Mechanomaterials: A Rational Deployment of Forces and Geometries in Programming Functional Materials

Mechanomaterials and Nanomechanics: Toward Proactive Design of Material Properties and Functionalities

While conventional mechanics of materials offers a passive understanding of the mechanical properties of materials in existing forms, a paradigm shift, referred to as mechanomaterials, is emerging to enable the proactive programming of materials' properties and functionalities by leveraging force-geometry-property relationships. One of the foundations of this new paradigm is nanomechanics, which permits functional and structural materials to be designed based on principles from the nanoscale and beyond. Although the field of mechanomaterials is still in its infancy at the present time, we discuss the current progress in three specific directions closely linked to nanomechanics and provide perspectives on these research foci by considering the potential research directions, chances for success, and existing research capabilities. We believe this new research paradigm will provide future materials solutions for infrastructure, healthcare, energy, and environment.

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.

Micromechanical interlocking structure at the filler/resin interface for dental composites: a review

Dental resin composites (DRCs) are popular materials for repairing caries or dental defect, requiring excellent properties to cope with the complex oral environment. Filler/resin interface interaction has a significant impact on the physicochemical/biological properties and service life of DRCs. Various chemical and physical modification methods on filler/resin interface have been introduced and studied, and the physical micromechanical interlocking caused by the modification of fillers morphology and structure is a promising method. This paper firstly introduces the composition and development of DRCs, then reviews the chemical and physical modification methods of the filler/resin interface, mainly discusses the interface micromechanical interlocking structures and their enhancement mechanism for DRCs, finally give a summary on the existing problems and development potential.

Micro-Scale Mechanical Metamaterial with a Controllable Transition in the Poisson's Ratio and Band Gap Formation

The ability to significantly change the mechanical and wave propagation properties of a structure without rebuilding it is currently one of the main challenges in the field of mechanical metamaterials. This stems from the enormous appeal that such tunable behavior may offer from the perspective of applications ranging from biomedical to protective devices, particularly in the case of micro-scale systems. In this work, a novel micro-scale mechanical metamaterial is proposed that can undergo a transition from one type of configuration to another, with one configuration having a very negative Poisson's ratio, corresponding to strong auxeticity, and the other having a highly positive Poisson's ratio. The formation of phononic band gaps can also be controlled concurrently which can be very useful for the design of vibration dampers and sensors. Finally, it is experimentally shown that the reconfiguration process can be induced and controlled remotely through application of a magnetic field by using appropriately distributed magnetic inclusions.

Elastically Isotropic Truss-Plate-Hybrid Hierarchical Microlattices with Enhanced Modulus and Strength

Bioinspired hierarchical design principles have been employed to create advanced architected materials. Here, a new type of truss-plate-hybrid two-level hierarchical architecture is created, referred to as the ISO-COP hierarchical lattice (isotropic truss at the first level and cubic+octet plate at the second level), in which truss-based unit cells are arranged according to the topology of the plate-based unit cell. Finite element analyses reveal that the ISO-COP hierarchical lattice outperforms the best existing octet-truss hierarchical lattices based on fractal geometries in achieving elastic isotropy and enhanced moduli. According to the designed architecture, ISO-COP and several other comparison hierarchical microlattices are fabricated via projection microstereolithography. In situ compression tests demonstrate that the fabricated ISO-COP microlattices exhibit elastic isotropy and enhanced moduli, as predicted from finite element simulations, and superior strength compared with existing fractal octet-truss hierarchical lattices. Theoretical models are further developed to predict the dependence of modulus and failure modes on two design parameters of the hierarchical lattices, with results in good agreement with those from experiments. This study relates mechanical properties of ISO-COP hierarchical lattices to their architectures at each level of hierarchy and exemplifies a route to harnessing hierarchical design principles to create architected materials with desired mechanical properties.

3D Printed Graphene-Based Metamaterials: Guesting Multi-Functionality in One Gain

Advanced functional materials with fascinating properties and extended structural design have greatly broadened their applications. Metamaterials, exhibiting unprecedented physical properties (mechanical, electromagnetic, acoustic, etc.), are considered frontiers of physics, material science, and engineering. With the emerging 3D printing technology, the manufacturing of metamaterials becomes much more convenient. Graphene, due to its superior properties such as large surface area, superior electrical/thermal conductivity, and outstanding mechanical properties, shows promising applications to add multi-functionality into existing metamaterials for various applications. In this review, the aim is to outline the latest developments and applications of 3D printed graphene-based metamaterials. The structure design of different types of metamaterials and the fabrication strategies for 3D printed graphene-based materials are first reviewed. Then the representative explorations of 3D printed graphene-based metamaterials and multi-functionality that can be introduced with such a combination are further discussed. Subsequently, challenges and opportunities are provided, seeking to point out future directions of 3D printed graphene-based metamaterials.

