Fracture analysis of chopped carbon fiber sheet molding compound composite under tensile loading via in-situ μXCT
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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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Highlights: • In personalized cancer medicine, each patient receives a specific treatment. • The tumor microenvironment and interpersonal differences can affect treatment. • Organoid culture systems fully recapitulate the tumor microenvironments. In contrast to conventional cancer treatment, in personalized cancer medicine each patient receives a specific treatment. The response to therapy, clinical outcomes, and tumor behavior such as metastases, tumor progression, carcinogenesis can be significantly affected by the heterogeneous tumor microenvironment (TME) and interpersonal differences. Therefore, using native tumor microenvironment mimicking models is necessary to improving personalized cancer therapy. Both in vitro 2D cell culture and in vivo animal models poorly recapitulate the heterogeneous tumor (immune) microenvironments of native tumors. The development of 3D culture models, native tumor microenvironment mimicking models, made it possible to evaluate the chemoresistance of tumor tissue and the functionality of drugs in the presence of cell-extracellular matrix and cell-cell interactions in a 3D construction. Various personalized tumor models have been designed to preserving the native tumor microenvironment, including patient-derived tumor xenografts and organoid culture strategies. In this review, we will discuss the patient-derived organoids as a native tumor microenvironment mimicking model in personalized cancer therapy. In addition, we will also review the potential and the limitations of organoid culture systems for predicting patient outcomes and preclinical drug screening. Finally, we will discuss immunotherapy drug screening in tumor organoids by using microfluidic technology.
The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.
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The research of this project integrates and advances state-of-the-art theoretical methods for geometrically complex BCP assembly (Grason), synthesis of precisely-defined BCP architectures (Coughlin), and quantitative and multi-scale approaches to morphological characterization of BCP assembly (Thomas).
The research performed integrated and advanced state-of-the-art theoretical methods for geometrically-complex BCP assembly (Grason), synthesis of precisely-defined BCP architectures (Coughlin), and quantitative and multi-scale approaches to morphological characterization of BCP assembly (Thomas).
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We propose developing numerical methods informed by novel experimental diagnostics that transition from solid-to-fluid, while accurately predicting the stress and deformation regardless of phase.
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This project aimed to uncover molecular design rules for block copolymer (BCP) assemblies of triply-periodic network (TPN) phases, which are highly sought after, yet elusive, nanostructures for many functional material applications that rely on their combination of symmetry and polycontinuous domain connectivity [R1, R2], such as topological-photonics, supercapacitors and ultrafiltration media. While they have been a target of “bottom up” approaches to nanostructured, hybrid materials, the ability to manipulate the tubular network morphology of TPN assemblies beyond the cubic double-gyroid (DG) phase has advanced relatively little.
• Selective photopolymerization of a liquid photocurable resin using ultraviolet (UV) or visible light in discrete layers • Photocurable resins are typically a mixture of monomer(s), oligomer(s), photoinitator(s), and additive(s), when applicable • Recent advances in high-performance resins have made VPP increasingly viable for functional tooling applications