Researchers can now observe the phonon dynamics and wave propagation in self-assembly of nanomaterials with unusual properties that rarely exist in nature. This advance will enable researchers to incorporate desired mechanical properties into reconfigurable, solution-processible metamaterials, which have wide-ranging applications — from shock absorption to devices that guide acoustic and optical energy in high-powered computer applications.
Phonons are natural phenomena that can be thought of as discrete packets of energy waves that move through the building blocks of materials, whether they are atoms, particles or 3D-printed hinges, causing them to vibrate and transfer energy. This is a quantum mechanical description of common properties observed in various contexts, including the transfer of heat, the flow of sound and even seismic waves formed by earthquakes.
Some materials, both artificial and natural, are designed to move phonons along specific paths, imparting specific mechanical attributes. Two real-life examples of this include materials used in structures to resist seismic waves during earthquakes and the evolution of the rugged yet lightweight skeletons of deep-sea sponges, which enable them to withstand the extreme pressures of deep-water environments.
“Using the liquid-phase electron microscopy technique developed in our lab at Illinois, the new study marks the first time we’ve been able to observe phonon dynamics in nanoparticle self-assemblies, acting as a new type of mechanical metamaterials,” said Qian Chen, a professor of material science and engineering at the University of Illinois Urbana-Champaign.
“This opens a new research area where nanoscale building blocks — along with their intrinsic optical, electromagnetic, and chemical properties — can be incorporated into mechanical metamaterials,” Mao said, “Enabling emerging technologies in multiple fields from robotics and mechanical engineering to information technology.”
“This work also demonstrates the potential of machine learning to advance the study of complex particle systems, making it possible to observe their self-assembly pathways governed by complex dynamics,” Pan said. “It opens new avenues for data-driven inverse design of reconfigurable colloidal metamaterials using machine learning and artificial intelligence.”
The Office of Naval Research, the National Science Foundation, the Defense Established Programto Stimulate Competitive Research and the Army Research Office supported this research.
Chen also is affiliated with the Materials Research Laboratory, chemistry, chemical and biomolecular engineering, the Carl R. Woese Institute for Genomic Biology and the Beckman Institute for Advanced Science and Technology at the U. of I.
