Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600 µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80 °C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF-TrFE) yarn structures are efficiently optimized. By leveraging a multi-objective Bayesian optimization-based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high-performance piezoelectric P(VDF-TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self-powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real-time signal detection.

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real-time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self-powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG-enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG-enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services.

Triboelectric nanogenerator (TENG) manifests distinct advantages such as multiple structural selectivity, diverse selection of materials, environmental adaptability, low cost, and remarkable conversion efficiency, which becomes a promising technology for micro-nano energy harvesting and self-powered sensing. Tribo-dielectric materials are the fundamental and core components for high-performance TENGs. In particular, the charge generation, dissipation, storage, migration of the dielectrics, and dynamic equilibrium behaviors determine the overall performance. Herein, a comprehensive summary is presented to elucidate the dielectric charge transport mechanism and tribo-dielectric material modification principle toward high-performance TENGs. The contact electrification and charge transport mechanism of dielectric materials is started first, followed by introducing the basic principle and dielectric materials of TENGs. Subsequently, modification mechanisms and strategies for high-performance tribo-dielectric materials are highlighted regarding physical/chemical, surface/bulk, dielectric coupling, and structure optimization. Furthermore, representative applications of dielectric materials based TENGs as power sources, self-powered sensors are demonstrated. The existing challenges and promising potential opportunities for advanced tribo-dielectric materials are outlined, guiding the design, fabrication, and applications of tribo-dielectric materials.

The development of excellently stretchable, highly mobile, and sustainable power supplies is of great importance for self-power wearable electronics. Transpiration-driven hydrovoltaic power generator (HPG) has been demonstrated to be a promising energy harvesting strategy with the advantages of negative heat and zero-carbon emissions. Herein, this work demonstrates a fiber-based stretchable HPG with the advantages of high output, portability, knittability, and sustainable power generation. Based on the functionalized micro-nano water diffusion channels constructed by the discarded mask straps (MSs) and oxidation-treated carbon nanomaterials, the applied water can continuously produce electricity during the spontaneous flow and diffusion. Experimentally, when a tiny 0.1 mL of water encounters one end of the proposed HPG, the centimeter-length device can yield a peak voltage of 0.43 V, peak current of 29.5 µA, and energy density of 5.833 mW h cm-3. By efficiently integrating multiple power generation units, sufficient output power can be provided to drive commercial electronic devices even in the stretched state. Furthermore, due to the reversibility of the electrical output during dynamic stretching-releasing, it can passively convert physiological activities and motion behaviors into quantifiable and processable current signals, opening up HPG\'s application in the field of self-powered wearable sensing.