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At least 1,117 records · Page 62

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Multimorphic Materials: Spatially Tailoring Mechanical Properties via Selective Initiation of Interpenetrating Polymer Networks

Access to multimaterial polymers with spatially localized properties and robust interfaces is anticipated to enable new capabilities in soft robotics, such as smooth actuation for advanced medical and manufacturing technologies. Here, orthogonal initiation is used to create interpenetrating polymer networks (IPNs) with spatial control over morphology and mechanical properties. Base catalyzes the formation of a stiff and strong polyurethane, while blue LEDs initiate the formation of a soft and elastic polyacrylate. IPN morphology is controlled by when the LED is turned “on”, with large phase separation occurring for short time delays (≈1–2 min) and a mixed morphology for longer time delays (>5 min), which is supported by dynamic mechanical analysis, small angle X-ray scattering, and atomic force microscopy. Through tailoring morphology, tensile moduli and fracture toughness can be tuned across ≈1–2 orders of magnitude. Furthermore, a simple spring model is used to explain the observed mechanical behavior. Photopatterning produces “multimorphic” materials, where morphology is spatially localized with fine precision (<100 µm), while maintaining a uniform chemical composition throughout to mitigate interfacial failure. As a final demonstration, the fabrication of hinges represents a possible use case for multimorphic materials in soft robotics.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data

Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Topology optimization for the design of porous electrodes

Porous electrodes are an integral part of many electrochemical devices since they have high porosity to maximize electrochemical transport and high surface area to maximize activity. Traditional porous electrode materials are typically homogeneous, stochastic collections of small-scale particles and offer few opportunities to engineer higher performance. Fortunately, recent breakthroughs in advanced and additive manufacturing are yielding new methods to structure and pattern porous electrodes across length scales. These architected electrodes are emerging as a promising new technology to continue to drive improvement; however, it is still unclear which structures to employ and few tools are available to guide their design. Here we address this gap by applying topology optimization to the design of porous electrodes. We demonstrate our framework on two applications: a porous electrode driving a steady Faradaic reaction and a transiently operated electrode in a supercapacitor. We present computationally designed electrodes that minimize energy losses in a half-cell. For low-conductivity materials, the optimization algorithm creates electrode designs with a hierarchy of length scales. Further, the designed electrodes are found to outperform undesigned, homogeneous electrodes. Finally, we present three-dimensional porous electrode designs. We thus establish a topology optimization framework for designing porous electrodes.

25 ENERGY STORAGE↗

DOE OSTI2021

Effects of Hydrogen Redistribution at High Temperatures in Yttrium Hydride Moderator Material

We report advanced materials development, manufacturing, and modeling capabilities for innovative reactor designs support nuclear security and mission-focused science through enhanced technology for safer and more efficient and secure production of nuclear energy. The high temperature moderator material yttrium hydride poses a significant enhancement in small reactor design by thermalizing (slowing down) neutrons and decreasing the required fuel mass for a system. The research presented here supports understanding hydrogen distribution in yttrium hydride through: (1) the development of neutron-based hydrogen imaging and crystallographic characterization that allows us to understand fundamental diffraction behaviors and to observe changes in hydrogen distribution as a function of temperature and (2) subsequent neutron multiplication (reactivity) effects of changes in hydrogen distribution using measurement-based cross sections in a sample microreactor design. The main conclusions from the work are that: (1) hydrogen does not redistribute significantly below temperatures of 800°C in yttrium hydride and (2) hydrogen redistribution affects the reactivity slightly but not significantly.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Electrification potential of U.S. industrial boilers and assessment of the GHG emissions impact

