batteries. Our work showcases the ability of generative models to address challenges in molecular mixture design for next generation batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Dual-step photocarrier injection by mixture layer of ZnO QDs and MoS 2 NPs on hybrid PdAu NPs

Hybrid UV-photodetector (PD) design consisted of various compatible nano-material systems are drawing significant research interests due to their improved device tunability and functionality. A hybrid ZnO–MoS 2 /HNP UV-PD design is demonstrated by the mixture layer of zinc oxide (ZnO) quantum dot (QD) and molybdenum di-sulfate (MoS 2 ) nanoparticles (NPs) on the plasmonic hybrid PdAu NP template. The proposed design exhibits an excellent photocurrent of 1.45×10 -3 A under the 54.9 mW/mm2 at ± 10 V due to a dual step enhancement of photo-carrier injection. It then leads to the significantly improved performance factors of photo-responsivity 1430 mA/mm 2 and detectivity 4.50 × 10 10 jones at 0.34 mW/mm 2 . The rise time (T r ) and fall time (T f ) of 3.37 and 0.35 s are obtained for the ZnO–MoS 2 /HNP PD. Finally, the demonstrated performance factors indicate that the hybrid ZnO–MoS 2 /HNP UV-PD platform is one of the best UV-PDs as summarized in Table 1.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Electrochemical Performance of Mixed Redox-Active Organic Molecules in Redox Flow Batteries

Designing electrolytes based on mixture of different organic redox active molecules brings the opportunity of enhancing the volumetric energy density of flow batteries and removes the requirement of high solubility for individual organic species in the mixture. Here, in the present work, we conduct computational and experimental analysis to investigate the electrochemical performance of mixed redox-active organic molecules. A zero-dimensional transient model is employed to investigate the changes in the half-cell potential and the concentrations and partial currents of individual redox reactions in a mixture of organic molecules over time. The model demonstrates the effects of individual properties of species such as kinetic rate constants, mass transfer coefficients, concentration ratios and standard redox potentials and reports the effect of energy-losing homogenous chemical redox reaction on the voltage efficiency and concentration ratios of the mixed species. Pairs of anthraquinone negolyte species were selected for an experimental case study. A mixture of 2,6-N-TSAQ and 2,6-DHAQ showed 40% increase in the volumetric energy density compared to the performance of 2,6-DHAQ alone. Based on the results of the experimental and computational analysis, we propose guidelines for the design of suitable mixed redox-active organic species.

25 ENERGY STORAGE↗
DOE OSTI2026

Surface and Buried Thermal, and RGB Unexploded Ordnance Data Collection

This document provides a description of a data collection campaign of unexploded ordnance (UXOI) set. The dataset captures a controlled UAV imaging campaign designed to support detection of UXO across varied environmental conditions. Data were collected during three campaigns in Norris and Northeast Knoxville, Tennessee, using RGB, and thermal sensors mounted on Parrot UKR. In total, the dataset contains 9925 images, 26 full-motion video, and approximately 81.99 GB of data, collected across late spring/summer conditions, every hour during sunlight, and multiple surface contexts, including tall grass, short grass, gravel, as well as buried in sand, and other gravel mixtures. The collection was designed to capture thermal and visual variability relevant to UXO detection in agricultural land, bare earth, and subsurface. Review of the imagery showed that ordnance was most detectable during periods of changing solar input, especially approximately 10-60 minutes after sunrise, approximately 20-60 minutes after sunset, and 2-3 min after cloud cover interrupted prolonged solar heating. These conditions increased thermal contrast because many ordnance items retained or released heat differently than the surrounding vegetation and ground surface. This dataset provides a useful resource for developing and evaluating airborne UXO detection methods under realistic field conditions. All ordnance used in the study was inert, and thermal behavior may differ from that of live ordnance. In addition, variation in ordnance type, composition, and placement introduced differences in thermal response that should be considered when interpreting results.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Excess Density as a Descriptor for Electrolyte Solvent Design

Electrolytes mediate interactions between the cathode and anode and determine performance characteristics of batteries. Mixtures of multiple solvents are often used in electrolytes to achieve desired properties, such as viscosity, dielectric constant, boiling point, and melting point. Conventionally, multi-component electrolyte properties are approximated with linear mixing, but in practice, significant deviations are observed. Excess quantities can provide insights into the molecular behavior of the mixture and could form the basis for designing high-performance electrolytes. Here we investigate the excess density of commonly used Li-ion battery solvents such as cyclic carbonates, linear carbonates, ethers, and nitriles with molecular dynamics simulations. We additionally investigate electrolytes consisting of these solvents and a salt. The results smoothly vary with mole percent and are fit to permutation-invariant Redlich-Kister polynomials. Mixtures of similar solvents, such as cyclic-cyclic carbonate mixtures, tend to have excess properties that are lower in magnitude compared to mixtures of dissimilar substances, such as carbonate-nitrile mixtures. We perform experimental testing using our automated test stand, Clio, to provide validation to the observed simulation trends. We quantify the structure similarity using SOAP fingerprints to create a descriptor for excess density, enabling the design of electrolyte properties. To a first approximation, this will allow us to estimate the deviation of a mixture from ideal behavior based solely upon the structural dissimilarity of the components.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Lanthanide Separations through Helicate Self-Assembly

