Research Progress of Flexible Piezoresistive Sensors Based on Polymer Porous Materials

Advancing Nanogenerators: The Role of 3D-Printed Nanocomposites in Energy Harvesting

Nanogenerators have garnered significant scholarly interest as a groundbreaking approach to energy harvesting, encompassing applications in self-sustaining electronics, biomedical devices, and environmental monitoring. The rise of additive manufacturing has fundamentally transformed the production processes of nanocomposites, allowing for the detailed design and refinement of materials aimed at optimizing energy generation. This review presents a comprehensive analysis of 3D-printed nanocomposites in the context of nanogenerator applications. By employing layer-by-layer deposition, multi-material integration, and custom microstructural architectures, 3D-printed nanocomposites exhibit improved mechanical properties, superior energy conversion efficiency, and increased structural complexity when compared to their conventionally manufactured counterparts. Polymers, particularly those with inherent dielectric, piezoelectric, or triboelectric characteristics, serve as critical functional matrices in these composites, offering mechanical flexibility, processability, and compatibility with diverse nanoparticles. In particular, the careful regulation of the nanoparticle distribution in 3D printing significantly enhances piezoelectric and triboelectric functionalities, resulting in a higher energy output and greater consistency. Recent investigations into three-dimensional-printed nanogenerators reveal extraordinary outputs, encompassing peak voltages of as much as 120 V for BaTiO3-PVDF composites, energy densities surpassing 3.5 mJ/cm2, and effective d33 values attaining 35 pC/N, thereby emphasizing the transformative influence of additive manufacturing on the performance of energy harvesting. Furthermore, the scalability and cost-effectiveness inherent in additive manufacturing provide substantial benefits by reducing material waste and streamlining multi-phase processing. Nonetheless, despite these advantages, challenges such as environmental resilience, long-term durability, and the fine-tuning of printing parameters remain critical hurdles for widespread adoption. This assessment highlights the transformative potential of 3D printing in advancing nanogenerator technology and offers valuable insights into future research directions for developing high-efficiency, sustainable, and scalable energy-harvesting systems.

Wearable flexible sensors based on dual-network ionic hydrogels with xanthan gum/sodium alginate/polyacrylamide/gallium indium alloy

With the rapid development of wearable electronic devices and smart sensors, flexible sensors have received much attention due to their excellent mechanical properties and good adaptability. However, developing a simple method to produce conductive hydrogels with excellent electrical conductivity, mechanical properties, environmental stability, and durability is still a major challenge. In this study, a novel dual-network composite flexible sensor was developed, which was mainly composed of xanthan gum (XG), sodium alginate (SA), polyacrylamide (PAAm), and gallium‑indium alloy (Ga-In). The sensor combined the good biocompatibility and thickening properties of natural polysaccharides, the flexibility of polymers, and the excellent electrical conductivity of conductive metal alloys. The sensors exhibited good mechanical properties (stress ≈ 400 KPa, strain ≈ 540 %), high fatigue resistance, recoverability and excellent environmental adaptability. In addition, the addition of liquid metal could increase the conductivity (1.83 S m-1) of the hydrogel while maintaining high transparency, and the flexible sensor device constructed from it had high sensitivity to strain (GF = 2.75). Therefore, the hydrogel as a flexible sensor showed promising applications in detecting human movement, which could monitor the movement of human joints, micro-expressions, and handwriting. This will provide new ideas for scientific management of sports and health monitoring.

Flexible Pressure Sensor with Tunable Sensitivity and a Wide Sensing Range, Featuring a Bilayer Porous Structure

Flexible piezoresistive pressure sensors have great potential in wearable electronics due to their simple structure, low cost, and ease of fabrication. Porous polymer materials, with their highly deformable internal pores, effectively expand the sensing range. However, a single-sized pore structure struggles to achieve both high sensitivity and a broad sensing range simultaneously. In this study, a PDMS-based flexible pressure sensor with a bilayer porous structure (BLPS) was successfully fabricated using clamping compression and a sacrificial template method with spherical sucrose cores. The resulting sensor exhibits highly uniform pore sizes, thereby improving performance consistency. Furthermore, since different pore sizes and thicknesses correspond to varying Young's moduli, this study achieves tunable sensitivity across a wide pressure range by adjusting the bilayer thickness ratio (maximum sensitivity of 0.063 kPa-1 in the 0-23.6 kPa range, with a pressure response range of 0-654 kPa). The sensor also demonstrates a fast response time (128 ms) and excellent fatigue stability (>10,000 cycles). Additionally, this sensor holds great application potential for facial expression monitoring, joint motion detection, pressure distribution matrices, and Morse code communication.

Optimized Magnetization Distribution in Body-Centered Cubic Lattice-Structured Magnetoelastomer for High-Performance 3D Force-Tactile Sensors

Flexible magnetic tactile sensors hold transformative potential in robotics and human-computer interactions by enabling precise force detection. However, existing sensors face challenges in balancing sensitivity, detection range, and structural adaptability for sensing force. This study proposed a pre-compressed magnetization method to address these limitations by amplifying the magnetoelastic effect through optimized magnetization direction distribution of the elastomer. A body-centered cubic lattice-structured magnetoelastomer featuring regular deformation under compression was fabricated via digital light processing (DLP) to validate this method. Finite element simulations and experimental analyses revealed that magnetizing the material under 60% compression strain optimized magnetization direction distribution, enhancing force-magnetic coupling. Integrating the magnetic elastomer with a hall sensor, the prepared tactile sensor demonstrated a low detection limit (1 mN), wide detection range (0.001-10 N), rapid response/recovery times (40 ms/50 ms), and durability (>1500 cycles). By using machine learning, the sensor enabled accurate 3D force prediction.