Electrification is a key strategy for decarbonizing the industrial sector. Industrial process heating, which still relies heavily on fossil fuel combustion and accounts for the majority of sector wide GHG emissions, is a particularly attractive electrification target. Electrifying industrial boilers represents a cross-cutting opportunity for GHG emissions reductions, given their widespread use in most manufacturing industries. Yet, there are gaps in the understanding of the current population of conventional industrial boilers in the United States that preclude a characterization of boiler electrification's technical potential to reduce fuel consumption and GHG emissions. In this study, we develop an up-to-date dataset of the industrial boiler population in the U.S. and quantify the county-level electricity requirements and net changes in fuel use and GHG emissions under the current electric grid and theoretical future grid scenarios. Our results show an increase of 105 MMmtCO 2 e and 73 MMmtCO 2 e in GHG emissions from boiler electrification, with and without the replacement of byproduct fuels, respectively, under the current electric grid, and a reduction of 19 MMmtCO2e and 7 MMmtCO2e in GHG emissions under a future high renewables electric grid. GHG emissions savings are currently possible only in certain regions of the U.S. unless future grids are decarbonized. We also provide discussion that could be useful for policy makers and manufacturing facilities for advancing the electrification of industrial boilers in locations and industries toward fuel savings and GHG emissions reductions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Atomistic modeling of tensile deformation and fracture of carbon fibers: Nanoscale stress redistribution, effect of local structural characteristics and nanovoids

The mechanisms of tensile deformation and fracture of carbon fibers are investigated in large-scale molecular dynamics simulations. The simulations performed for computational samples with different structural characteristics enable analysis of the structural sensitivity of the mechanical properties. It is found that the carbon fiber nanostructure can be represented by a fine (nanoscale) mixture of stiff and soft regions. Despite the nanoscale structural heterogeneity of the computational fibers, their fracture strain and tensile strength exceed the experimental values by almost an order of magnitude. Here, this observation is explained by the ability of the carbon fiber nanostructure, largely consisting of curved and folded layers of turbostratic carbon, to exhibit continuous nanoscale load redistribution during the deformation. The transient, fluid nature of a percolating cluster of stress-holding structural elements prevents stress localization at the early stage of the deformation and enables the fiber nanostructure to adapt to the increasing load. Overall, the results of the simulations suggest that the design of advanced carbon fiber manufacturing and post-processing methods focused on the elimination of critical mesoscopic structural defects (internal voids, foreign inclusions, and surface flaws) can yield a substantial increase in the strength and fracture strain of carbon fibers.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Experimental characterization of a triply periodic minimal surface PCM-to-air thermal energy storage device

Here, the expansion of energy production has intensified research into thermal energy storage (TES) to manage variability and improve system efficiency. Heat exchanger design is critical to achieving high power densities in TES systems. This study presents a high-surface-area phase change material (PCM)-to-air heat exchanger fabricated via resin-based stereolithography with a novel gyroid-based geometry tailored for enhanced performance. Material properties of the commercial PCM and resin were characterized using analytical techniques. A controlled air loop was used to evaluate heat transfer and pressure drop at various flow rates and inlet temperatures. Results show a strong dependence on the inlet temperature difference (ΔT) relative to the PCM melting point. During charging at the highest flow rate, increasing the inlet air temperature from ΔT = 5 °C to 20 °C above the melting point increased the average heat transfer rate by 187%. During discharging, decreasing the inlet temperature by the same ΔT below the melting point led to a 232% increase. Notably, the high-surface-area design enabled nearly symmetric charging and discharging behavior, a novel result for PCM-based TES systems which are often restricted by natural convection and other effects. The overall heat transfer coefficient was calculated and compared to values from standard design correlations. The maximum thermal effectiveness reached 95% at ΔT = 20 °C and a moderate flow rate of 34 m 3 /h. Peak coefficients of performance (COP) of 7 during charging and 6.4 during discharging were observed at ΔT = 20 °C and a low flow rate of 20 m 3 /h. These results demonstrate the viability of additively manufactured geometries for advanced TES applications.

25 ENERGY STORAGE↗

DOE OSTI2025

Post-build stress-relief optimization for laser powder bed fusion 316H stainless steel

Nuclear energy remains a critical component of a diversified and efficient energy portfolio, offering reliable, high-capacity, and low-carbon power. However, in the U.S., aging infrastructure and the slow qualification and deployment of advanced materials and manufacturing techniques hinder progress in next-generation reactor technologies. This study explores the application of laser powder bed fusion (LPBF) additive manufacturing for stainless steel 316H, with a focus on optimizing post-build heat treatments to enhance material properties for high-temperature nuclear applications. The research targets the optimization of stress-relief temperatures to alleviate postbuild residual stresses, ensuring improvements in the microstructural corelated properties. A series of microstructural and mechanical evaluations were performed on LPBF-printed SS-316H samples which were subjected to annealing at temperatures varying between 650 °C and 850 °C. X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses revealed that increasing the heattreatment temperature accelerated dislocation recovery. Vickers microhardness measurements showed an initial reduction in values, followed by stabilization over extended durations at all the temperatures. While higher temperatures facilitated faster recovery, they also promoted carbide precipitation along grain and solidification cell boundaries, narrowing the safe processing window. In contrast, heat treatment at 650°C preserved the cellular substructure and enabled controlled carbide precipitation over time. In conclusion, these findings highlight the importance of time–temperature optimization and suggest that 650°C for up to 2 h provides the most favorable balance between recovery and carbide control for a stress-relief treatment.

