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Mechanical behavior of nanocomposites

Published online by Cambridge University Press:  10 January 2019

Markus J. Buehler
Affiliation:
Massachusetts Institute of Technology, USA; mbuehler@mit.edu
Amit Misra
Affiliation:
Department of Materials Science and Engineering, University of Michigan, USA; amitmis@umich.edu

Abstract

While initial interest in nanocomposites was to explore size effects in mechanical behavior, current research is focused around the new paradigm of interface-dominated/enabled mechanical behavior. The use of advanced computational tools to accelerate the discovery, design, and fundamental understanding of behavior is highlighted along with novel synthesis and in situ nanomechanical characterization tools. Designed interfaces can be used as building blocks to create new forms of hierarchical composites with unprecedented mechanical and physical behavior. New frontiers in the field of mechanical behavior of nanocomposites involving metallic, nanocarbon, bioinspired, and biomaterials are reviewed.

Information

Type
Mechanical Behavior of Nanocomposites
Copyright
Copyright © Materials Research Society 2019 
Figure 0

Figure 1. Example of interface-enabled behavior in model Cu-Nb metallic nanocomposites. (a) Interface atomic structures that lead to low shear strength interfaces that (b) shear under the local stress field of an impinging glide dislocation that gets trapped via core spreading in the interface plane. In addition to high yield strength, these interfaces give rise to unprecedented morphological stability at elevated temperatures (c), under ion irradiation (d), and under high plastic strains during severe plastic deformation processing (e).

Figure 1

Table I. Summary of interface-enabled mechanical behavior in metallic nanocomposites comprised of immiscible elements and semicoherent interfaces.

Figure 2

Figure 2. Creating functional diversity out of the systematic and directed assembly of material constituents at defined length scales. This nested, hierarchical approach allows one to engineer structure and properties at each scale by defining a specific process, leading to interactive and tunable material designs. PCN is the process at level N; SN is the structure at level N; and PN is the property at level N. Hydrogen bonds (H1) with characteristic lengths on the order of a fraction of a nanometer provide the chemical bonding that forms beta strands (H2), which in turn, form highly ordered, twisted fibrils (H3). The fibrils grow into long fibers (H4) that are then arranged into plaques or films through a casting process (H5). Reprinted with permission from Reference 46. © 2010 Springer Nature.

Figure 3

Figure 3. Additive manufacturing and subsequent testing of hierarchical composites designed using machine learning (ML). (a) Comparison of stress–strain response of ML-generated 3D printed sample (ML-opt) to its (soft and stiff) building blocks, lowest toughness geometry obtained from machine learning (ML-min), and the maximum toughness geometry from the training set (train-max). Three-dimensional printed designs for ML-opt, ML-min, and train-max are shown as insets in the figure. (b) Strain field plots obtained from digital image correlation for ML-opt (top) and ML-min (bottom). (c) Toughness and strength values for the various designs.48