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DOE OSTI2022

Sooting tendencies of terpenes and hydrogenated terpenes as sustainable transportation biofuels

Terpenes are a diverse group of molecules that are synthesized by plants and microorganisms through combining units of isoprene (2-methyl-1,3-butadiene). They typically contain rings and methyl branches, which gives them high energy densities and low freezing points and makes them appealing candidates for sustainable transportation biofuels. Between the original biosynthesis and upgrading options such as hydrogenation, they have a large degree of freedom of structures, e.g., different carbon skeletons, positions of double bonds, and functional groups. Therefore, structure-property data is needed to downselect potential fuel candidates. Here, we measured the sooting tendencies of 17 C10 monoterpenes and 7 of their hydrogenated analogues. The hydrogenated compounds were custom synthesized, so the quantities were too small for conventional smoke point measurements. Thus, the sooting tendencies were quantified with yield sooting index (YSI), which is based on the soot yield in a fuel-doped non-premixed methane flame. Derived smoke points (DSPs) were estimated from a correlation between YSI and smoke point for other hydrocarbons. The YSI of terpenes and their derivatives varies widely from 85.6 to 248.5. The YSI follows the trend: terpenes > dihydroterpenes > tetrahydroterpenes. The DSPs of all the tetrahydroterpenes and some dihydroterpenes are higher than that of a Jet-A fuel sample, suggesting that they offer soot reduction benefits. Further, the YSIs depend strongly on molecular structure; for example, α-pinene and β-pinene have identical carbon skeletons and differ only in the position of one carbon-carbon double bond, but the YSI of α-pinene is 34% higher than that of β-pinene. Detailed decomposition analysis via density functional theory (DFT) suggests that compared with β-pinene, α-pinene requires fewer steps to form the first aromatic ring and the process is more thermodynamically favorable. The YSI difference between the pinenes is mainly affected by the identity of the products from the dominant decomposition pathways.

09 BIOMASS FUELS↗

DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

DOE OSTI2023

Structure and thermodynamics of water adsorption in NU-1500-Cr

Abstract Metal-organic frameworks (MOFs) are a class of materials with diverse chemical and structural properties, and have been shown to effectively adsorb various types of guest molecules. The mechanism of water adsorption in NU-1500-Cr, a high-performance atmospheric water harvesting MOF, is investigated using a combination of molecular dynamics simulations and infrared spectroscopy. Calculations of thermodynamic and dynamical properties of water as a function of relative humidity allow for following the adsorption process from the initial hydration stage to complete filling of the MOF pores. Initial hydration begins at the water molecules that saturate the open Cr 3+ sites of the framework, which is then followed by the formation of water chains that extend along the channels connecting the hexagonal pores of the framework. Water present in these channels gradually coalesces and fills the hexagonal pores sequentially after the channels are completely hydrated. The development of hydrogen-bond networks inside the MOF pores as a function of relative humidity is characterized at the molecular level using experimental and computational infrared spectroscopy. A detailed analysis of the OH-stretch vibrational band indicates that the low-frequency tail stems from strongly polarized hydrogen-bonded water molecules, suggesting the presence of some structural disorder in the experimental samples. Strategies for designing efficient water harvesting MOFs are also proposed based on the mechanism of water adsorption in NU-1500-Cr.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Process-Microstructure-Property Relationships in Low Heat Input Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282

Conference presentation on WAAM process optimization on Ni-based superalloy Haynes® 282®. This alloy is targeted for advanced power generation systems for its superior high temperature mechanical properties. Wire-arc additive manufacturing (WAAM) offers attractive cost and materials savings with high deposition rates. Based on initial build process optimization, WAAM blocks were made, and screened for defects using high-throughput CT scanning. The microstructural evolution in arc energy range of 250-700 J/mm was characterized in the as-built and aged conditions, using electron microscopy. Results showed self-consistent microstructure across conditions, with grain size dependence on arc energy, MC and M6C carbides in as-built, and blocky grain boundary M23C6 in as-heat-treated condition. Further reductions in as-built porosity were explored through 20+ combinations of H2 and CO2 additions to shielding gas. Initial results show improved wetting behavior, and bead overlap with up to 1% H2 and up to 0.25% CO2. Combination of experimentally determined datasets provided comprehensive understanding of process-structure-property relationships in WAAM Haynes 282.

