George K. Stylios

Triboelectric nanogenerators (TENGs) emerged in 2012 as a way to harness electricity from the triboelectric effect. There have been major developments in materials and performance optimization since then. However, the testing of TENGs is something that requires attention. There are still major shortcomings in how testing is carried out and how results are reported and interpreted. Not only does this impact on the interpretation of results, but it also makes direct performance improvements difficult to compare. The problem is that there are a multitude of factors that affect the electrical output of TENGs. If these are not properly accounted for during testing, the results will not be comparable across different test rigs and laboratories. Many of the physical issues that affect the output can now be found in different research papers, but these have never been collated and synthesized in the context of testing. This paper serves as a comprehensive guide on how to test TENGs, uniquely bringing together the fabrication, mechanical, electrical, and environmental aspects of testing in a single paper. Finally, this review recommends the establishment of a new ISO standardization committee to develop protocols for testing TENG materials and products.

The growing demand for wearable electronics has driven interest in flexible fiber-based supercapacitors (F-SCs) as power sources, offering tunable sizes, adaptable shapes, and versatile design possibilities. This study presents the fabrication of a highly flexible and twistable fiber-shaped yarn supercapacitor (F-SC) via direct electrodeposition of ternary metal-oxide nanostructures (ZnMn₂O₄) onto flexible and conductive carbon yarn substrates. The uniform growth of ZnMn₂O₄ nanostructures on the carbon yarn not only enhances the capacitive performance of the fabricated devices but also significantly enhances the mechanical integrity of the electrodes, ensuring excellent bending and electrochemical stability for the F-SC device. The device exhibits a high areal capacitance of 87.6 mF/cm² at a scan rate of 10 mV/s and 35.4 mF/cm² at a current density of 0.1 mA/cm². Furthermore, it retains 92% of its capacitance after 10,000 charge–discharge cycles, achieving energy and power densities of 11 μWh/cm² and 385 μW/cm², and maintaining consistent performance under varying bending and twisting conditions. This work offers a simple, cost-effective, and efficient strategy for developing flexible and twistable fiber electrodes using a straightforward electrodeposition process. The fabricated electrodes hold great potential in developing flexible energy storage technologies and enabling seamless integration into next-generation portable and wearable electronics.

The fabrication of durable and scalable nanofiber fabrics (NFs) remains a critical challenge, limiting their practical applications in wearable electronics, smart textiles, biosensing, and energy harvesting systems. Recent advances in self-powered wearable textiles have demonstrated the potential of converting biomechanical motion into electricity, paving the way for battery-free next-generation SMART textiles. However, achieving a balance among flexibility, durability, high output performance, and wearability remains a major hurdle for real-world adoption. In this study, we introduce NanoTwist Spinning, an integrated nanospinning and yarn-twisting system designed to fabricate core-sheath nanofiber yarns (CSNYs) with high mechanical resilience and electrical conductivity. These yarns feature a precisely twisted nanofiber sheath wrapped around a conductive silver core, enabling large-scale processing through standard knitting machines to produce high-performance electronic-NFs (E-NFs). By optimizing fabrication parameters and utilizing polycaprolactone (PCL) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) polymers, we achieved uniform, stable CSNYs with an optimized nanofiber wrapping rate of 38.21%. The resulting knitted NFs exhibited exceptional mechanical properties, including 83% compressive resilience, a breaking force of 350.5 N, a tensile strength of 17.53 MPa, and an elongation of 261.8%, ensuring superior durability, wearability, and comfort. To demonstrate real-world feasibility, the fabricated PCL/PVDF-HFP NF-based triboelectric nanogenerator (TENG) achieved an impressive electrical output of 100 V and 8 μA under real-time conditions, validating its potential for energy-harvesting applications. This work marks a significant breakthrough in scalable NYs and NFs production, offering a transformative pathway for the smart textile industry and opening new frontiers in sustainable, self-powered E-Textiles.

Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco‐friendly power generation from mechanical motion. They harness mechanical energy while enabling self‐sustaining sensing for self‐powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low‐power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI‐enhanced TENG solutions.

The structure of a fabric is a highly complex assembly of fibres, which have order and regularity as well as disorder and randomness. The complexity of the structure poses challenges in defining its mechanical behaviour, particularly at low stress, which is typical to end uses. The coexistence of multiple deformations and the high degree of nonlinearity of the fabric due to fibre friction make its stress–strain relationship complicated. This article reviews the literature on friction related to the low-stress mechanics of fabrics, and it establishes its range and regularity to help with finding a unified reference model, in which although the physical meanings of fabric tensile, shear, and bending vary, they follow consistent mathematical regularities. So, invariably, their disorder and randomness needed in defining them can be obtained from fabric measurement data. It defines the scope and patterns of friction to facilitate the development of a unified reference model. It argues that although the physical interpretations of fabric tensile, shear, and bending characteristics may differ, they adhere to consistent mathematical regularities within this model, and hence extracting disorder and randomness from fabric measurement data may be achievable. This paper concludes with a number of recommendations, postulating that hysteresis caused by friction between fibres in a fabric is an important component of mechanical information, and it coexists with its purely elastic component, but it cannot be obtained directly by measurement. Seeking means to effectively decompose the friction hysteresis and pure elastic components contained in fabric mechanics measurement data will provide an accurate characterization of fabric mechanical properties and hence an accurate modelling and simulation of its behaviour, and will impact many traditional and industrial textile end uses.

The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric’s low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties.

To meet the complex and diverse demands for low-stress mechanical measurements of fabrics and other flexible materials, two integrated multidimensional force sensors with the same structure but different ranges were explored. They can support both rapid and precise low-noise, high-precision, low-cost, easy-to-use, reliable, and intelligent solutions for the complex measurement of fabric mechanics. Having analysed the mechanical relationship of the parallel beam theory, and considering the specific requirements of fabric measurement, a novel multi-dimensional force sensor is designed, capable of measuring tensile, shear, and buckling properties. Finite element analysis is used to simulate the mechanical performance of this sensor for fabric-loading/unloading measurement, and the sensitivity of the mechanical quantity transfer, the amount of sensor deformation, the stress distribution, and the degree of inter-dimensional coupling have been investigated and verified. The basis for subsequent digital processing is achieved by a low-offset, low-temperature-drift, low-power-consumption analogue front end, 24-bit ADC circuit, and signal conditioning electronics, suitable for the measurement of fabric mechanics under low stress, which is like the end-user requirements. The sensor information channel is supported by a host microcontroller with a DSP and a floating-point processing instruction set. Information processing is performed in time-sharing with the support of a multitasking real-time operating system. The purpose of designing this sensor is to facilitate the development of a new testing instrument, which will adopt the advances of current instruments whilst eliminating their shortcomings.