Session: 13-15-01: Mechanics of Soft Materials I

Paper Number: 164526

Fracture of Polymer-Like Networks With Hybrid Bond Strengths 

The design and functionality of polymeric materials hinge on failure resistance. While molecular-level details drive crack evolution in polymer networks, fracture behaviors across length scales in soft materials remain unclear and difficult to probe. The key bottleneck lies in the challenge of relating molecular structure, chain scission, network connectivity, and strand mechanics to the bulk-scale material separation that occurs during failure. In this work, we systematically study the fracture mechanics of polymer-like networks with hybrid bond strengths. We employ a polymer-like network model to tune the ratios of weak and strong strands within networks in both lattice-model simulations and architected-network experiments. Our coarse-grained simulation approach gives improved computational efficiency compared with molecular dynamics, permitting realistic domain sizes for fracture studies. By capturing when strands break, this platform provides qualitative outcomes describing the growth of cracks with time. We also realize polymer networks with hybrid bond strengths by tuning the feeding ratio of two mechanophores with distinct reactivities used to crosslink tetra-PEG gels. We then conduct the pure shear fracture test to measure the fracture energies of these networks across different concentrations. We reveal that varying the strong and weak strand ratios within otherwise identical networks gives a non-monotonic relationship between intrinsic fracture energy and strong strand fraction. Networks with some weak strands can counterintuitively outperform those with exclusively strong strands. We show through computational visualization that strand type concentrations impact crack growth patterns and fracture energy trends. Cracks propagate through weak layers at low strong strand fractions. Aggregate clusters deflect or pin cracks at similar concentrations of strong and weak strands. Cracks blunt due to dispersed weak strand failure at high strong strand fractions. The sacrificial weak strands can notably deconcentrate stress near the crack tip, which toughens by delaying crack advancement. The interplay between concentration and clustering of strand types in networks with hybrid bond strengths, combined with crack growth phenomena and nonlocal energy release, provides insights into unusual fracture characteristics. These findings offer a foundation for understanding the impact of bond strength on fracture mechanisms in polymer-like networks and mechanistic strategies for fabricating materials with tailored properties. Conclusions from polymer-like network studies can directly give intuition into soft architected network fracture and indirectly shed light on trends related to polymer failure. In addition, this work can facilitate the design of tough polymer, polymer-like, and metamaterial networks with mechanisms that intrinsically resist failure, spanning applications from biomaterials to spacecraft.

Presenting Author: Chase Hartquist Massachusetts Institute of Technology

Presenting Author Biography: Chase Hartquist received his Ph.D. from the Department of Mechanical Engineering at the Massachusetts Institute of Technology working alongside Professor Xuanhe Zhao. He was awarded four graduate research fellowships from the National Science Foundation (NSF), MathWorks, the Epp and Ain Sonin Fund, and the Warren M. Rohsenow Fund to support his doctoral studies. He earned his B.S. and M.S. in Mechanical Engineering from Washington University in St. Louis where he conducted research with Professor Guy Genin. His research focuses on understanding the mechanical and failure behaviors of soft materials. This work leverages fundamental structure-property relationships across scales to inform design of high-performing soft materials and structures for emerging applications in medical technology and clean energy.

Authors:

Chase Hartquist Massachusetts Institute of Technology
Shu Wang Massachusetts Institute of Technology
Bolei Deng Georgia Institute of Technology
Haley Beech University of California Santa Barbara
Stephen Craig Duke University
Bradley Olsen Massachusetts Institute of Technology
Michael Rubinstein Duke University
Xuanhe Zhao Massachusetts Institute of Technology