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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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The work describes a superstructure based optimization framework to integrate catalysis, process synthesis, and fuel property modeling to enable the design of tailor made biofuels using ethanol as feedstock.
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Mitigation of failure modes in the continuous synthesis (CS) of a drug substance (DS) has the potential to widen the adoption of continuous manufacturing (CM) technologies by the pharmaceutical industry. Here, this work demonstrates the development of a robust continuous process for the synthesis of carbamazepine (CBZ), an essential medicine as per the World Health Organization (WHO), facilitated by kinetic modelling and monitored by in-line Raman spectroscopy. Accurate kinetic modelling and the use of validated process analytical technology (PAT) models for quantitative measurement were found to play an important role in developing CS of drug substances. Kinetic data for the formation of CBZ from iminostilbene (ISB) were collected by batch reaction sampling and high-performance liquid chromatography (HPLC) analysis. A non-linear solver and iterative method was applied to determine two sets of Arrhenius parameters simultaneously for the reaction system by minimizing the standard error of the model fit. The start-up and dynamic equilibrium stages for the CS of CBZ using a continuous stirred tank reactor (CSTR) were modelled based on the batch kinetic data and employed to optimize conversion and simulate process disturbances. An in-line Raman spectroscopy method was successfully developed, validated, and integrated to determine the concentrations of CBZ and ISB within the operating range for the CS. The CS kinetic model was evaluated experimentally from startup to dynamic equilibrium over 10 residence times with monitoring by HPLC and in-line Raman spectroscopy. The developed kinetic model in tandem with in-line Raman spectroscopy successfully predicted disturbances due to changes in process variables and can serve as a useful tool in the future design of advanced process control strategies for the continuous synthesis of CBZ.
Isotopic engineering has emerged as a key approach to study the nucleation, diffusion, phase transitions, and reactions of materials at an atomic level. It aims to uncover mass transport pathways, kinetics, and operational and failure mechanisms of functional materials and devices. Understanding these phenomena leads to deeper insights into important physical processes, such as the transport of ions in energy conversion and storage devices and the role of active sites and supports during heterogeneous catalytic reactions. Likewise, isotopic engineering is being pursued as a means of modifying functionality to enable future technological applications. In this Account, we summarize our recent work employing isotope labeling (e.g., 18 O 2 and 57 Fe) during thin film synthesis and postgrowth processing to reveal growth mechanisms, defect chemistry, and elemental diffusion under working and extreme conditions. Isotope-resolved analysis techniques with nanometer-scale spatial resolution, such as time-of-flight secondary ion mass spectrometry and atom probe tomography, facilitate the accurate quantification of isotopic placement and concentration in our well-defined heterostructures with precisely positioned, isotope-enriched layers. By measuring the nanometer-scale redistribution between natural abundance and isotopically enriched oxygen layers during the deposition of Fe 2 O 3 and Cr 2 O 3 by molecular beam epitaxy, we identified intermixing processes driven by surface adatoms occurring both at the film growth surface and within the first few layers below the surface. Further insights into synthesis mechanisms were gained by studying the tungsten oxide thin films grown by evaporating WO 3 powder in the presence of background 18 O 2 , revealing minimal incorporation of background oxygen during the film formation process. Thermal and radiation-enhanced diffusion in epitaxial Fe and Cr oxides were precisely tracked using 18 O and 57 Fe tracer layers incorporated into model epitaxial oxide thin films. This approach has allowed us to access thermal diffusion behavior at lower temperatures than previously measured, revealing a potential changeover in diffusion mechanism. Understanding radiation-enhanced diffusion in model oxides that represent the surface layers on the structural components of nuclear reactors informs our understanding of their corrosion behavior under irradiation. Isotopic labeling can also provide unique insights into the surface exchange reactions and defect chemistry of electrocatalysts. For instance, tracking the change in 18 O concentration at the surface of an epitaxial LaNiO 3 thin film after the electrocatalytic oxygen evolution reaction revealed the participation of lattice oxygen, confirming a hypothesis that had been proposed previously. Lastly, we highlight a new direction wherein we perform in situ processing studies utilizing isotopic tracers in conjunction with model epitaxial thin films within the atom probe tomography instrument. Additionally, this Account illustrates the great potential of isotopic engineering to enable fundamental mechanistic insights into physical processes and engineer functional properties in epitaxial films, heterostructures, and superlattices.
