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Duoss, Eric B.

Publications and source records attributed to Duoss, Eric B..

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unlocking Larger Scales and Aspect Ratios in 3D Printed Glass: Coupling Active Mixing and UV Curing for Advanced Printability and Crack Resistance

Recent developments in additive manufacturing (AM) of glass via silica-filled inks have facilitated fabrication of previously unattainable geometries and compositions. However, the maximum processable size of 15 mm limits the use of these prints in applications such as optics. A key limitation lies in the trade-off between material printability and green strength: increasing silica content in the feedstock improves crack resistance and reduces shrinkage but results in dramatic changes in viscoelastic properties that hinder flowability. Here, this paper presents a novel approach that offers expanded versatility in processable size, feedstock formulation, and printing. Described here is a direct ink writing (DIW) system coupled with an active high-shear micromixer and UV light source, capable of simultaneously printing multiple inks with a wide range of rheological properties. Choice of silica sourc, solvent, UV-curable binder, and dispersant is used to tune the ink rheology and improve printability and mechanical properties. Imparting high shear with the micromixer while UV-curing the extrudate allows for increased ink viscosities and reduced nozzle diameters, enabling printing finer feature sizes. With these advances, thin-walled high-aspect ratio structures and a crack-free glass disk measuring 44 mm in diameter are demonstrated, an increase of 3× in the greatest dimension compared to current state-of-the-art.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Additive manufacturing of multiscale NiFeMn multi-principal element alloys with tailored composition

Nanostructured multi-principal element alloys (MPEAs) have been explored as next-generation engineering materials due to unique mechanical and functional properties which have significant advantages over traditional dilute alloys. However, the practical applications of nanostructured MPEAs are still limited due to the lack of scalable processing approaches to prepare a large quantity of nanostructured MPEAs, as well as lack of an efficient pathway for high-throughput discovery of better functional nanostructured MPEAs within their vast compositional space. Here we tackle these challenges by presenting an integrated approach by combining direct-ink-writing-based additive manufacturing, solid-state sintering, and chemical dealloying to manufacture hierarchically porous MPEAs. The hierarchical structure is comprised of macro- and micro-scale pores introduced via extrusion printing and polymer decomposition during sintering, as well as nanoscale pores formed via chemical dealloying. The macro- and micro-scale pores allow efficient dealloying of a large mass of material as the diffusion length that the corroding medium must penetrate remains at the scale of the ligaments formed after sintering (∼10 μm), despite the large volume of the 3D-printed samples. In addition, this integrated approach enables versatile control of the alloy composition via precisely tuning the ratio of elemental powders in the starting ink, thus offering a pathway for high-throughput discovery of novel functional MPEAs. As a case study, multiscale macro/micro/nanoporous NiFeMn MPEAs with three different compositions were investigated as catalysts to reduce the overpotential of oxygen evolution reaction (OER), where NiFeMn-based electrocatalysts display composition-dependent performance such that the overpotential measured at a current of 0.5 A g −1 for OER increases in the order of Ni 58 Fe 29 Mn 13 ⩽ Ni 64 Fe 26 Mn 10 < Ni 76 Fe 18 Mn 6 . This introduced manufacturing process offers new opportunities for scalable fabrication and rapid screening of nanostructured multi-component complex alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

3D printed optimized electrodes for electrochemical flow reactors

Recent advances in 3D printing have enabled the manufacture of porous electrodes which cannot be machined using traditional methods. With micron-scale precision, the pore structure of an electrode can now be designed for optimal energy efficiency, and a 3D printed electrode is not limited to a single uniform porosity. As these electrodes scale in size, however, the total number of possible pore designs can be intractable; choosing an appropriate pore distribution manually can be a complex task. To address this challenge, we adopt an inverse design approach. Using physics-based models, the electrode structure is optimized to minimize power losses in a flow reactor. The computer-generated structure is then printed and benchmarked against homogeneous porosity electrodes. We show how an optimized electrode decreases the power requirements by 16% compared to the best-case homogeneous porosity. Future work could apply this approach to flow batteries, electrolyzers, and fuel cells to accelerate their design and implementation.

25 ENERGY STORAGE↗
DOE OSTI2024

Three dimensional porous siloxanes using leachable porogen particles

A product of additive manufacturing with a silicone-based ink includes a plurality of continuous filaments comprised of a siloxane matrix, wherein the continuous filaments are arranged in a geometric pattern, a plurality of inter-filament pores defined by the geometric pattern of the continuous filaments, and a plurality of intra-filament pores having an average diameter in a range of greater than 1 micron to less than 50 microns.

Perez, Lemuel Perez↗
DOE OSTI2024

Interpenetrating 3D Electrodes for High-Rate Alkaline Water Splitting

Mass transfer is critical for the reaction kinetics and efficiency of alkaline water splitting (AWS). For AWS to operate at high current densities (hundreds of mA/cm 2 ), the device architecture must ensure a large catalytic surface area, rapid ion diffusion, and minimal solution and charge transfer resistances. Effective electrodes should also facilitate gas bubble detachment and release. 3D-printed electrodes have shown promise, but stacking them increases the ion diffusion length and solution resistance. Here we demonstrate a new device architecture with interpenetrating gyroid electrodes, providing a large ion-accessible surface area and gas diffusion channels. This design significantly reduces the interelectrode distance, lowering ion diffusion length and solution resistance. Simulations show faster ion diffusion and higher current density in the interpenetrating configuration compared with separate electrodes. This improved performance, especially at low temperatures and high current densities, highlights a promising strategy for enhancing AWS and other electrochemical systems limited by slow ion diffusion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R 2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward Multiscale, Multimaterial 3D Printing

Biological materials and organisms possess the fundamental ability to self-organize, through which different components are assembled from the molecular level up to hierarchical structures with superior mechanical properties and multifunctionalities. These complex composites inspire material scientists to design new engineered materials by integrating multiple ingredients and structures over a wide range. Additive manufacturing, also known as 3D printing, has advantages with respect to fabricating multiscale and multi-material structures. The need for multifunctional materials is driving 3D printing techniques toward arbitrary 3D architectures with the next level of complexity. In this paper, the aim is to highlight key features of those 3D printing techniques that can produce either multiscale or multimaterial structures, including innovations in printing methods, materials processing approaches, and hardware improvements. Several issues and challenges related to current methods are discussed. Ultimately, the authors also provide their perspective on how to realize the combination of multiscale and multimaterial capabilities in 3D printing processes and future directions based on emerging research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition

Numerous cutting-edge scientific technologies originate at the laboratory scale, but transitioning them to practical industry applications is a formidable challenge. Traditional pilot projects at intermediate scales are costly and time-consuming. An alternative, the pilot-scale model, relies on high-fidelity numerical simulations, but even these simulations can be computationally prohibitive at larger scales. To overcome these limitations, we propose a scalable, physics-constrained reduced order model (ROM) method. The ROM identifies critical physics modes from small-scale unit components, projecting governing equations onto these modes to create a reduced model that retains essential physics details. We also employ Discontinuous Galerkin Domain Decomposition (DG-DD) to apply ROM to unit components and interfaces, enabling the construction of large-scale global systems without data at such large scales. Here this method is demonstrated on the Poisson and Stokes flow equations, showing that it can solve equations about 15–40 times faster with only ~1% relative error. Furthermore, ROM takes one order of magnitude less memory than the full order model, enabling larger scale predictions at a given memory limitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