316 stainless steel↗

DOE OSTI2025

A novel approach for large-scale wind energy potential assessment

Increasing wind energy generation is central to grid decarbonization, yet methods to estimate wind energy potential are not standardized, leading to inconsistencies and even skewed results. This study aims to improve the fidelity of wind energy potential estimates through an approach that integrates geospatial analysis and machine learning (i.e., Gaussian process regression). We demonstrate this approach to assess the spatial distribution of wind energy capacity potential in the Contiguous United States (CONUS). We find that the capacity-based power density ranges from 1.70 MW/km2 (25th percentile) to 3.88 MW/km2 (75th percentile) for existing wind farms in the CONUS. The value is lower in agricultural areas (2.73 ± 0.02 MW/km2, mean ± 95 % confidence interval) and higher in other land cover types (3.30 ± 0.03 MW/km2). Notably, advancements in turbine manufacturing could reduce power density in areas with lower wind speeds by adopting low specific-power turbines, but improve power density in areas with higher wind speeds (>8.35 m/s at 120m above the ground), highlighting opportunities for repowering existing wind farms. Wind energy potential is shaped by wind resource quality and is regionally characterized by land cover and physical conditions, revealing significant capacity potential in the Great Plains and Upper Texas. The results indicate that areas previously identified as hot spots using existing approaches (e.g., the west of the Rocky Mountains) may have a limited capacity potential due to low wind resource quality. Improvements in methodology and capacity potential estimates in this study could serve as a new basis for future energy systems analysis and planning.

Dai, Tao↗

DOE OSTI2026

Strong and recyclable bio-derived poly(ester amide) hot-melt adhesive

Bio-based adhesives offer inherent advantages over conventional petrochemical-derived systems, including renewable sourcing, reduced environmental impact and potential degradability. However, most bio-based adhesives suffer from poor adhesion strength, limited substrate compatibility and a lack of chemical recyclability. Here, in this work, we present a bio-derived multiblock poly(ester amide) adhesive that leverages microphase segregation between different segments to reconcile mechanical robustness with strong interfacial bonding. Notably, this multiblock architecture is accessed through a one-pot, selective acceptorless dehydrogenative polymerization, obviating the need for multistep synthesis. The materials exhibit excellent adhesion across a range of substrates including metals, glass and wet wood surpassing commercial benchmarks, while also demonstrating thermal stability, tunable mechanical properties and closed-loop chemical recyclability even in the presence of other commodity plastics. Furthermore, the adhesive strength of these materials could be tuned for various potential applications through control over the chemical composition of the polymer. By integrating renewable feedstocks, high-performance functionality and efficient chemical circularity within a single platform, this work provides a viable pathway toward more sustainable adhesive technologies and contributes to advancing circular materials manufacturing.

09 BIOMASS FUELS↗

DOE OSTI2026

Ionic liquid-enhanced recycling of lithium-ion battery black mass via heavy liquid centrifugal separation

Direct recycling of lithium-ion batteries (LIBs) is of great significance to supply chain security and environmental protection by restoring spent battery materials to their original purpose without destroying their chemical structure. One of the key processes for direct recycling is to separate valuable anode and cathode active materials from black mass. This study evaluates ionic liquid-enhanced Heavy Liquid Centrifugal Separation (HLCS) as an efficient method for separating LIB black mass into its constituent anode and cathode materials. The optimized HLCS process achieved a separation efficiency of over 95%, yielding a graphite-rich upper layer and an NMC-rich lower layer. Characterization by thermogravimetric analysis (TGA), X-ray diffraction (XRD), and inductively coupled plasma-optical emission spectroscopy (ICP-OES) confirmed the purity and structural integrity of the recovered fractions. The addition of N-methyl-2-pyrrolidone and ionic liquid 1-ethyl-3-methylimidazolium bromide decoupled entangled particles, while subsequent treatment of the anode layer with 1-(2,3-dihydroxypropyl)-3-methylimidazolium chloride further enhanced separation purity. The recycled graphite exhibited comparable battery performance to pristine graphite. These results demonstrate HLCS as a promising LIB recycling strategy, advancing sustainable battery manufacturing.