In-Memory Tactile Sensor with Tunable Steep-Slope Region for Low-Artifact and Real-Time Perception of Mechanical Signals

A tactile sensor needs to perceive static pressures and dynamic forces in real-time with high accuracy for early diagnosis of diseases and development of intelligent medical prosthetics. However, biomechanical and external mechanical signals are always aliased (including variable physiological and pathological events and motion artifacts), bringing great challenges to precise identification of the signals of interest (SOI). Although the existing signal segmentation methods can extract SOI and remove artifacts by blind source separation and/or additional filters, they may restrict the recognizable patterns of the device, and even cause signal distortion. Herein, an in-memory tactile sensor (IMT) with a dynamically adjustable steep-slope region (SSR) and nanocavity-induced nonvolatility (retention time >1000 s) is proposed on the basis of a machano-gated transistor, which directly transduces the tactile stimuli to various dope states of the channel. The programmable SSR endows the sensor with a critical window of responsiveness, realizing the perception of signals on demand. Owing to the nonvolatility of the sensor, the mapping of mechanical cues with high spatiotemporal accuracy and associative learning between two physical inputs are realized, contributing to the accurate assessment of the tissue health status and ultralow-power (about 25.1 μW) identification of an occasionally occurring tremor.

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young's moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young's modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young's modulus.

Nanoenabled Trainable Systems: From Biointerfaces to Biomimetics

In the dynamic biological system, cells and tissues adapt to diverse environmental conditions and form memories, an essential aspect of training for survival and evolution. An understanding of the biological training principles will inform the design of biomimetic materials whose properties evolve with the environment and offer routes to programmable soft materials, neuromorphic computing, living materials, and biohybrid robotics. In this perspective, we examine the mechanisms by which cells are trained by environmental cues. We outline the artificial platforms that enable biological training and examine the relationship between biological training and biomimetic materials design. We place emphasis on nanoscale material platforms which, given their applicability to chemical, mechanical and electrical stimulation, are critical to bridging natural and synthetic systems.

Flexible Metamaterial Electronics

Over the last decade, extensive efforts have been made on utilizing advanced materials and structures to improve the properties and functionalities of flexible electronics. While the conventional ways are approaching their natural limits, a revolutionary strategy, namely metamaterials, is emerging toward engineering structural materials to break the existing fetters. Metamaterials exhibit supernatural physical behaviors, in aspects of mechanical, optical, thermal, acoustic, and electronic properties that are inaccessible in natural materials, such as tunable stiffness or Poisson's ratio, manipulating electromagnetic or elastic waves, and topological and programmable morphability. These salient merits motivate metamaterials as a brand-new research direction and have inspired extensive innovative applications in flexible electronics. Here, such a groundbreaking interdisciplinary field is first coined as "flexible metamaterial electronics," focusing on enhancing and innovating functionalities of flexible electronics via the design of metamaterials. Herein, the latest progress and trends in this infant field are reviewed while highlighting their potential value. First, a brief overview starts with introducing the combination of metamaterials and flexible electronics. Then, the developed applications are discussed, such as self-adaptive deformability, ultrahigh sensitivity, and multidisciplinary functionality, followed by the discussion of potential prospects. Finally, the challenges and opportunities facing flexible metamaterial electronics to advance this cutting-edge field are summarized.

Shape morphing of plastic films

Three-dimensional (3D) architectures have qualitatively expanded the functions of materials and flexible electronics. However, current fabrication techniques for devices constrain their substrates to 2D geometries and current post-shape transformation strategies are limited to heterogenous or responsive materials and are not amenable to free-standing inert plastic films such as polyethylene terephthalate (PET) and polyimide (PI), which are vital substrates for flexible electronics. Here, we realize the shape morphing of homogeneous plastic films for various free-standing 3D frameworks from their 2D precursors by introducing a general strategy based on programming the plastic strain in films under peeling. By modulating the peeling parameters, previously inaccessible free-standing 3D geometries ranging from millimeter to micrometer were predicted theoretically and obtained experimentally. This strategy is applicable to most materials capable of plastic deformation, including polymers, metals, and composite materials, and can even enable 4D transformation with responsive plastic films. Enhanced performance of 3D circuits and piezoelectric systems demonstrates the enormous potential of peeling-induced shape morphing for 3D devices.

