Fabrication of a textile-based triboelectric nanogenerator toward high-efficiency energy harvesting and material recognition

Design of Double Strains in Triboelectric Nanogenerators toward Improving Human Behavior Monitoring

Triboelectric nanogenerators (TENGs) offer a convenient means to convert mechanical energy from human movement into electricity, exhibiting the application prospects in human behavior monitoring. Nevertheless, the present methods to improve the device monitoring effect are limited to the design of a triboelectric material level (control of electron gain and loss ability). As compared with reported work, we improve the monitoring effect of TENG-based tactile sensors by optimizing the structure of the electrode/triboelectric material interface by means of a multiple strains mechanism. Cu@Ni double-clad waste woven fabrics are used as electrodes, which are characterized by a structure with a large number of pores formed between the fibers, greatly increasing the specific surface area of the electrode and generating dynamic strain under differentiated stress fields because of their different elastic modulus. To be exact, the resin layer undergoes elastic deformation under 0.64-4.47 kPa external stress and a new deformation generates at the electrode/triboelectric material interface induced by Cu@Ni double-clad woven fabrics slip under 4.47-63.84 kPa external stress, resulting in the accumulation of triboelectric charges on the PDMS surface. The establishment of multiple strains in triboelectric material further facilitates the generation of distinct triboelectric signal waveforms that are easily distinguishable by its amplitude and peak form. Besides, combined with deep machine learning and the triboelectric effect, in an open setting, the identification accuracy of five distinct behaviors approaches 100%. This provides a new pathway for enhancing identification accuracy of a TENG-based tactile sensor.

Electrical Microneedles for Wound Treatment

Electrical stimulation has been hotpot research and provoked extensive interest in a broad application such as wound closure, tissue injury repair, and nerve engineering. In particular, immense efforts have been dedicated to developing electrical microneedles, which demonstrate unique features in terms of controllable drug release, real-time monitoring, and therapy, thus greatly accelerating the process of wound healing. Here, a review of state-of-art research concerning electrical microneedles applied for wound treatment is presented. After a comprehensive analysis of the mechanisms of electrical stimulation on wound healing, the derived three types of electrical microneedles are clarified and summarized. Further, their applications in wound healing are highlighted. Finally, current perspectives and directions for the development of future electrical microneedles in improving wound healing are addressed.

Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270 000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

Electroless Copper Plating on a Cotton Surface: Effect of Metal Ion Ligand Stability Constant on Reduction Deposition

Electroless plating facilitates the metallization of nonconductive substrate surfaces, and of note, the precise control of the bath stability constant influences the deposition process of metal particles. In this paper, trisodium citrate, potassium sodium tartrate, nitrogen triacetic acid, thiourea, and ethylenediamine tetraacetic acid disodium were selected as coordination agents, and the effect of the metal ion ligand stability constant on the reduction deposition was studied. Coordination bonds can be established between the Cu2+ and O/N/S particles in the ligand because paired electrons in O/N/S hybrid orbitals tend to occupy empty Cu2+ hybrid orbitals and establish coordination bonds. More importantly, the copper-potassium sodium tartrate ligand exhibits the lowest stability constant and lowest reduction barrier. As an exception, a consecutive Cu-plated coating with an excellent crystallinity property was deposited on the cotton surface when potassium sodium tartrate was used as the coordination agent in the plating solution. The deposition amounts are 55.2% and 74.1% after 1 and 4 h of electroless copper plating, respectively. The surface resistivity of Cu-plated cotton is 0.38 Ω/cm2, and additionally, the surface resistivity ratio before and after 1000 cycles fluctuated between 0.9 and 1.1, indicating that the Cu-plated cotton exhibits outstanding flexibility. In this paper, the deposition rate can be optimized by adjusting the copper particle ligand stability constant in the plating solution, aiming to achieve optimal results.

Highly Moisture-Resistant Flexible Thin-Film-Based Triboelectric Nanogenerator for Environmental Energy Harvesting and Self-Powered Tactile Sensing

Triboelectric nanogenerator (TENG) has been demonstrated as a sustainable energy utilization method for waste mechanical energy and self-powered system. However, the charge dissipation of frictional layer materials in a humid environment severely limits their stable energy supply. In this work, a new method is reported for preparing polymer film as a hydrophobic negative friction material by solution blending poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) and polyvinyl chloride (PVC), doping with titanium dioxide (TiO2) nanoparticles, and further surface patterning modification. The P-TENG composed of the PVDF-HFP/PVC/TiO2 composite film with optimized hydrophobic performance (WCA = 124°) achieved an output voltage of 235 V and a short-circuit current of 35 μA, which is approximately three times that of the bare PVDF-HFP-based TENG. Under charge excitation, the transferred charge of the P-TENG can reach 35 nC. When the external load resistance is 5.5 MΩ, the output peak power density can reach 1.4 W m-2. Meanwhile, the hydrophobic surface layer with a rough surface structure enables the device to overcome the influence of water molecules on charge transfer in a humid environment, quickly recover, and maintain a high output. The P-TENG can effectively monitor finger flexibility and strength and realize real-time evaluation of the exercise state and hand fatigue of the elderly and rehabilitation trainers. It has broad application prospects in self-powered intelligent motion sensing, soft robotics, human-machine interaction, and other fields.

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.