Haynes 282↗

DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Thermodynamic properties of Ca–Pb electrodes determined by electromotive force measurements

Thermodynamic properties of Ca–Pb alloys are investigated by electromotive force (emf) measurements to determine equilibrium cell potentials and phase properties for their application in energy storage systems such as liquid metal batteries. Using the electrochemical cell Ca(s) | CaF 2 (s) | Ca(in Pb) at 700–1060 K, cell emf is measured for thirteen Ca–Pb alloys at mole fractions, x Ca = 0.06–0.80. At 873 K, the equilibrium potentials of liquid Ca–Pb alloys are 0.57–0.62 V versus Ca and the activity values are as low as a Ca = 6.2 × 10 -8 at x Ca = 0.06. In addition, the emf values as a function of temperature provide partial molar quantities (entropy and enthalpy) as well as phase transitions which are corroborated by determining transition temperatures and phase constituents using differential scanning calorimetry (DSC) and powder X-ray diffraction (XRD). This study establishes the fundamental data necessary for the design of Pb-containing liquid metal electrodes through the integration of electrochemical, thermal, and structural properties of Ca–Pb electrodes.

25 ENERGY STORAGE↗

DOE OSTI2022

Chemical Kinetics Underlying the Sooting Tendency and Auto-Ignition Characteristics of Linear, Branched, and Cyclic Ether Compounds

Biofuels present opportunities for improving the performance and reducing emissions from internal combustion engines by incorporating oxygenated functional groups to the fuels. Among various oxygenates, ethers have been recognized as promising candidates for an alternative to conventional diesel fuel owing to their higher reactivity and lower sooting tendency. The detailed guidelines for designing ethers, however, have not been fully discussed, even though their combustion characteristics are sensitive to the molecular structure. This study was devoted to exploring the structure-property relationships, particularly focusing on the cetane number and yield sooting index, using five linear, branched, and cyclic ethers: di-amyl-ether, 4-butoxy-heptane, 3,3-dimethyl-oxetane, 2-ethyl-4-methyl-1,3-dioxolane, and 2-isopropyl-4-methyl-1,3-dioxolane. First, we examined the chemical kinetics underlying the sooting tendency of the test fuels. The combustion product distribution was measured from flow reactor experiments at 750-1100 K, F=3, at atmospheric pressure. As a result, it was revealed that the sooting tendency is closely related to the size of hydrocarbon intermediates in the high-temperature regime (>1000 K); that is, larger hydrocarbons lead to more soot precursor formation. The underlying chemistry determining the size of the hydrocarbons from the tested fuels was analyzed using reaction pathway analysis and quantum mechanics calculations, which showed that the branched and cyclic ether structures form abundant C3-C4 compounds. Moreover, the auto-ignition characteristics of the test fuels were studied using the flow reactor at low-temperature (400-700 K) and F=1. We found a clear difference in the combustion-product distribution from high and low reactivity fuels, which was then correlated to the systematic analysis of the key reaction energy barriers with the varying molecular structure.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗

DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗

DOE OSTI2026

Single-particle detection of enhanced polarizability in Au-decorated semiconducting nanorods via scanning dielectric microscopy

Hybrid nanostructures that combine semiconducting and metallic components offer great potential for photothermal therapy, optoelectronics, and sensing, by integrating tunable optical properties with enhanced light absorption and charge transport. Boosting the integrated performance of these hybrid systems demands techniques capable of probing local variations of the physical properties inaccessible to bulk analysis. Here, we report the single-particle dielectric characterization of hybrid, semiconducting bismuth sulfide (Bi 2 S 3 ) nanorods (NR) decorated with metallic Au nanoparticles (NP), employing scanning dielectric microscopy, which uses electrostatic force microscopy in combination with finite-element numerical simulations. We reveal a pronounced enhancement in the local dielectric response of Bi2S3 upon Au decoration, attributed to interfacial polarization and electron transfer from Au to the Bi 2 S 3 matrix, thus suggesting a enhanced metallic-like polarizability at the single-particle level. Numerical simulations show that the response is dominated by the vertical component of the permittivity and that the decorating metallic Au NP produce only moderate shielding of the semiconductor Bi 2 S 3 NR core, indicating that the large increase in the dielectric response originates primarily from intrinsic modifications within the NR. Overall, these findings provide direct insight into structure–property relationships at the single-particle level, supporting the rational design of advanced hybrid nanostructures with tailored electronic functionalities.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Optimizing dicyandiamide pretreatment conditions for enhanced structure and electronic properties of polymeric graphitic carbon nitride