Achieving the theoretical limit of strength in shell-based carbon nanolattices

Recent developments in mechanical metamaterials exemplify a new paradigm shift called mechanomaterials, in which mechanical forces and designed geometries are proactively deployed to program material properties at multiple scales. Here, we designed shell-based micro-/nanolattices with I-WP (Schoen's I-graph-wrapped package) and Neovius minimal surface topologies. Following the designed topologies, polymeric microlattices were fabricated via projection microstereolithography or two-photon lithography, and pyrolytic carbon nanolattices were created through two-photon lithography and subsequent pyrolysis. The shell thickness of created lattice metamaterials varies over three orders of magnitude from a few hundred nanometers to a few hundred micrometers, covering a wider range of relative densities than most plate-based micro-/nanolattices. In situ compression tests showed that the measured modulus and strength of our shell-based micro-/nanolattices with I-WP topology are superior to those of the optimized plate-based lattices with cubic and octet plate unit cells and truss-based lattices. More strikingly, when the density is larger than 0.53 g cm^-3, the strength of shell-based pyrolytic carbon nanolattices with I-WP topology was found to achieve its theoretical limit. In addition, our shell-based carbon nanolattices exhibited an ultrahigh strength of 3.52 GPa, an ultralarge fracture strain of 23%, and an ultrahigh specific strength of 4.42 GPa g^-1 cm³, surpassing all previous micro-/nanolattices at comparable densities. These unprecedented properties can be attributed to the designed topologies inducing relatively uniform strain energy distributions and avoiding stress concentrations as well as the nanoscale feature size. Our study demonstrates a mechanomaterial route to design and synthesize micro-/nanoarchitected materials.

Micro-Scale Auxetic Hierarchical Mechanical Metamaterials for Shape Morphing

Shape morphing and the possibility of having control over mechanical properties via designed deformations have attracted a lot of attention in the materials community and led to a variety of applications with an emphasis on the space industry. However, current materials normally do not allow to have a full control over the deformation pattern and often fail to replicate such behavior at low scales which is essential in flexible electronics. Thus, in this paper, novel 2D and 3D microscopic hierarchical mechanical metamaterials using mutually-competing substructures within the system that are capable of exhibiting a broad range of the highly unusual auxetic behavior are proposed. Using experiments (3D microprinted polymers) supported by computer simulations, it is shown that such ability can be controlled through geometric design parameters. Finally it is demonstrated that the considered structure can form a composite capable of shape morphing allowing it to deform to a predefined shape.

Bridging pico-to-nanonewtons with a ratiometric force probe for monitoring nanoscale polymer physics before damage

Understanding the transmission of nanoscale forces in the pico-to-nanonewton range is important in polymer physics. While physical approaches have limitations in analyzing the local force distribution in condensed environments, chemical analysis using force probes is promising. However, there are stringent requirements for probing the local forces generated before structural damage. The magnitude of those forces corresponds to the range below covalent bond scission (from 200 pN to several nN) and above thermal fluctuation (several pN). Here, we report a conformationally flexible dual-fluorescence force probe with a theoretically estimated threshold of approximately 100 pN. This probe enables ratiometric analysis of the distribution of local forces in a stretched polymer chain network. Without changing the intrinsic properties of the polymer, the force distribution was reversibly monitored in real time. Chemical control of the probe location demonstrated that the local stress concentration is twice as biased at crosslinkers than at main chains, particularly in a strain-hardening region. Due to the high sensitivity, the percentage of the stressed force probes was estimated to be more than 1000 times higher than the activation rate of a conventional mechanophore.

Engineering Photoresponsive Ligand Tethers for Mechanical Regulation of Stem Cells

Regulating stem cell functions by precisely controlling the nanoscale presentation of bioactive ligands has a substantial impact on tissue engineering and regenerative medicine but remains a major challenge. Here it is shown that bioactive ligands can become mechanically "invisible" by increasing their tether lengths to the substrate beyond a critical length, providing a way to regulate mechanotransduction without changing the biochemical conditions. Building on this finding, light switchable tethers are rationally designed, whose lengths can be modulated reversibly by switching a light-responsive protein, pdDronpa, in between monomer and dimer states. This allows the regulation of the adhesion, spreading, and differentiation of stem cells by light on substrates of well-defined biochemical and physical properties. Spatiotemporal regulation of differential cell fates on the same substrate is further demonstrated, which may represent an important step toward constructing complex organoids or mini tissues by spatially defining the mechanical cues of the cellular microenvironment with light.