This work presents an automated control system for the synthesis of nanomaterials by plasma-enhanced chemical vapor deposition (PECVD), implemented using the LabVIEW software environment. The main objective of the study is to develop an integrated hardware-software platform that enables sequential control of the key stages of the PECVD process, including vacuum chamber preparation, pressure monitoring, working gas supply, plasma ignition, power matching, cyclic nanomaterial growth, and optical monitoring of nanoparticles in the plasma environment. The use of LabVIEW made it possible to integrate actuator control, experimental parameter acquisition, and realtime process visualization within a single automated system. The automated cycle begins with evacuation of the reaction chamber to a predefined base pressure. Transition to the next stage is permitted only after the specified pressure threshold has been reached, ensuring reproducible initial conditions for each experiment. The program then controls the supply of the working gas through mass flow controllers (MFCs). In this work, two gas-flow control modes were considered: analog control using a 0-5 V voltage signal and digital communication via RS-232 interface. It was shown that the analog approach requires accurate scaling of the control voltage, since applying 5 V corresponds to full-scale opening of the controller and results in the maximum gas flow. In contrast, the RS232 interface enables the gas flow rate to be specified directly in sccm, improving the accuracy, flexibility, and convenience of gas-environment control. After pressure stabilization, LabVIEW initiates RF plasma ignition and executes the RF matching algorithm aimed at minimizing reflected power and improving the stability of the plasma process. A separate software module implements the cyclic nanomaterial growth mode, in which the plasma-on time, plasma duration, and total number of synthesis cycles are predefined. This approach makes it possible to control material accumulation on the substrate and to correlate the process parameters with the morphological characteristics of the resulting nanostructures. The final module of the system is designed for optical monitoring of the nanoparticle cloud density in dusty plasma. For this purpose, the change in the intensity of laser radiation passing through the plasma region is recorded using a photodetector and a Keithley 2401 measuring unit connected to LabVIEW via RS-232 interface. The difference between the initial and modified optical signal intensity is used as a diagnostic parameter characterizing the formation and temporal evolution of nanoparticles. The developed system demonstrates that LabVIEW can be effectively applied not only for the automation of individual instruments, but also for the implementation of a complete digital control cycle for PECVD-based nanomaterial synthesis.
The chalcogenide perovskite family has been steadily gaining increasing attention from the research community due to its optoelectronic properties and potential for diverse applications. While BaZrS 3 and BaTiS 3 have been the most extensively studied, other promising compounds in this family, such as Sr x TiS 3 (1.05 < x < 1.22), are now being explored for various optical, optoelectronic, and energy storage applications. However, challenges remain in achieving the low-temperature synthesis of Sr x TiS 3 . In this study, we report, for the first time, the synthesis of Sr x TiS 3 nanocrystals at temperatures below 400 °C. The synthesized nanocrystals exhibit a rod-like morphology. Additionally, we have developed solution-processing routes to synthesize phase-pure Sr x TiS 3 thin films, marking the first reported instance of such films, at temperatures below 600 °C. We also demonstrate the solid-state synthesis of Sr x TiS 3 powder below 600 °C. Our work paves the way for new and exciting application avenues for Sr x TiS 3 .
Gallium oxide (Ga 2 O 3 ) is being actively explored for electronics that can operate at high power, temperature, and frequency as well as for deep-ultraviolet optoelectronics and other applications due to its ultra-wide bandgap (UWBG) and low projected fabrication cost of large-size and high-quality crystals. Efficient n-type doping of monoclinic beta-phase of Ga 2 O 3 has been achieved, but p-type doping faces fundamental obstacles due to compensation, deep acceptor levels, and the polaron transport mechanism of free holes. However, aside from the challenges of achieving p-type conductivity, plenty of opportunity exists to engineer the position of the Fermi level for improved design of Ga 2 O 3 -based devices. We use first-principles defect theory and defect equilibrium calculations to simulate a three-step growth-annealing-quench synthesis protocol for hydrogen-assisted Mg doping in ß-Ga 2 O 3 . The simulations take into account the gas phase equilibrium between H 2 , O 2 , and H 2 O, which determines the H chemical potential. We predict Ga 2 O 3 doping-type conversion to a net p-type regime after growth under reducing conditions in the presence of H2 followed by O-rich annealing, which is a similar process to Mg acceptor activation by H removal in GaN. For equilibrium annealing with re-equilibration of compensating O vacancies, there is an optimal temperature that maximizes the Ga 2 O 3 net acceptor density for a given Mg doping level; the acceptor density is further increased in the non-equilibrium annealing scenario without re-equilibration. After quenching to operating temperature, the Ga 2 O 3 Fermi level drops below mid-gap down to about 1.5 eV above the valence band maximum, creating a significant number of uncompensated neutral Mg Ga 0 acceptors. The resulting free hole concentration in Ga 2 O 3 is very low even at elevated operating temperature (~10 8 cm -3 at 400°C) due to the deep energy level of these Mg acceptors, and hole conductivity is further impeded by the polaron hopping mechanism. However, the Fermi-level reduction and suppression of free electron density in this doping-type converted (NA>ND) Ga 2 O 3 material are important for improved designs of Ga 2 O 3 electronic devices. These results illustrate the power of computational predictions not only for new materials but also for their synthesis science.
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Hybrid yeast strain co-produces isobutanol and ethanol at high yields. Reducing hydrolysis enzyme loading and enhancing xylose conversion greatly impact the economic potential of the biorefinery.
