Soft Adaptive Mechanical Metamaterials

Piezo-Wormbots for Continuous Crawling

Biomimetic soft robots are typically made of soft materials with bioinspired configurations. However, their locomotion is activated and manipulated by externally controlled soft actuators. In this study, piezo-wormbots were developed by automatically triggering the mechanical metamaterial-inspired soft actuator to mimic the continuous crawling of inchworms without manipulation, where crawling was controlled by the deformation of the piezo-wormbots themselves. We designed the flexible piezo-wormbots with an actuator to generate bending deformation under continuous inflation, piezoelectric rubber to automatically create internal excitation voltage to trigger deflation, as well as true legs and prolegs to convert the bending-recovering sequence into continuous crawling. We tailored the actuator to enhance the crawling performance and found that the response was critically affected by the leg pattern, inflation-to-deflation time duration ratio, air pressure, and ground environment. We observed satisfactory locomotion performance for the following tasks (pushing boxes and approaching a predefined target) through accurate self-actuated crawling under up to 51 continuous bending cycles. The maximum crawling velocity of the piezo-wormbots was found to be 16.6 mm/s, resulting in a maximum body length per second (BL/s) of 0.13, which is comparable to those of most natural inchworms (0.1-0.3 BL/s). This study offers new insight into bioinspired soft robotics and expands its biomimetic performance to continuously autonomous locomotion.

Reprogrammable Metamaterial Processors for Soft Machines

Soft metamaterials have attracted extensive attention due to their remarkable properties. These materials hold the potential to program and control the morphing behavior of soft machines, however, their combination is limited by the poor reprogrammability of metamaterials and incompatible communication between them. Here, printable and recyclable soft metamaterials possessing reprogrammable embedded intelligence to regulate the morphing of soft machines are introduced. These metamaterials are constructed from interconnected and periodically arranged logic unit cells that are able to perform compound logic operations coupling multiplication and negation. The scalable computation capacity of the unit cell empowers it to simultaneously process multiple fluidic signals with different types and magnitudes, thereby allowing the execution of sophisticated and high-level control operations. By establishing the laws of physical Boolean algebra and formulating a universal design route, soft metamaterials capable of diverse logic operations can be readily created and reprogrammed. Besides, the metamaterials' potential of directly serving as fluidic processors for soft machines is validated by constructing a soft latched demultiplexer, soft controllers capable of universal and customizable morphing programming, and a reprogrammable processor without reconnection. This work provides a facile way to create reprogrammable soft fluidic control systems to meet on-demand requirements in dynamic situations.

Viscoelastic dynamics of a soft strip subject to a large deformation

To produce sounds, we adjust the tension of our vocal folds to shape their properties and control the pitch. This efficient mechanism offers inspiration for designing reconfigurable materials and adaptable soft robots. However, understanding how flexible structures respond to a significant static strain is not straightforward. This complexity also limits the precision of medical imaging when applied to tensioned organs like muscles, tendons, ligaments and blood vessels among others. In this article, we experimentally and theoretically explore the dynamics of a soft strip subject to a substantial static extension, up to 180%. Our observations reveal a few intriguing effects, such as the resilience of certain vibrational modes to a static deformation. These observations are supported by a model based on the incremental displacement theory. This has promising practical implications for characterizing soft materials but also for scenarios where external actions can be used to tune properties.

Multi-Functional Reconfigurable Intelligent Surfaces for Enhanced Sensing and Communication

In this paper, we propose a reconfigurable intelligent surface (RIS) that can dynamically switch the transmission and reflection phase of incident electromagnetic waves in real time to realize the dual-beam or quad-beam and convert the polarization of the transmitted beam. Such surfaces can redirect a wireless signal at will to establish robust connectivity when the designated line-of-sight channel is disturbed, thereby enhancing the performance of wireless communication systems by creating an intelligent radio environment. When integrated with a sensing element, they are integral to performing joint detection and communication functions in future wireless sensor networks. In this work, we first analyze the scattering performance of a reconfigurable unit element and then design a RIS. The dynamic field scattering manipulation capability of the RIS is validated by full-wave electromagnetic simulations to realize six different functions. The scattering characteristics of the proposed unit element, which incorporates two p-i-n diodes have been substantiated through practical implementation. This involved the construction of a simple prototype and the subsequent examination of its scattering properties via the free-space measurement method. The obtained transmission and reflection coefficients from the measurements are in agreement with the anticipated outcomes from simulations.

Mechanical metamaterials and beyond

Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information processing, and advancing data-driven designs are grand challenges in the mechanical metamaterials community that could lead to truly intelligent mechanical metamaterials. In this perspective, we provide an overview of mechanical metamaterials within and beyond their classical mechanical functionalities. We discuss various aspects of data-driven approaches for inverse design and optimization of multifunctional mechanical metamaterials. Our aim is to provide new roadmaps for design and discovery of next-generation active and responsive mechanical metamaterials that can interact with the surrounding environment and adapt to various conditions while inheriting all outstanding mechanical features of classical mechanical metamaterials. Next, we deliberate the emerging mechanical metamaterials with specific functionalities to design informative and scientific intelligent devices. We highlight open challenges ahead of mechanical metamaterial systems at the component and integration levels and their transition into the domain of application beyond their mechanical capabilities.