Triboelectric nanogenerators (TENGs), leveraging their capability for ambient energy harvesting and self-powered sensing, have emerged as a revolutionary solution for the Internet of Things (IoTs) and distributed intelligent systems. As human exploration extends into extreme environments such as deep space, abyssal oceans, and polar regions, TENGs exhibit tremendous application potential in extreme conditions, including high humidity, large temperature differences, low temperature, and strong radiation. However, these extreme environments impose unprecedented requirements on both the structural integrity and functional performance of devices and materials. To bridge this gap, an expanding repertoire of advanced extreme manufacturing methods is being employed in TENG fabrication to transcend the performance boundaries of conventional processing. This article begins by introducing fundamental principles of TENGs, provides comprehensive review on state-of-the-art extreme manufacturing technologies and their applications in harsh environments, and offers forward-looking perspectives on future developments in this field.

Silicon-based accelerators deliver high computational precision through the von Neumann architecture, yet incur substantial energy costs due to frequent data movement and discrete logic switching. In contrast, in-materia reservoir computing harnesses the intrinsic nonlinear dynamics of materials to enable energy-efficient temporal information processing, offering a promising route toward neuromorphic hardware. Here, we report a two-terminal lateral memristor based on 2D ferroelectric CuCrP₂S₆, where electric-field-driven Cu⁺ ion migration yields continuously tunable nonlinear conductance, short-term memory, and rich relaxation dynamics—properties that closely match the physical requirements of reservoir computing. On this basis, pattern recognition and chaotic prediction were implemented. On the MNIST handwritten digit benchmark, the system achieves 88.91% accuracy. Furthermore, the reservoir achieved normalized root-mean-square errors (NRMSE) of 0.02732 and 0.3716 for autonomous prediction of the Hénon map (steps 500–550) and the Mackey-Glass (steps 500–600) time series, respectively. These results establish CuCrP₂S₆ lateral memristors as an in-materia reservoir platform for temporal information processing and highlight their potential for advancing post-Moore neuromorphic computing systems.

Ionogel, a novel flexible electronic material, presents a plethora of applications. Despite its potential, the fabrication of multifunctional ionogel with high-performance suitable for diverse scenarios remains a significant challenge. In this study, we prepare a multifunctional amphibious ionogel skin (AIGS) using a polymerizable ionic liquid (PIL) and a conductive ionic liquid (IL) in conjunction with titanium carbide (Ti₃C₂T_x-MXene). The resulting soft AIGS materials exhibit ductility, self-healing, and robust adhesion in mechanical properties due to non-covalent interactions, such as ion-dipole interactions and hydrogen bonding. They also demonstrate a wide sensing range (2%‒400%), high sensing sensitivity (gauge factor (GF) up to 6.06), and stable sensing performance (good reliability and stability after strain) in electrical properties. The hydrophobic and dynamic viscoelastic network formed by extensive C−F bonds in the used polymer matrix, ensures the AIGS’s suitability for amphibious environments. We find that AIGS has excellent triboelectric properties. Utilizing AIGS as a flexible electrode, a single-electrode triboelectric nanogenerator (SE-TENG) was constructed, achieving outstanding output performance (~300 V open-circuit voltage, 172 nA short-circuit current, and 34 nC transferred charge). This device can power commercial portable electronic devices and identify different body movements. AIGS-based wearable strain sensors have also been shown to reliably detect human motion, including larger limb movements such as finger flexion and elbow flexion and extension, as well as subtle muscle movements such as frowning and swallowing. In addition, depending on the characteristics of the AIGS application in amphibious environments, the following functions can be realized simultaneously. AIGS in an aquatic environment combined with machine learning for intelligent recognition of breathing type, in an underwater environment combined with Morse code to convey simple information, and motion monitoring in an amphibious environment, demonstrates its potential feasibility in a variety of situations.

The advent of the Internet of Things (IoT) era has significantly accelerated advancements in neuromorphic computing research. Triboelectric nanogenerators (TENGs) exhibit dual functionality as both energy harvesters and synaptic simulators, facilitated by their inherent mechanoelectrical transduction properties and seamless circuit integration capabilities. In this work, we presented a vertically contact-separated paper-based artificial synaptic device employing TENG technology. The fabricated device successfully replicates fundamental synaptic behaviors, including paired-pulse facilitation (PPF), high-pass filtering characteristics, and spatiotemporal dynamic logic operations. Through optimized circuit configurations, we achieved elementary “NOT” logic gate using single devices, while implementing “AND/NAND” logic gates and “OR/NOR” logic gates operations through two- and three-device assemblies, respectively. Capitalizing on the mechanical flexibility and lightweight of paper substrates, we further developed a trilayer artificial synaptic architecture that mimics hierarchical neural information processing. This mechanoelectrical coupling approach establishes a novel paradigm for flexible neuromorphic systems, demonstrating exceptional potential for environmentally interactive robotics and adaptive wearable prosthetics.

