Textiles have been concomitant of human civilization for thousands of years. With the advances in chemistry and materials, integrating textiles with energy harvesters will provide a sustainable, environmentally friendly, pervasive, and wearable energy solution for distributed on-body electronics in the era of Internet of Things. This article comprehensively and thoughtfully reviews research activities regarding the utilization of smart textiles for harvesting energy from renewable energy sources on the human body and its surroundings. Specifically, we start with a brief introduction to contextualize the significance of smart textiles in light of the emerging energy crisis, environmental pollution, and public health. Next, we systematically review smart textiles according to their abilities to harvest biomechanical energy, body heat energy, biochemical energy, solar energy as well as hybrid forms of energy. Finally, we provide a critical analysis of smart textiles and insights into remaining challenges and future directions. With worldwide efforts, innovations in chemistry and materials elaborated in this review will push forward the frontiers of smart textiles, which will soon revolutionize our lives in the era of Internet of Things.

Smart Textiles for Electricity Generation.

Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN)1. The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed2-7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two-dimensional (2D) semiconductor8,9 photodiode10-12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second.

Ultrafast machine vision with 2D material neural network image sensors

Over the past few decades, two-dimensional graphene based materials (2DGMs) have piqued the interest of scientists worldwide, and the exploration of their potential applications in catalysis, sensors, electronic devices and energy storage due to their extraordinary physical and chemical properties has rapidly progressed. As for these 2DGMs, there is a complementary need to assemble 2D building blocks hierarchically into more complicated and hierarchical three-dimensional graphene-based materials (3DGMs). Such a capability is vitally crucial in order to design sophisticated and multi-functional catalysts with tailorable properties. This comprehensive review describes some important recent advances with respect to 3DGMs, including their preparation methods, characterization and applications in catalysis, e.g., photocatalysis, electrocatalysis, organic catalysis, and CO oxidation. The importance of the relationship between the structure and catalytic performance, a topic which has become a central focus of research in order to develop high-performance catalytic systems, is discussed. Likely future developments and their associated challenges are proposed and discussed.

Recent advances in three-dimensional graphene based materials for catalysis applications

The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

A comprehensive review on emerging artificial neuromorphic devices

Replacement of precious metals with earth-abundant electrocatalysts for oxygen evolution reaction (OER) holds great promise for realizing practically viable water-splitting systems. It still remains a great challenge to develop low-cost, highly efficient, and durable OER catalysts. Here, the composition and morphology of Ni–Co bimetal phosphide nanocages are engineered for a highly efficient and durable OER electrocatalyst. The nanocage structure enlarges the effective specific area and facilitates the contact between catalyst and electrolyte. The as-prepared Ni–Co bimetal phosphide nanocages show superior OER performance compared with Ni2P and CoP nanocages. By controlling the molar ratio of Ni/Co atoms in Ni–Co bimetal hydroxides, the Ni0.6Co1.4P nanocages derived from Ni0.6Co1.4(OH)2 nanocages exhibit remarkable OER catalytic activity (η = 300 mV at 10 mA cm−2) and long-term stability (10 h for continuous test). The density-functional-theory calculations suggest that the appropriate Co doping concentration increases density of states at the Fermi level and makes the d-states more close to Fermi level, giving rise to high charge carrier density and low intermedia adsorption energy than those of Ni2P and CoP. This work also provides a general approach to optimize the catalysis performance of bimetal compounds.

Fabrication of Nickel–Cobalt Bimetal Phosphide Nanocages for Enhanced Oxygen Evolution Catalysis

Neuromorphic visual systems have considerable potential to emulate basic functions of the human visual system even beyond the visible light region. However, the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges in terms of device integration and power consumption. Here we show simple two-terminal optoelectronic resistive random access memory (ORRAM) synaptic devices for an efficient neuromorphic visual system that exhibit non-volatile optical resistive switching and light-tunable synaptic behaviours. The ORRAM arrays enable image sensing and memory functions as well as neuromorphic visual pre-processing with an improved processing efficiency and image recognition rate in the subsequent processing tasks. The proof-of-concept device provides the potential to simplify the circuitry of a neuromorphic visual system and contribute to the development of applications in edge computing and the internet of things.