Graphitic carbon nitride, a polymeric semiconductor, possesses a distinctive electronic band structure and exceptional chemical stability, making it a highly promising material for various catalytic applications such as electrocatalysis, photocatalysis, and photo-electrocatalysis. However, its practical applications remain limited due to its low active site density and poor electrical conductivity. In this study, to overcome such limitations, we have conducted a thorough investigation to explore the impact of dicyandiamide (DCDA) precursor pretreatment prior to thermal polymerization to graphitic carbon nitride. The DCDA precursor was subjected to various pretreatment methods including grinding using mortar and pestle, recrystallization through stirring or probe sonication after dissolving in deionized water, and freeze drying, prior to thermal polymerization at 550 °C for 2 hours. The structural and morphological properties of the catalysts prepared were compared and characterized by X-ray diffraction (XRD), attenuated total reflectance–Fourier transform infrared (ATR-IR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) analysis while the electronic band gap properties were determined based on density functional theory (DFT) calculations for a set of crystalline systems having C/N ratios similar to those identified experimentally. In comparison to direct thermal polymerization, pretreated samples rendered the same product yield, diverse morphologies with flat or wrinkle structures, and reduced electrochemical resistance, making them suitable for use in various catalytic processes.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, in this work, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Synthesis of (hemi)cellulosic lubricant base oils via catalytic coupling and deoxygenation pathways

The past decade has witnessed a significant growth in bioproduct development due to the strong interest in creating a more diverse energy supply, environmental sustainability, and a carbon neutral bioeconomy. The initial biorefinery strategy, which focused mainly on the production of low carbon number drop-in bioproducts, has recently shifted its focus on the production of high carbon number and high performance bioproducts with unique properties and value propositions. One such product is lubricant base oil (LBO), which represents over $150 billion global lubricant market and is used for various industrial, automotive, marine, metal-working, consumer, and specialized applications. The application landscape of lubricants has changed significantly over the past decade because of environmental regulations and the growing demand for sustainable lubricants to mitigate regulatory challenges and improve carbon footprint. Currently, only 3% LBOs are produced from bio-based triglycerides/fatty acids. Because of low oxidation stability and high hydrolytic susceptibility, these bio-based LBOs have limited applications. Thus, research and development and commercialization efforts for (hemi)cellulosic LBOs to meet the market demand have received momentum over the past few years. This review describes strategies to produce (hemi)cellulosic LBOs via carbon–carbon coupling and hydrodeoxygenation chemistries. We emphasize the coupling strategies to create LBOs with a branched architecture to surrogate petroleum-based poly-α-olefins. Here, a structure–property relationship for tuning the key specifications of LBOs as a function of molecular architecture is illustrated. Mechanistic understanding and molecular interactions of multi-furan substrates with the active sites of complex multifunctional catalysts are described. Furthermore, techno-economic and life-cycle analysis is summarized along with standing challenges and future opportunities.

09 BIOMASS FUELS↗

DOE OSTI2022

Interfacial Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies

Atomically precise cerium oxo clusters offer a platform to investigate structure–property relationships that are much more complex in the ill-defined bulk material cerium dioxide. We investigated the activity of the MCe 70 torus family (M = Cd, Ce, Co, Cu, Fe, Ni, and Zn), a family of discrete oxysulfate-based Ce 70 rings linked by monomeric cation units, for CO oxidation. CuCe 70 emerged as the best performing MCe 70 catalyst among those tested, prompting our exploration of the role of the interfacial unit on catalytic activity. Temperature-programmed reduction (TPR) studies of the catalysts indicated a lower temperature reduction in CuCe 70 as compared to CeCe 70 . In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) indicated that CuCe 70 exhibited a faster formation of Ce 3+ and contained CO bridging sites absent in CeCe 70 . Isothermal CO adsorption measurements demonstrated a greater uptake of CO by CuCe 70 as compared to CeCe 70 . The calculated energies for the formation of a single oxygen defect in the structure significantly decreased with the presence of Cu at the linkage site as opposed to Ce. Furthermore, this study revealed that atomic-level changes in the interfacial unit can change the reducibility, CO binding/uptake, and oxygen vacancy defect formation energetics in the MCe 70 family to thus tune their catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Engineering mechanical and thermomechanical performance in additive manufacturing–Compression molded composites through multiplexed extrusion