Adigun, Babafemi [Univ. of Tennessee, Knoxville, T↗

DOE OSTI2023

Effect of Interlayer Time-Lapse and Workability Retention on Printed Concrete Performance

The rapid advancement of additive manufacturing technologies for concrete construction requires further understanding of material performance and its relationship with print parameters such as interlayer time-lapse and workability retention during the print. Here this study addresses the effects of the time delay between consecutive extruded layers on the mechanical performance of printed concrete using a mix designed by Quikrete. A series of specimens were prepared with printing delays of 10, 15, 30, and 60 min. This work correlates the change in fracture properties, mechanical performance, and workability retention with the interlayer time-lapse between two consecutive concrete layers. Interlayer time-lapse is defined as the time difference in printing of two consequent layers of concrete. It affects individual properties differently based on the loading direction and test types, but it is consistent in terms of interlayer performance. The understanding of the effect of interlayer time-lapse on the fracture properties between two consecutive printed layers fills the knowledge gaps in printed concrete research. Therefore, this study can help researchers perform required optimization and safely estimate the performance of printed concrete.

3D printing↗

DOE OSTI2021

A 1D Lyman-alpha profile camera for plasma edge neutral studies on the DIII-D tokamak

A one dimensional, absolutely calibrated pinhole camera system was installed on the DIII-D tokamak to measure edge Lyman-alpha (Ly-α) emission from hydrogen isotopes, which can be used to infer neutral density and ionization rate profiles. The system is composed of two cameras, each providing a toroidal fan of 20 lines of sight, viewing the plasma edge on the inboard and outboard side of DIII-D. The cameras’ views lie in a horizontal plane 77 cm below the midplane. At its tangency radius, each channel provides a radial resolution of ~2 cm full width at half maximum (FWHM) with a total coverage of 22 cm. Each camera consists of a rectangular pinhole, Ly-α reflective mirror, narrow-band Ly-α transmission filter, and a 20 channel AXUV photodetector. The combined mirror and transmission filter have a FWHM of 5 nm, centered near the Ly-α wavelength of 121.6nm and is capable of rejecting significant, parasitic carbon-III (C-III) emission from intrinsic plasma impurities. To provide a high spatial resolution measurement in a compact footprint, the camera utilizes advanced engineering and manufacturing techniques including 3D printing, high stability mirror mounts, and a novel alignment procedure. Absolutely calibrated, spatially resolved Ly-α brightness measurements utilize a bright, isolated line with low parasitic surface reflections and enable quantitative comparison to modeling to study divertor neutral leakage, main chamber fueling, and radial particle transport.

Ionization Rate↗

DOE OSTI2022

Direct ink write multi-material printing of PDMS-BTO composites with MWCNT electrodes for flexible force sensors

Here, with recent advances of additive manufacturing technology, direct ink write (DIW) printing has allowed to incorporate multi-material printing of various materials with freedom of design and complex geometric shapes to complete functional sensors in a one-step fabrication. This paper introduces the use of DIW 3D printing of polydimethylsiloxane (PDMS) with barium titanate (BTO) filler as stretchable composites with tunable piezoelectric properties that can be used for force sensors applications. To improve the bonding between stretchable piezoelectric composites and electrodes, multi-walled carbon nanotubes was included in the fabrication of electrodes at a fixed ratio of 11 wt. %. The alignment of the BTO dipoles was achieved through corona poling method, which applies an electric charge on the surface layer of the functional material, aligning the dipoles in the desired direction and thus gaining the piezoelectricity. Different BTO mixing ratios (10–50 wt. %) were evaluated in order to obtain tunable piezoelectric properties and compare the sensitivity with respect their elastic properties. Tensile testing and piezoelectric testing were carried out to characterize mechanical and piezoelectric properties. Results showed that fabricated PDMS with 50 wt. % BTO gave the highest piezoelectric coefficient (d 33 ) of 11.5 pC N -1 and with an output voltage of 385 mV under compression loading of >200 lbF. This demonstrates feasibility of using multi-material DIW printing to fabricate piezoelectric force sensors with integrated electrodes in one-step without compromising the flexibility of the material.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Prediction and Validation of Flow Properties in Porous Lattice Structures