Skyrocketing demand for rare-earth elements makes their isolation and reuse from electronic waste an increasingly attractive alternative to mining. Typically, the capture of individual lanthanides from mixtures requires the iterative design of complex, macrocyclic ligands, a sometimes successful but often laborious process. Here we show that the self-assembly of rare-earth ions with a ditopic ligand, L d , results in the selective incorporation of smaller lanthanides and gives rise to impressive separation factors. Comparison to a monotopic ligand, L m , shows that the separation performance of ditopic L d critically depends on the presence of two adjacent binding sites. Isothermal titration calorimetry (ITC) experiments show that the selectivity of L d for smaller ions is thermodynamically driven and provide evidence of positive cooperativity among the two binding sites of L d . The simplicity of the separation procedure reported here, requiring only 1 min of sonication in methanol, shows the potential applicability of this approach to real-world separations. Here, by achieving efficient rare-earth separations in small, synthetically facile helicates, this work shows the promise of self-assembly as a mechanism to drive metal separations.

Alcohols↗
DOE OSTI2024

The Case for Reducing Emissions from Heavy-Duty Off-Road Applications via Ducted Fuel Injection

Designers of heavy-duty diesel engines use a variety of techniques to improve efficiency and reduce pollutant emissions. Ducted fuel injection (DFI), high fuel injection pressures, optimized injection orifices, multiple injection sites, and oxygenated fuels are a few techniques used by designers to improve fuel/charge-gas mixtures within the combustion chamber to improve efficiency and emissions results. Ducted fuel injection (DFI) has been proven to substantially reduce soot for low- and mid-load conditions in heavy duty engines, without significantly increasing nitrogen oxides (NO x ). This study investigates the performance of DFI utilizing conventional diesel fuel and the potential the technique has for future research utilizing varieties of test fuels and operating parameter values. Optimization for high engine efficiency and low emissions will help facilitate DFI deployment for substantial environmental benefits in heavy-duty sectors where electrification and/or carbon-free fuels aren’t feasible.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l - and d -amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC 50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.

42 ENGINEERING↗
DOE OSTI2024

Insights into Preceramic Polymer-Based Additive Manufacturing Inks via Rheological and Scattering Studies of Preceramic Polymer-Grafted Nanoparticles Suspended in Polycarbosilane

Preceramic polymers (PCPs) offer advantages in producing ceramics due to their processability and ability to tailor the final chemistry of the produced material. However, challenges such as volumetric shrinkage and mass loss during pyrolysis often result in polymer-derived ceramics containing pores and cracks. PCP-grafted ceramic nanoparticles (PCPGNPs) have been proposed and studied as a route to mitigate the shrinkage issues associated with neat PCPs. Prior studies on PCPGNPs have principally focused on the synthesis and characterization of neat materials. Dispersing PCPGNPs in commercial preceramic polymer is another attractive, but underexplored, route to control the rheological and char yield properties of PCP systems. In this work, a systematic rheological study of commercial PCP (SMP-877) and PCPGNP (silica with poly(1,1-dimethylpropylsilane) corona) mixtures was executed to develop design rules for the processing of such systems. A rheological study demonstrated the effect of increasing particle concentration on network formation with percolation occurring between 50 and 60 wt %. Samples above the percolation threshold exhibited higher viscosities and rapid shear thinning thus demonstrating their direct-write printability. X-ray photon correlation spectroscopy (XPCS) corroborated the rheology and showed two diffusive modes when the material was above percolation. Mixtures of PCPGNPs and SMP-877 had synergistically higher char yields upon thermal treatment and pyrolysis. XPCS and rheological measurements during thermal treatment identified thermal jamming of the polymer grafts as a key factor in improving the char yield. In conclusion, with the insights gained here, we expect these mixed systems to provide attractive feedstocks for polymer-derived ceramics, with proof-of-principal application as feedstocks for direct ink write (DIW) additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structurally Defined Water‐Soluble Metallofullerene Derivatives towards Biomedical Applications

Abstract Endohedral metallofullerenes (EMFs) are excellent carriers of rare‐earth element (REE) ions in biomedical applications because they preclude the release of toxic metal ions. However, existing approaches to synthesize water‐soluble EMF derivatives yield mixtures that inhibit precise drug design. Here we report the synthesis of metallobuckytrio (MBT), a three‐buckyball system, as a modular platform to develop structurally defined water‐soluble EMF derivatives with ligands by choice. Demonstrated with PEG ligands, the resulting water‐soluble MBTs show superb biocompatibility. The Gd MBTs exhibit superior T 1 relaxivity than typical Gd complexes, potentially superseding current clinical MRI contrast agents in both safety and efficiency. The Lu MBTs generated reactive oxygen species upon light irradiation, showing promise as photosensitizers. With their modular nature to incorporate other ligands, we anticipate the MBT platform to open new paths towards bio‐specific REE drugs.

Li, Yanbang↗