Nature Reviews Materials最新文献

One of the grand challenges in the physical sciences is to ‘design’ a material before it is ever synthesized. There has been fast progress in predicting new solid-state compounds with the help of quantum-mechanical computations and supervised machine learning, and yet such progress has largely been limited to materials with ordered crystal structures. In this Perspective, we argue that the computational design of entirely non-crystalline, amorphous solids is an emerging and rewarding frontier in materials research. We show how recent advances in computational modelling and artificial intelligence can provide the previously missing links among atomic-scale structure, microscopic properties and macroscopic functionality of amorphous solids. Accordingly, we argue that the combination of physics-based modelling and artificial intelligence is now bringing amorphous functional materials ‘by design’ within reach. We discuss new implications for laboratory synthesis, and we outline our vision for the development of the field in the years ahead.

A non-faradaic junction (NFJ) is a connection between an ionic conductor and an electronic conductor in which no electrochemical reaction takes place. The junction behaves like a capacitor and couples the ionic and electronic currents through chemistry, electricity and entropy. Its charge–voltage curve is sensitive to various environmental signals, allowing it to function as a sensor; because no reaction occurs, the sensing is non-destructive and long-lasting. NFJ sensors have high sensitivity, rapid response and small size, and they can be self-powered. These sensors are familiarly used in electrophysiology of the heart, brain and muscles, and applications are emerging in wearable and implantable devices and soft robotics, as well as in sensing pressure, sound, temperature and chemicals. In this Review, we discuss NFJ sensors, emphasizing the development of devices and materials for each side of the junction. The flexibility in choosing materials enables NFJ sensors to fulfil challenging requirements, such as softness, stretchability, transparency and degradability.

The ongoing need for materials with difficult-to-combine properties has driven dramatic advancements in the field of bioinspired and biomimetic (nano)structures. These materials blend order and disorder, making their structures difficult to describe and, thus, reproduce. Their practical design involves the approximate replication of geometries found in biological tissues, aiming to achieve desired functionalities using a diverse array of human-made molecular and nanoscale components. Although this approach led to the successful development of numerous high-performance nanocomposites, the rapidly growing demand for better and better materials in energy, water, health and other technologies necessitates an accelerated design process, multidimensional property assessment and, thus, a shift towards quantitative biomimetics. In this Perspective, we approach the design of complex bioinspired materials from the standpoint of interfacial chemistry and physics. Analysing typical examples of biological composites and their successful replicates, we propose a framework based on Taylor series and property differentials that quantifies their interdependence. Five specific cases are considered for limiting their cross-products in Taylor expansions, including discontinuities of differentials at interfaces and multiple scales of organization. We also discuss how the integration of theory, simulations and machine learning is central to the development of quantitative biomimetics. This approach will enable the n-dimensional optimization of contrarian properties by leveraging materials with a high volumetric density of interfaces, graph theoretical description of complex structures and hierarchical multiscale architectures.

Vision is crucial for intelligent machines to detect and interact with their environments. However, conventional artificial vision systems (AVS) are hindered by several limitations, including narrowed field of view, optical aberrations, limited adaptability and suboptimal efficiency. Advancements in nanomaterials have facilitated the development of biomimetic optoelectronics that structurally or functionally mimic biological eyes. Two main approaches have revolutionized AVS: biomimetic designs that replicate the superior optical performance of biological eyes, enhancing the field of view, imaging quality and adaptability, and neuromorphic optoelectronics that integrate processing functions at the sensory endpoints, thus boosting computational and energy efficiency. This Review emphasizes nanomaterial-based biomimetic optoelectronics, featuring novel curved image sensors and neuromorphic devices. We delve into advanced nanomaterials and innovative design strategies that underpin these novel AVS. This Review aims to offer valuable insights to inspire researchers to advance the development of next-generation vision devices.