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Magneto‐elastic materials facilitate features such as shape programmability, adaptive stiffness, and tunable strength, which are critical for advances in structural and robotic materials. Magneto‐elastic networks are commonly fabricated by employing hard magnets embedded in soft matrices to constitute a monolithic body. These architected network materials have excellent mechanical properties but damage incurred in extreme loading scenarios are permanent. To overcome this limitation, we present a novel design for elastic bars with permanent fixed dipole magnets at their ends and demonstrate their ability to self‐assemble into magneto‐elastic networks under random vibrations. The magneto‐elastic unit configuration, most notably the orientation of end dipoles, is shown to dictate the self‐assembled network topology, which can range from quasi‐ordered triangular lattices to stacks or strings of particles. Network mechanics are probed with uniaxial tensile tests and design criteria for forming stable lightweight 2D networks are established. It is shown that these magneto‐elastic networks rearrange and break gracefully at their magnetic nodes under large excitations and yet recover their original structure at moderate random excitations. This work paves the way for structural materials that can be self‐assembled and repaired on‐the‐fly with random vibrations, and broadens the applications of magneto‐elastic soft materials.

 

Abstract The Kresling truss structure, derived from Kresling origami, has been widely studied for its bi-stability and various other properties that are useful for diverse engineering applications. The stable states of Kresling trusses are governed by their geometry and elastic response, which involves a limited design space that has been well explored in previous studies. In this work, we present a magneto-Kresling truss design that involves embedding nodal magnets in the structure, which results in a more complex energy landscape, and consequently, greater tunability under mechanical deformation. We explore this energy landscape first along the zero-torque folding path and then release the restraint on the path to explore the complete two-degree-of-freedom behavior for various structural geometries and magnet strengths. We show that the magnetic interaction could alter the potential energy landscape by either changing the stable configuration, adjusting the energy well depth, or both. Energy wells with different minima endow this magneto-elastic structure with an outstanding energy storage capacity. More interestingly, proper design of the magneto-Kresling truss system yields a tri-stable structure, which is not possible in the absence of magnets. We also demonstrate various loading paths that can induce desired conformational changes of the structure. The proposed magneto-Kresling truss design sets the stage for fabricating tunable, scalable magneto-elastic multi-stable systems that can be easily utilized for applications in energy harvesting, storage, vibration control, as well as active structures with shape-shifting capability. 

A persistent challenge in molecular modeling of thermoset polymers is capturing the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a coarse-graining (CG) approach combining the energy renormalization method with Gaussian process surrogate models of molecular dynamics simulations. This allows a machine-learning informed functional calibration of DC-dependent CG force field parameters. Taking versatile epoxy resins consisting of Bisphenol A diglycidyl ether combined with curing agent of either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we demonstrated excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young’s modulus, and yield stress at any DC. We further introduced a surrogate model-enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.

 

Increased ability to predict protein structures is moving research focus towards understanding protein dynamics. A promising approach is to represent protein dynamics through networks and take advantage of well‐developed methods from network science. Most studies build protein dynamics networks from correlation measures, an approach that only works under very specific conditions, instead of the more robust inverse approach. Thus, we apply the inverse approach to the dynamics of protein dihedral angles, a system of internal coordinates, to avoid structural alignment. Using the well‐characterized adhesion protein, FimH, we show that our method identifies networks that are physically interpretable, robust, and relevant to the allosteric pathway sites. We further use our approach to detect dynamical differences, despite structural similarity, for Siglec‐8 in the immune system, and the SARS‐CoV‐2 spike protein. Our study demonstrates that using the inverse approach to extract a network from protein dynamics yields important biophysical insights.