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.

Triboelectric nanogenerators (TENGs) represent a promising technology for next generation human–computer interaction. The effective enhancement of induced charges are critical factors that determine the recognition accuracy of TENG-based tactile sensors. Here, we propose a magnetic field-assisted TENG device utilizing waveform feature enrichment strategies to significantly enhance the tactile recognition accuracy in natural environments. An elastic micro-nano structure was fabricated on a polydimethylsiloxane (PDMS) film via a facile templating method. Leveraging the inherent hydrophobicity and microscale surface roughness of PDMS, our device demonstrates stable and distinct waveform characteristics under natural operating conditions. Importantly, the introduction of a magnetic field generates a Lorentz force, which effectively modulates induced charges within the electrode, yet minimally affects triboelectric charges at the PDMS interface. This selective modulation induces an asymmetric charge distribution inside the electrode, substantially increasing the induced charge density, consequently, subtle waveform features are markedly enhanced. These enriched signal features play a crucial role in elevating material recognition accuracy. As a result, the sensor achieves a remarkable recognition accuracy of 99% when distinguishing among ten different materials under magnetic field assistance. This work provides valuable guidelines for advancing the performance and accuracy of TENG-based tactile sensing systems.

The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles (UAVs) technology. Herein, rotor UAVs are increasingly favored by consumers due to their unique advantages. The UAVs motion is altered by adjusting propeller speed, which is governed by motor speed. Consequently, motor speed is a key factor influencing flight performance that is susceptible to environmental interference. Accurate and real-time monitoring of motor speed is essential. Conventional speed sensors are bulky, reliant on external power, and challenging to integration into compact UAVs systems. They also suffer from insufficient accuracy and unstable measurements, particularly with small motors. This article introduces a self-powered digital aircraft rotational speed sensor (SDARSS) utilizing a rotating triboelectric nanogenerators (TENGs) to address current challenges. This sensor is lightweight, energy-efficient, and self-powered, weighing only 2.185 g and measuring 3.43 mm in thickness, with an accuracy exceeding 99.94%. It measures speeds up to 10,000 revolutions per minute (rpm) with exceptional precision and stability. The sensor enables real-time monitoring of UAVs motor speeds, which is crucial for enhancing flight safety.

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.

Despite the rapid development in tribology, the frictional characteristics influenced by ferroelectric materials remain largely unexplored. Here, through first-principles calculations, we demonstrate that the interfacial electronic structures in polar β-phase poly(vinylidene fluoride) (PVDF)/graphene van der Waals (vdW) heterostructures can be effectively tuned by varying the thickness and polarization of the ferroelectric polymer β-PVDF. Our potential energy surface (PES) calculations reveal that the sliding friction at β-PVDF/graphene interfaces can be modulated by altering the polarization of β-PVDF. Specifically, reversing the polarization of β-PVDF from upward to downward, pointing towards graphene, results in an increase in the PES amplitude and frictional shear strength. Additionally, we observe a significant increase in the energy corrugation of the PES at the polar β-PVDF/graphene sliding interfaces as the number of polar β-PVDF molecular layers increases. In comparison, no thickness-dependent friction behavior is observed at the nonpolar α-PVDF/graphene interfaces. This tunable frictional behavior is attributed to the controlled internal electric field within β-PVDF, which is governed by its thickness and polarization. The internal electric field substantially influences the interfacial electronic structures, leading to a tunable PES that governs the friction properties. Our study reveals the potential of ferroelectric polymers for controlling friction, offering significant promise for novel tribological applications.

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.

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.

Energy is the most fundamental driving force for global development and economic growth, and the foundation on which human civilization builds. However, the extraction and use of traditional energy often come with environmental pollution and ecological damage. With the limited resources of fossil energy, such as oil, coal, and gas, developing renewable green energy is desperately important for protecting the ecological environment, achieving global sustainable development and simultaneously realizing zero carbon emissions. Here, the most important component of green energy, blue energy, is introduced, which refers to all kind energy related to kinetic and potential energy from water, including hydropower, ocean tide and wave energy, and offshore energy including offshore wind energy, offshore solar energy, and their hybrid energy. The current status and development trends of blue energy are presented, and a novel blue energy harvesting technology, triboelectric nanogenerator (TENG), is brief presented.

Iontronics based on nanoconfined effects exhibit enhanced ion dynamics and have become more important in the fields such as energy harvesting and storage, sensors, and human-machine communications, which maybe an alternative or supplementary solution to electronics due to their biocompatibility and safety. The enhanced ion dynamics can be attributable to the strong interactions between ions and the electrical double layer (EDL) in the nanoconfined spaces. Therefore, in this review, an overview of the EDL is firstly provided, with its distinctive nanoconfined effects in governing ion dynamics highlighted. The primary material frameworks associated with nanoconfined spaces, including nanopores, nanochannels, and multidimensional nanostructures, are systematically classified. Strategies for modulating ion dynamics through external physical and chemical fields are explored, forming the basis for iontronic applications driven by nanoconfined effects. These applications are presented, encompassing iontronic power sources, sensors, logic components such as memristors, diodes, and transistors, as well as iontronic filter capacitors, with their unparalleled advantages in biosafety, flexibility, cost-effectiveness, and environmental adaptability emphasized. Finally, existing challenges in nanoconfined iontronics are addressed, with the expectation that advancements in nanoconfined iontronics will catalyze more efficient energy and information flow.

The advancement of digital microfluidics technology has been pivotal in academic research and engineering applications. However, the prevailing limitation is that traditional voltage sources generate an excess of Joule heat, adversely impacting droplet operation. Moreover, the power supply equipment required by digital microfluidics limits its applications. Here, we propose a self-powered microdroplet manipulation (SMDM) via triboelectric nanogenerator (TENG), which presents a capability for splitting and mixing different kinds of droplets. Fundamentally, SMDM is based on the electroosmotic flow principle, thereby enabling droplet splitting in the range of from 2 to 630 μL. Notably, for droplet splitting in the range of from 5 to 60 μL, the TENG only requires a power output ranging from 2.704 to 6.084 mW. In addition, SMDM demonstrates proficiency in droplet mixing, which achieves complete mixing of 10 μL droplets in 60 s and 30 μL droplets in a mere 53 s. Therefore, leveraging the strengths of the TENG, a self-powered microdroplet manipulated system is designed for digital microfluidics. It carries significant advantages over the traditional voltage source, including self-powered, low-Joule heat, increased safety and enhanced portability. This research provides a new solution for portable applications of digital microfluidics.