Traditional extrusion-based additive manufacturing is limited to single material systems, restricting the multifunctional properties of composites. For this work to overcome this limitation, multiplexed additive manufacturing–compression molding (AM-CM) was employed to fabricate multi-material thermoplastic composites with spatially tailored architectures. Neat acrylonitrile butadiene styrene (ABS) and 20 wt% carbon-fiber reinforced ABS (CF-ABS) were co-extruded through a core–sheath nozzle to produce hybrid composites with neat ABS as sheath (30-50 wt%) and CF-ABS as core (50 – 70 wt%). The results show that the hybrid composites have balance of mechanical and thermomechanical performance. The tensile strength and modulus of hybrid composites exhibited a 61–95% and 173–473% increase compared to neat ABS with increases in CF-ABS content whereas the impact resistance improved by 41% compared to CF-ABS at 50 wt% ABS. Additionally, hybrid composites showed significant reduction (54 - 70%) in creep strain at 100 °C compared to neat ABS. These findings demonstrate that multiplexed AM-CM enables tunable structure–property relationships, reducing CF-ABS usage up to 50 wt% while maintaining balanced stiffness, toughness, and creep resistance.

Additive manufacturing↗

DOE OSTI2024

Ensemble Manufacturing Techniques for Steam Turbine Components Across Length Scales

Faster design to manufacturing requirements were sought for steam turbine components to meet the changing demands of today’s power grid of improved efficiency through operating temperature increases and enhanced operational flexibility from baseload to cyclic operations. Advances in multiple advanced manufacturing (AM) process enabled redesign of turbine components for extreme environments with the potential to reduce cost. AM is of particular interest to improve component functionality, higher temperature capability, and superior durability in large scale steam turbine applications. AM methods have an immense potential to open-up the design space by working directly with the 3D model to produce near-net shapes, enable fast design-manufacturing iterations, thereby significantly reducing product cost and lead-time up to 25 % from current baseline. However, these benefits cannot be fully realized due to the potential for unknown AM processing defects and their resultant effect upon component performance in service. Siemens is partnering with Oak Ridge National Laboratory (ORNL), Electric Power Research Institute (EPRI), and Connecticut Center for Advanced Technology (CCAT) to advance the knowledge of complex process-material interactions for desired microstructures and properties that are closely interlinked to component geometries across different length scales. The proposed program utilized an ensemble of multidisciplinary technologies to accelerate the development of materials, high-throughput experiments for their qualification, and design flexibility/topology optimization for repair/redesign of components to address critical failure mechanisms for improved performance and increased reliability of existing power plant components. The proposed activities, if successfully demonstrated for identified components, will enable paradigm shift in customized manufacturing and accelerated qualification/certification towards increased steam turbine component durability, increased turbine efficiency, and reduced CO 2 emissions in load-following environments compared to today’s technology. Technology maturation is built into the project as successful research will include customized process-component down-selection enabling AM methodologies to be incorporated directly into the existing supply chain. The specific activities of the proposed effort are: 1. Topology optimization of down-selected steam turbine parts that are amenable to additive and hybrid manufacturing for cost/performance improvement. 2. Process-structure-property relationships for five AM processes for steam turbine materials of interest to compare with conventional materials. 3. Perform part/assembly build process using advanced additive/hybrid machine tools followed by quality inspection of the built components for insight into qualification for production scale-up. Steam turbine rig testing of printed components under targeted, well monitored and characterized environmental conditions of for performance comparison of baseline and redesigned components.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Quantification of Oxygen Vacancies in SrFe0.5Cr0.5O3−δ Thin Films: Correlating Lattice Expansion with Oxidation State Variability