Optoelectronic resistive random access memory for neuromorphic vision sensors.

The programmable nature of smart textiles makes them an indispensable part of an emerging new technology field. Smart textile-integrated microelectronic systems (STIMES), which combine microelectronics and technology such as artificial intelligence and augmented or virtual reality, have been intensively explored. A vast range of research activities have been reported. Many promising applications in healthcare, the internet of things (IoT), smart city management, robotics, etc., have been demonstrated around the world. A timely overview and comprehensive review of progress of this field in the last five years are provided. Several main aspects are covered: functional materials, major fabrication processes of smart textile components, functional devices, system architectures and heterogeneous integration, wearable applications in human and nonhuman-related areas, and the safety and security of STIMES. The major types of textile-integrated nonconventional functional devices are discussed in detail: sensors, actuators, displays, antennas, energy harvesters and their hybrids, batteries and supercapacitors, circuit boards, and memory devices.

Smart Textile-Integrated Microelectronic Systems for Wearable Applications

Bioinspired in-sensor visual adaptation for accurate perception

The continuous development of electron devices towards the trend of "More than Moore" requires functional diversification that can collect data (sensors) and store (memories) and process (computing units) information. Considering the large occupation proportion of image data in both data center and edge devices, a device integration with optical sensing and data storage and processing is highly demanded for future energy-efficient and miniaturized electronic system. Two-dimensional (2D) materials and their heterostructures have exhibited broadband photoresponse and high photoresponsivity in the configuration of optical sensors and showed fast switching speed, multi-bit data storage, and large ON/OFF ratio in memory devices. In addition, its ultrathin body thickness and transfer process at low temperature allow 2D materials to be heterogeneously integrated with other existing materials system. In this paper, we overview the state-of-the-art optoelectronic random-access memories (ORAMs) based on 2D materials, as well as ORAM synaptic devices and their applications in neural network and image processing. The ORAM devices potentially enable direct storage/processing of sensory data from external environment. We also provide perspectives on possible directions of other neuromorphic sensor design (e.g., auditory and olfactory) based on 2D materials towards the future smart electronic systems for artificial intelligence.

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2D materials based optoelectronic memory: Convergence of electronic memory and optical sensor

Robust hydrogen evolution reaction (HER) in alkaline solution using a cost-effective catalyst is still a critical challenge for industrial H2O electrolysis. The dramatic depletion of interfacial H2O and hindered OH− diffusion bring about a harsh interfacial environment and thereby lead to sluggish hydrogen evolution. Here we develop heterogeneous catalysts based on defect-rich three-dimensional graphene (M/3D graphene) and achieve improved HER kinetics via surface hydroxyl group modification on the graphene surface. Our theoretical studies suggest that the surface hydroxyl groups derived from the hydroxylation process effectively attract multi-overlayer H2O clusters without any thermodynamic barrier and form a reservoir to continuously supply H2O for catalytic sites. Electrochemical characterizations indicate that the HER kinetics per active electrochemical surface area have been greatly improved after crafting abundant hydroxyl groups on the 3D graphene framework, which can be attributed to sufficient proton donors and an ameliorative interfacial pH environment. Benefiting from the surface hydroxyl group modification, the MoS2/3D graphene structure facilitates the HER at a low onset potential of 60 mV in alkaline medium and exhibits excellent durability after 3000 cycles. This surface modification strategy reveals the importance of interfacial H2O supply in HER kinetics and provides general guidance on designing highly efficient catalysts in alkaline medium.

Jiewei Chen

Papers

Optoelectronic resistive random access memory for neuromorphic vision sensors.

Smart Textile-Integrated Microelectronic Systems for Wearable Applications

Bioinspired in-sensor visual adaptation for accurate perception

2D materials based optoelectronic memory: Convergence of electronic memory and optical sensor

Improved interfacial H2O supply by surface hydroxyl groups for enhanced alkaline hydrogen evolution