This project aims to develop technology that converts domestic United States (US) raw coal to high quality, high value, and marketable carbon fiber. More specifically, the project aims to significantly improve the selectivity and yield of carbon fiber produced per ton of coal over conventional coal pitch-based production by using low-severity direct coal conversion technology to maximize the yield of pitch from coal, suitable for production of carbon fiber. To meet the stated objective of developing a technology platform, capable of producing high quality, high-value and marketable carbon fiber from domestic US coal, the proposed scope of work involves testing of a low-severity direct coal liquefaction (LS-DCL) process approach. The isotropic pitch produced from the LS-DCL process will be thermally treated using conventional processes to convert to a liquid crystal or “mesophase” pitch as a precursor for structural carbon fibers. This mesophase pitch will then be melt-spun into fiber, oxygen stabilized, and carbonized using conventional processes. The resulting carbon fiber will be evaluated for mechanical properties and suitability for structural applications such as automotive parts and spars for wind turbine blades. The overall process is expected to significantly lower the cost of fiber compared to state of the art fibers produced from polyacrylonitrile (PAN) precursors.
The quantitative characterization of surface structures captured in scanning electron microscopy (SEM) images has proven to be effective for discerning provenance of an unknown nuclear material. Recently, many works have taken advantage of the powerful performance of convolutional neural networks (CNNs) to provide faster and more consistent characterization of surface structures. However, one inherent limitation of CNNs is their degradation in performance when encountering discrepancy between training and test datasets, which limits their use widely. The common discrepancy in an SEM image dataset occurs at low-level image information due to user-bias in selecting acquisition parameters and microscopes from different manufacturers. Therefore, in this study, we present a domain adaptation framework to improve robustness of CNNs against the discrepancy in low-level image information. Furthermore, our proposed approach makes use of only unlabeled test samples to adapt a pretrained model, which is more suitable for nuclear forensics application for which obtaining both training and test datasets simultaneously is a challenge due to data sensitivity. Through extensive experiments, we demonstrate that our proposed approach effectively improves the performance of a model by at least 18% when encountering domain discrepancy, and can be deployed in many CNN architectures.
Recent advances in laser synthesis and processing of 2D materials are described that address key issues of scalable synthesis, precise heterogeneity control, and transformative manufacturing. Here, laser spectroscopy is used to remotely characterize and optimize the structure and functionality of 2D materials, enabling their in-situ diagnostic-based synthesis and processing.
Processes for tailoring the macroscopic shape, metallic composition, mechanical properties, and pore structure of nanoporous metal foams prepared through combustion synthesis via direct write 3D printing of metal energetic ligand precursor inks made with water and an organic thickening agent are disclosed. Such processes enable production of never before obtainable metal structures with hierarchical porosity, tailorable from the millimeter size regime to the nanometer size regime. Structures produced by these processes have numerous applications including, but not limited to, catalysts, heat exchangers, low density structural materials, biomedical implants, hydrogen storage medium, fuel cells, and batteries.
The high demand of lightweight, high energy density batteries for energy storage promotes new materials discovery and development. Despite the large number of battery materials being discovered, very few of them have been commercially deployed, mostly bottlenecked by synthesis and processing – namely, making certain phases with the desired structure and properties to meet the multifaceted performance requirements. Alternative to the traditional trial and error, we present here an in situ study aided synthesis- and processing-by-design approach. With specific examples, we illustrate how to use the approach to identify reaction pathways in synthesis and processing of high-Nickel (Ni) cathode materials for next-generation lithium-ion batteries, thereby ensuring precise control of their structure, morphology, and surface properties. Furthermore, perspectives are provided on the wide applicability of the approach to solving critical issues inherent to high-Ni cathodes and the new directions and opportunities in the area.
Techniques are disclosed concerning applied magnetic field synthesis and processing of iron nitride magnetic materials. Some methods concern casting a material including iron in the presence of an applied magnetic field to form a workpiece including at least one ironbased phase domain including uniaxial magnetic anisotropy, wherein the applied magnetic field has a strength of at least about 0.01 Tesla (T). Also disclosed are workpieces made by such methods, apparatus for making such workpieces and bulk materials made by such methods.
Solid-state synthesis involves a web of coupled chemical reactions and physical changes that unfold across multiple scales. Efforts to fine-tune its parameters have historically followed heuristic, trial-driven workflows that demand significant time and resources. In this study, we aimed to open this black box by employing multiscale in situ synchrotron imaging and diffraction. Using LiNi 0.5 Mn 0.3 Co 0.2 O 2 battery positive electrode material as a model system and Ba-based sintering aids, we reveal dopant segregation, intergranular mass transport, and porosity evolution as key drivers of single-crystalline particle formation. Notably, we uncovered a dynamic competition between particle-level grain coalescence and atomic-scale cation disordering, both of which are thermally activated yet have opposing impacts on battery performance. These findings highlight the coupled, multiscale nature of structure development and offer a mechanistic basis for optimizing the solid-state synthesis process. This framework provides a path toward more controlled, efficient, and scalable production of high-performance battery positive electrode materials.