Oxygen vacancies (VO), even in the dilute regime, can significantly impact the physical and chemical properties of complex oxides. It is however challenging to reliably detect and adequately quantify such defects in thin film samples where the VO concentration and distribution may further vary in response to interfacial strain. Here, we present a method to quantify VO concentrations in SrFe0.5Cr0.5O3−δ (SFCO) epitaxial thin films. Through coherent Bragg rod analysis, X-ray absorption spectroscopy, and theoretical modeling, we systematically correlate the c-axis lattice expansion detected in SFCO films with a VO concentration gradient. Our results reveal a nearly linear relationship between oxygen off-stoichiometry and out-of-plane lattice expansion in coherently strained SFCO films, with δ increasing from 0.32 to 0.52 and the lattice expanding by ∼1.0%. Moreover, a significant decrease in Cr oxidation state (from Cr5+ to Cr3+) is observed as the VO concentration increases, while the Fe oxidation state remains fixed at Fe3+. Our findings provide a reliable way to quantify oxygen stoichiometry in complex oxides, offering a pathway to design materials with precisely tailored structure–property relationships.

Hossain, Mohammad Delower↗

DOE OSTI2020

Materials for the biorefinery: high bio-content, shape memory Kraft lignin-derived non-isocyanate polyurethane foams using a non-toxic protocol

Polyurethanes are among the top six polymers produced in the world and are widely used in the automotive, furniture, construction and appliance industry for their light weight, impact resistance, and insulating properties. However, the use of hazardous diisocyanates used in polyurethane formulations has led many to search for more sustainable alternatives. The lignin component of biomass has been targeted to replace the often toxic and petroleum-derived precursors to polymer synthesis in support of the biorefinery concept where natural materials are used as the feedstock for commodity plastics. The use of lignin is often hampered due to its low reactivity, heterogeneity and the necessity of employing extensive purification and/or functionalization measures to ensure materials are of comparable quality. In this report, a unique method is presented for the synthesis and processing of a non-toxic, non-isocyanate polyurethane foam utilizing unmodified Kraft lignin and a biobased curing agent. Here, raw Kraft lignin is functionalized with green organic carbonates and a non-toxic approach is taken to solubilize the precursors with a curing agent from renewable fatty acids enabling rapid gel-times. For the first time, a NIPU foam from lignin is synthesized while the structure–property relationship of different reaction mixtures is studied demonstrating shape memory capacity and 100% biobased carbon content.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Precisely segmented PEEK–ionene + ionic liquid composite membranes for CO 2 separation

Ionenes, polymers with ionic groups incorporated directly within the backbone are a highly versatile class of materials, although they have received much less attention than polyelectrolytes which have ionic groups pendant from the polymer backbone. By designing ionenes that incorporate robust properties of poly(ether ether ketone) (PEEK), we have achieved new imidazolium-containing PEEK–ionene architectures that create opportunities for enhanced CO 2 separation membranes. To achieve these materials, an new imidazole-functionalized PEEK oligomer (ImK(EEK) 2 KIm) was synthesized through facile and straightforward routes. This molecule was then polymerized via condensation reactions with two different aromatic (α,α'-dibromo-p-xylene) and aliphatic (dibromohexyl containing-bis(imidazolium)hexane dibromide salt) comonomers and exchanged with bistriflimide ([Tf 2 N]) anion to obtain two unique PEEK–ionenes containing distinct PEEK and ionic segments: p([ImK(EEK) 2 Im-p-xyl][Tf 2 N] 2 ) and p([ImK(EEK) 2 (ImC 6 ) 3 ][Tf 2 N] 4 ). While the neat PEEK–ionenes exhibited high molecular weight but were not able to form high-quality films, adding stoichiometric amounts of “free” IL (1-methyl-3-butylimidazolium bistriflimide, [C 4 mIm][Tf 2 N]), greatly improved the flexibility and processability of the resultant membranes. Further, the structure–property relationships of bulk PEEK–ionenes and corresponding composites were extensively characterized by different analytical techniques (thermogravimetric analysis, differential scanning calorimetry, X-ray diffraction, and solid-state NMR). In conclusion, the gas separation properties were investigated, with the PEEK–ionenes + IL composites exhibiting CO 2 permeabilities of 14–94 barrer and good CO 2 /N 2 permselectivities of 26–35, indicating that new designs of segmented ionenes and composites with ILs a promising material design strategy for developing gas separation membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