In recent years, the rapid progression of artificial intelligence and the Internet of Things has led to a significant increase in the demand for advanced computing capabilities and more robust data storage solutions. In light of these challenges, neuromorphic computing, inspired by human brain’s architecture and operation principle, has surfaced as a promising answer to the growing technological demands. This novel methodology emulates the biological synaptic mechanisms for information processing, enabling efficient data transmission and computation at the identical position. Two-dimensional (2D) materials, distinguished by their atomic thickness and tunable physical properties, exhibit substantial potential in emulating synaptic plasticity and find broad applications in neuromorphic computing. With respect to device architecture, memory devices based on floating-gate (FG) structures demonstrate robust data retention capabilities and have been widely used in the realm of flash memory. This review begins with a succinct introduction to 2D materials and FG transistors, followed by an in-depth discussion on remarkable research progress in the integration of 2D materials with FG transistors for applications in neuromorphic computing and memory. This paper offers a thorough review of the existing research landscape, encapsulating the notable progress in swiftly expanding field. In conclusion, it addresses the constraints encountered by FG transistors using 2D materials and delineates potential future trajectories for investigation and innovation within this area.

The laser-assisted manufacturing technology has significant advantages in meeting various demands such as complex structures, functional integration, customized devices, and cost-effectiveness, which makes it a highly attractive option for fabricating sensors. In this review, the latest advancements and strategies in intelligent sensor development through laser processing were surveyed and outlined following the interaction of laser and materials. Laser-assisted manufacturing technologies have been extensively applied in materials science and device processing. Firstly, laser technology can be utilized in a wide range of materials, encompassing carbon-based materials, metals, and metallic oxides. In the field of device scale processing, laser manufacturing is widely used in micro/nano structures, planar device construction, and stereoscopic electronic devices such as cutting, engraving, and lithography. Additionally, laser technology provides robust support for sensor applications, covering fields such as pressure sensing, temperature sensing, gas sensing, and biosensors. Furthermore, laser considerably serves in real application areas such as multifunctional sensing systems, actuators, and robots. The widespread application of laser manufacturing technology in sensor platform fabrication offers effective solutions for realizing the miniaturization, multifunctionality, and integration of sensors.

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided.

Today, energy is essential for every aspect of human life, including clothing, food, housing and transportation. However, traditional energy resources are insufficient to meet our modern needs. Self-powered sensing devices emerge as promising alternatives, offering sustained operation without relying on external power sources. Leveraging advancements in materials and manufacturing research, these devices can autonomously harvest energy from various sources. In this review, we focus on the current landscape of self-powered wearable sensors, providing a concise overview of energy harvesting technologies, conversion mechanisms, structural or material innovations, and energy storage platforms. Then, we present experimental advances in different energy sources, showing their underlying mechanisms, and the potential for energy acquisition. Furthermore, we discuss the applications of self-powered flexible sensors in diverse fields such as medicine, sports, and food. Despite significant progress in this field, widespread commercialization will necessitate enhanced sensor detection abilities, improved design factors for adaptable devices, and a balance between sensitivity and standardization.

The use of water resources for energy generation has become increasingly prevalent, encompassing the conversion of kinetic energy from streams, tides, and waves into renewable electrical power. Water energy sources offer numerous benefits, including widespread availability, stability, and the absence of carbon dioxide and other greenhouse gas emissions, making them a clean and environmentally friendly form of energy. In this work, we develop a droplet-based liquid–solid triboelectric nanogenerator (LS-TENG) using sophisticatedly designed inflatable columnar structures with inner and outer dual-electrodes. This device can be utilized to harvest both the internal droplet-rolling mechanical energy and the external droplet-falling mechanical energy, capable of being assembled into various structures for versatile applications. The design incorporates a combined structure of both internal and external TENG to optimize output performance via multiple energy harvesting strategies. The internal structure features a dual-electrode columnar-shaped LS-TENG, designed to harvest fluid kinetic energy from water droplets. By leveraging the back-and-forth motion of a small amount of water within the air column, mechanical energy can be readily collected, achieving a maximum mass power density of 9.02 W·Kg⁻¹ and an energy conversion efficiency of 10.358%. The external component is a droplet-based LS-TENG, which utilizes a double-layer capacitor switch effect elucidated with an equivalent circuit model. Remarkably, without the need for pre-charging, a single droplet can generate over 140 V of high voltage, achieving a maximum power density of 7.35 W·m⁻² and an energy conversion efficiency of 22.058%. The combined LS-TENG with a sophisticated inflatable columnar structure can simultaneously collect multiple types of energy with high efficacy, exhibiting great significance in potential applications such as TENG aeration rollers, inflatable lifejacket, wind energy harvesting, TENG tents, and green houses.

With the arrival of the era of artificial intelligence (AI) and big data, the explosive growth of data has raised higher demands on computer hardware and systems. Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to breaking the von Neumann bottleneck. Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner, with the capability to sense/store/process information of external stimuli. In this review, we have presented the piezotronic neuromorphic devices (which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator-gated field effect transistors based on device structure) and discussed their operating mechanisms and related manufacture techniques. Secondly, we summarized the research progress of piezotronic neuromorphic devices in recent years and provided a detailed discussion on multifunctional applications, including bionic sensing, information storage, logic computing, and electrical/optical artificial synapses. Finally, in the context of future development, challenges, and perspectives, we have discussed how to modulate novel neuromorphic devices with piezotronic effects more effectively. It is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/computation to facilitate the development of the Internet of Things, AI, biomedical engineering, etc.