High-porosity metal foams have been extensively studied as an attractive candidate for efficient and compact heat exchanger design. With the advancements in additive manufacturing, such foams can be manufactured with controlled topology to yield highly tailorable mechanical and transport properties. In this study, a lattice Boltzmann method (LBM)-based pore-scale model is implemented to simulate the fluid flow in additively manufactured (AM) metal foams with unit cell topologies of Cube, Face Diagonal (FD)-Cube, Tetrakaidecahedron (TKD), and Octet lattices. The pressure gradient versus average velocity profiles predicted by the LBM model were validated against in-house measurements on the AM lattice samples with the same unit cell topologies. Based on the simulation results, a novel hybrid model is proposed to accurately predict the volume averaged flow properties (permeability and inertial coefficients) of the four structures. Specifically, the linear LBM (neglecting inertial forces) is first implemented to obtain the intrinsic permeability, and then the standard LBM is applied to obtain the inertial coefficient. Convenient correlations for those flow properties as a function of porosity and fiber diameter are constructed. The effects of the AM print qualities on the flow properties are also discussed. The advantages of the hybrid model compared to the polynomial fitting approach for determining flow properties are discussed and compared quantitatively. The hybrid model and presented results are valuable for flow and thermal transport evaluation when designing new metal foams for specific applications and with different materials and topologies. Finally, the presented correlations based on pore-scale simulations can also be conveniently used in volume-averaged models to predict the macroscale flow behavior in such complex structures.

42 ENGINEERING↗

DOE OSTI2024

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Micro-architected material design for mechanical response

Rapid advances in additive manufacturing (AM) have enabled the creation of micro-architected materials—also known as mechanical metamaterials—with unprecedented control over fine-scale geometries and arrangements of multiple material constituents. These “materials” can achieve unique and extraordinary effective mechanical properties through their complex architectures rather than composition alone. A key challenge is to design for these bespoke effective mechanical responses within the constraints of available AM techniques (i.e., given a set of desired effective properties), identify a (often nonunique) micro-architecture and selection of material constituents that achieves them. Two main strategies have emerged. Gradient-based methods use sensitivity analysis to iteratively refine candidate designs, while data-driven methods learn micro-architecture-constituent relationships from existing examples to propose new designs. This article reviews these design approaches for micro-architected materials with tailored mechanical responses that can be fabricated by AM as well as their applications.

Spadaccini, Christopher M [Lawrence Livermore Nati↗

DOE OSTI2020

Understanding Microstructural Effects on Dynamic Performance Towards the Development of Shock Metamaterials

With the recent advances in additive manufacturing (AM), long-range periodic lattice assemblies are being developed for vibration and shock mitigation components in aerospace and military applications with unique geometric and topological structures. There has been extensive work in understanding the static properties associated with varying topology of these lattice architectures, but there is almost no understanding of microstructural affects in such structures under high-strain rate dynamic loading conditions. Here we report the shock behavior of lattices with varying intrinsic grain structures achieved by post process annealing. High resolution 316L stainless steel lattices were 3D printed by a laser-powder bed fusion machine and characterized by computed tomography. Subsequent annealing resulted in stress-relieved and recrystallized lattices. Overall the lattices had strong cubic texture aligning with the x-, y- and z-directions of the build with a preference outside the build direction (z). The recrystallized sample had more equiaxed polygonal grains and a layer of BCC ferrite at the surface of the structure approximately 1 grain thick. Upon dynamic compression the as-deposited lattice showed steady compaction behavior while the heat-treated lattices exhibit negative velocity behavior indicative of failure. We attribute this to the stiffer BCC ferrite in the annealed lattices becoming damaged and fragmenting during compression.

36 MATERIALS SCIENCE↗