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Graphene is one of the stiffest materials ever measured, and yet foams of this material experience such massive degradation in mechanical properties at low densities that they are worse than polymer foams. (Z. Qin, G. S. Jung, M. J. Kang and M. J. Buehler, Sci. Adv., 2017, 3, e1601536). 3D printed mechanical metamaterials have shown the unprecedented ability to alleviate such degradation, but all current 3D printing techniques capable of printing graphene foam are unable to reproduce the complex metamaterial architectures (e.g. insufficient resolution, toolpath limitations, etc.). Here we demonstrate high-resolution graphene foams incorporating hierarchical architecture which reduces mechanical degradation of graphene foams with decreasing density. Our technique achieves an order-of-magnitude finer resolution and far more intricate structures than any previous method. This technique opens new possibilities not only to enhance graphene foam mechanical properties, but to explore complex architectures and mesoscale effects for other graphene applications including energy storage and conversion, separations, and catalysis.
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Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing
Abstract Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process‐property relationships. In this work, a self‐driving lab is utilized that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, an underlying self‐stabilizing characteristic of VTP layer thickness is discovered as an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable‐driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing.
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- PAR ID:
- 10544638
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Science
- Volume:
- 11
- Issue:
- 44
- ISSN:
- 2198-3844
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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