Natural polysaccharides-based smart sensors for health monitoring, diagnosis and rehabilitation: A review

With the rapid growth of multi-level health needs, precise and real-time health sensing systems have become increasingly pivotal in personal health management and disease prevention. Natural polysaccharides demonstrate immense potential in healthcare sensors by leveraging their superior biocompatibility, biodegradability, environmental sustainability, as well as diverse structural designs and surface functionalities. This review begins by introducing a variety of natural polysaccharides, including cellulose, alginates, chitosan, hyaluronic acid, and starch, and analyzing their structural and functional distinctions, which offer extensive possibilities for sensor design and construction. Further, we summarize several principal sensing mechanisms, such as piezoresistivity, piezoelectricity, capacitance, triboelectricity, and hygroelectricity, which provide a theoretical and technological foundation for developing high-performance healthcare sensing devices. Additionally, the review discusses the most recent applications of natural polysaccharide-based sensors in diverse healthcare contexts, including human body motion tracking, respiratory and heartbeat monitoring, electrophysiological signal recording, body temperature variation detection, and biomarker analysis. Finally, prospective development directions are proposed, such as the integration of artificial intelligence for real-time data analysis, the design of multifunctional devices that combine sensing with therapeutic functionalities, and advancements in remote monitoring and smart wearable technologies. This review aims to provide valuable insights into the development of next-generation healthcare sensors and propose novel research directions for personalized medicine and remote health management.

Semi-auxetic piezoresistive textronic

Auxetic textile sensors represent a new generation of wearable sensors, offering advantages such as high sensitivity, enhanced mechanical properties, and greater comfort due to their suitable physical features. Research in this field remains limited and is still in its nascent stages. In this work, a piezoresistive sensor with a negative Poisson's ratio was developed using the design concept of semi-auxetic yarn, where a stretchable band replaced the core to serve as a substrate for the piezoresistive sensors. A semi-auxetic piezoresistive textronic structure was created by stitching the sensors onto the substrate in a zig-zag pattern. Electromechanical analysis highlighted a trade-off between auxetic behaviour and sensitivity, with the highest sensitivity (-4.8) observed in a structure with a stitching length of 2 cm, and the highest auxeticity (-14.1) found in a structure with a stitching length of 3 cm. This type of piezoresistive textronic is suitable for applications such as yoga straps, driving safety belts, compression bandages, and breathable belly belts for pregnant women.

Characterizing Six Percolation Cases in Flexible Electronic Composites: A Monte Carlo-Based 3D Compressive Percolation Model for Wearable Pressure Sensors

This study employs a Monte Carlo-based 3D compressive percolation model to systematically analyze the electrical behavior of flexible electronic composites under compressive deformation. By simulating the spatial distribution and connectivity of conductive particles, this study identifies six distinct percolation cases, each describing a unique connectivity evolution under strain. The model reveals that excessive initial connectivity leads to saturation effects, reducing sensitivity, while a high Poisson's ratio (≥0.3) causes connectivity loss due to shear plane expansion. Notably, asymmetric particle shapes, such as cylinders and rectangles, exhibit superior percolation behavior, forming infinite clusters at lower strain thresholds (~0.4) compared to spherical particles (~0.5). Monte Carlo simulations with 3000 particles validate these findings, showing consistent trends in percolation behavior across different deformation states. By classifying and quantifying these six connectivity scenarios, this research provides a structured framework for optimizing flexible sensor designs, ensuring an optimal balance between conductivity and sensitivity. These findings contribute to advancing flexible electronics, particularly in wearable health monitoring, robotics, and smart textiles.

Advances in Porous Structure Design for Enhanced Piezoelectric and Triboelectric Nanogenerators: A Comprehensive Review

Porous structures offer several key advantages in energy harvesting, making them highly effective for enhancing the performance of piezoelectric and triboelectric nanogenerators (PENG and TENG). Their high surface area-to-volume ratio improves charge accumulation and electrostatic induction, which are critical for efficient energy conversion. Additionally, their lightweight and flexible nature allows for easy integration into wearable and flexible electronics. These combined properties make porous materials a powerful solution for addressing the efficiency limitations that have traditionally restricted nanogenerators. Recognizing these benefits, this review focuses on the essential role that porous materials play in advancing PENG and TENG technologies. It examines a wide range of porous materials, including aerogels, nano-porous films, sponges, and 2D materials, explaining how their unique structures contribute to higher energy harvesting efficiency. The review also explores recent breakthroughs in the development of these materials, demonstrating how they overcome performance challenges and open up new possibilities for practical applications. These advancements position porous nanogenerators as strong candidates for use in wearable electronics, smart textiles, and Internet of Things (IoT) devices. By exploring these innovations, the review underscores the importance of porous structures in driving the future of energy harvesting technologies.

Multipopulation Whale Optimization-Based Feature Selection Algorithm and Its Application in Human Fall Detection Using Inertial Measurement Unit Sensors

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems.