Crack-Based Core-Sheath Fiber Strain Sensors with an Ultralow Detection Limit and an Ultrawide Working Range

An 8-Micrometer-Thick Film Strain Sensor with Conformal 3D Microstructure for Accurate Detection of Body Motion and Air Leakage

Elastomer-based resistive super-thin film strain sensors show great application potentials in electronic skins, human-machine interaction systems, wearable devices for healthcare, and machine learning algorithms. However, it is challenging to accurately monitor the deformation of human body joints and organs with curved surfaces (e.g., knees, throats, finger joints) by only taking advantage of material thickness and elasticity of conventional 2D film strain sensors. Herein, a simple strategy is developed to fabricate conformal elastomeric thin film sensors with periodic 3D microstructure inspired by the ridges and valleys of human skin for accurate signal acquisition. Specifically, an 8-micrometer-thick elastic film strain sensor with 3D microstructure is fabricated via thermoforming followed by in situ chemical growth of silver nanoparticles. The 3D film strain sensors exhibit excellent signal linearity (R2 = 0.99) and relatively high sensitivity (gauge factor = 14) over a relatively wide strain range (≈43%), with an ultra-low strain detection limit of 0.025%, enabling potential applications in human healthcare monitoring and air leakage detection. Thus, this study unveils a simple methodology to process microstructure-enabled conformable 3D film strain sensors, which show good conformability and multiple mechanical sensing functions for advancing the development of next-generation flexible strain sensors.

Advances in Crack-Based Strain Sensors on Stretchable Polymeric Substrates: Crack Mechanisms, Geometrical Factors, and Functional Structures

This review focuses on deepening the structural understanding of crack-based strain sensors (CBSS) on stretchable and flexible polymeric substrates and promoting sensor performance optimization. CBSS are cutting-edge devices that purposely incorporate cracks into their functional elements, thereby achieving high sensitivity, wide working ranges, and rapid response times. To optimize the performance of CBSS, systematic research on the structural characteristics of cracks is essential. This review comprehensively analyzes the key factors determining CBSS performance such as the crack mechanism, geometrical factors, and functional structures and proposes optimization strategies grounded in these insights. In addition, we explore the potential of numerical analysis and machine learning to offer novel perspectives for sensor optimization. Following this, we introduce various applications of CBSS. Finally, we discuss the current challenges and future prospects in CBSS research, providing a roadmap for next-generation technologies.

Hierarchical Crack Engineering-Enabled High-Linearity and Ultrasensitive Strain Sensors

Growing imperative for intelligent transformation of electro-ionic actuators in soft robotics has necessitated self-perception for accurately mapping their nonlinear dynamic responses. Despite the promise of integrating crack-based strain sensors for such a purpose, significant challenges remain in controlling crack propagation to prevent the induction of through-cracks, resulting in lower sensitivity, linearity, and poor detection limits. Herein, we propose a hierarchical crack-based synergistic enhancement structure by incorporating conductive poly(pyrrole)-coated polystyrene nanospheres and Ti3C2Tx MXene to induce cross-long sensing cracks via point-to-plane contacts, along with silver nanowires for positively engineering networked microcracks for linearity tuning. The prepared microstrain sensor achieves high linearity (GF = 152.4, R2 = 0.99) regulation within ∼6% strain range, ultralow detection limit of 0.02%, and ultrafast response/recovery time of 31 ms/32 ms under 0.2%. Notably, state-of-the-art sensing performance by detecting minimal strain changes down to one millionth, i.e., ∼1 microstrain, has been demonstrated by voiceprint recognition, while maintaining superior dynamic measurement capability and long-term stability for mechanical vibrations up to 100 Hz with a response time of 5 ms. Moreover, the introduction of an adhesive and cross-linking layer facilitates robust bonding between the actuator and sensing structure, enabling real-time tracking of the actuation strain without structural interference by a resistance change resolution of 0.01%, providing significant insights for empowering soft robotics with integrated perception and intelligence.

Structure and Wiring Optimized TT/MT Double-Helical Fiber Sensors: Fabrication and Applications in Human Motion Monitoring and Gesture Recognition

A fibrous flexible sensor, with its small size, minimally burdens the human body, ranking among the most user-friendly flexible sensors. However, its application is often limited by damage caused by electrode movement, as flexible sensors are typically attached to joints, which can be greatly alleviated by placing the two electrodes on the same side. Inspired by the hydrogen bonds in the double-helical structure of DNA, the double-helical electrode design is commonly found and applied in fiber-based batteries and supercapacitors into fibrous flexible sensors through coaxial wet-spinning and further treatment. The double helical sensor exhibits high strength and maintains stable operation and is prepared under over 300% strain with gauge factors (GF) of 0.9, 39.5, and 349, respectively, in its working ranges. This unique single-sided electrode structure also enabled applications such as water flow sensing. The sensor into a smart glove capable of real-time is further integrated, five-channel finger motion detection, and used a convolutional neural network (CNN)-based machine learning algorithm to achieve 98.8% accuracy in recognizing six common gestures. This study provides a novel approach to optimize the electrode distribution in fiber-based flexible sensors through an internally encapsulated double-helical structure, making a significant contribution to the field of flexible sensing.

3D Printing-Induced Hierarchically Aligned Nanocomposites With Exceptional Multidirectional Strain Sensing Performance

High-performance sensors capable of detecting multidirectional strains are indispensable to understand the complex motions involved in flexible electronics. Conventional isotropic strain sensors can only measure uniaxial deformations or single stimuli, hindering their practical application fields. The answer to such challenge resides in the construction of engineered anisotropic sensing structures. Herein, a hierarchically aligned carbon nanofiber (CNF)/polydimethylsiloxane nanocomposite strain sensor is developed by one-step 3D printing. The precisely controlled printing path and shear flow bring about highly aligned nanocomposite filaments at macroscale and orientated CNF network within each filament at microscale. The periodically orientated nanocomposite filaments along with the inner aligned CNF network successfully control the strain distribution and the appearance of microcracks, giving rise to anisotropic structural response to external deformations. The synergetic effect of the multiscale structural design leads to distinguishable gauge factors of 164 and 0.5 for applied loadings along and transverse to the alignment direction, leading to an exceptional selectivity of 3.77. The real-world applications of the hierarchically aligned sensors in multiaxial movement detector and posture-correction device are further demonstrated. The above findings propose new ideas for manufacturing nanocomposites with engineered anisotropic structure and properties, verifying promising applications in emerging wearable electronics and soft robotics.

Topological Polymer Networks-Enabled Mechanically Strong Polyamide-Imide Aerogel Fibers for Thermal Insulation in Harsh Environments

Aerogel fibers have sparked substantial interest as attractive candidates for thermal insulation materials. Developing aerogel fibers with the desired porous structure, good knittability, flame retardancy, and high- and low-temperature resistance is of great significance for practical applications; however, that is very challenging, especially by using an efficient method. Herein, mechanically strong and flexible aerogel fibers with remarkable thermal insulation performance are reported, which are achieved by constructing stiff-soft topological polymer networks and a multilevel hollow porous structure. The combination of polyamide-imide (PAI) with stiff chains and polyurethane (PU) with soft chains is first found to be able to form a topological entanglement architecture. More importantly, multilevel hollow pores can be constructed synchronously through just a one-step and green wet-spinning process. The resultant PAI/PU@340 aerogel fibers show an ultrahigh breaking strength of 94.5 MPa and superelastic property with a breaking strain of 20%. Furthermore, they can be knitted into fabrics with a low thermal conductivity of 25 mW/(m·K) and exhibit attractive thermal insulation property under extremely high (300 °C) and low temperatures (-191 °C), implying them as promising candidates for next-generation thermal insulation materials.

Highly Sensitive Fiber Crossbar Sensors Enabled by Second-Order Synergistic Effect of Air Capacitance and Equipotential Body

Fiber crossbars, an emerging electronic device, have become the most promising basic unit for advanced smart textiles. The demand for highly sensitive fiber crossbar sensors (FCSs) in wearable electronics is increased. However, the unique structure of FCSs presents challenges in replicating existing sensitivity enhancement strategies. Aiming at the sensitivity of fiber crossbar sensors, a second-order synergistic strategy is proposed that combines air capacitance and equipotential bodies, resulting in a remarkable sensitivity enhancement of over 20 times for FCSs. This strategy offers a promising avenue for the design and fabrication of FCSs that do not depend on intricate microstructures. Furthermore, the integrative structure of core-sheath fibers ensures a robust interface, leading to a low hysteresis of only 2.33% and exceptional stability. The outstanding capacitive response performance of FCSs allows them to effectively capture weak signals such as pulses and sounds. This capability opens up possibilities for the application of FCSs in personalized health management, as demonstrated by wireless monitoring systems based on pulse signals.

A Review of Wearable Optical Fiber Sensors for Rehabilitation Monitoring

As the global aging population increases, the demand for rehabilitation of elderly hand conditions has attracted increased attention in the field of wearable sensors. Owing to their distinctive anti-electromagnetic interference properties, high sensitivity, and excellent biocompatibility, optical fiber sensors exhibit substantial potential for applications in monitoring finger movements, physiological parameters, and tactile responses during rehabilitation. This review provides a brief introduction to the principles and technologies of various fiber sensors, including the Fiber Bragg Grating sensor, self-luminescent stretchable optical fiber sensor, and optic fiber Fabry-Perot sensor. In addition, specific applications are discussed within the rehabilitation field. Furthermore, challenges inherent to current optical fiber sensing technology, such as enhancing the sensitivity and flexibility of the sensors, reducing their cost, and refining system integration, are also addressed. Due to technological developments and greater efforts by researchers, it is likely that wearable optical fiber sensors will become commercially available and extensively utilized for rehabilitation.

A Wearable Strain Sensor Utilizing Shape Memory Polymer/Carbon Nanotube Composites Measuring Respiration Movements

Flexible wearable sensors are integral in diverse applications, particularly in healthcare and human-computer interaction systems. This paper introduces a resistive stretch sensor crafted from shape memory polymers (SMP) blended with carbon nanotubes (CNTs) and coated with silver paste. Initially, the sensor's characteristics underwent evaluation using a Universal Testing Machine (UTM) and an LCR meter. These sensors showcased exceptional sensitivity, boasting a gauge factor of up to 20 at 5% strain, making them adept at detecting subtle movements or stimuli. Subsequently, the study conducted a comparison between SMP-CNT conductors with and without the silver coating layer. The durability of the sensors was validated through 1000 cycles of stretching at 4% ∆R/R0. Lastly, the sensors were utilized for monitoring respiration and measuring human breathing. Fourier transform and power spectrum density (PSD) analysis were employed to discern frequency components. Positioned between the chest and abdominal wall for contact-based respiration monitoring, the sensors revealed a dominant frequency of approximately 0.35 Hz. Signal filtering further enhanced their ability to capture respiration signals, establishing them as valuable tools for next-generation personalized healthcare applications.

Strain-Driven Negative Resistance Switching of Conductive Fibers with Adjustable Sensitivity for Wearable Healthcare Monitoring Systems with Near-Zero Standby Power

Recently, one of the primary concerns in e-textile-based healthcare monitoring systems for chronic illness patients has been reducing wasted power consumption, as the system should be always-on to capture diverse biochemical and physiological characteristics. However, the general conductive fibers, a major component of the existing wearable monitoring systems, have a positive gauge-factor (GF) that increases electrical resistance when stretched, so that the systems have no choice but to consume power continuously. Herein, a twisted conductive-fiber-based negatively responsive switch-type (NRS) strain-sensor with an extremely high negative GF (resistance change ratio ≈ 3.9 × 108 ) that can significantly increase its conductivity from insulating to conducting properties is developed. To this end, a precision cracking technology is devised, which could induce a difference in the Young's modulus of the encapsulated layer on the fiber through selective ultraviolet-irradiation treatment. Owing to this technology, the NRS strain-sensors can allow for effective regulation of the mutual contact resistance under tensile strain while maintaining superior durability for over 5000 stretching cycles. For further practical demonstrations, three healthcare monitoring systems (E-fitness pants, smart-masks, and posture correction T-shirts) with near-zero standby power are also developed, which opens up advancements in electronic textiles by expanding the utilization range of fiber strain-sensors.

Highly Sensitive Fiber Pressure Sensors over a Wide Pressure Range Enabled by Resistive-Capacitive Hybrid Response

Soft capacitive pressure sensors with high performance are becoming increasingly in demand in the emerging flexible wearable field. While capacitive fiber pressure sensors have achieved high sensitivity, their sensitivity range is limited to low-pressure levels. As fiber sensors typically require preloading and fixation, this narrow range of high sensitivity poses a challenge for practical applications. To overcome this limitation, the study proposes resistive-capacitive hybrid response fiber pressure sensors (HFPSs) with three-layer core-sheath structures. The trigger and sensitivity enhancement mechanisms of the hybrid response are determined through model analysis and experimental verification. By adjustment of the sensitivity enhancement range of the hybrid response, the sensitivity attenuation of HFPSs is alleviated significantly. The obtained results demonstrate that HFPSs have excellent characteristics such as fast response, low hysteresis, wide response frequency, small signal drift, and good durability. The hybrid response enhances the practical sensitivity of HFPSs for various applications. With enhanced sensitivity, HFPSs can effectively monitor pulse signals at preloads ranging from 0 to 22.7 kPa. This wide range of preloads improves the fault tolerance of pulse monitoring and expands the potential application scenarios of fiber pressure sensors.

Piezoresistive Free-standing Microfiber Strain Sensor for High-resolution Battery Thickness Monitoring

Highly sensitive microfiber strain sensors are promising for the detection of mechanical deformations in applications where limited space is available. In particular for in situ battery thickness monitoring where high resolution and low detection limit are key requirements. Herein, the realization of a highly sensitive strain sensor for in situ lithium-ion (Li-ion) battery thickness monitoring is presented. The compliant fiber-shaped sensor is fabricated by an upscalable wet-spinning method employing a composite of microspherical core-shell conductive particles embedded in an elastomer. The electrical resistance of the sensor changes under applied strain, exhibiting a high strain sensitivity and extremely low strain detection limit of 0.00005 with high durability of 10 000 cycles. To demonstrate the accuracy and ease of applicability of this sensor, the real-time thickness change of a Li-ion battery pouch cell is monitored during the charge and discharge cycles. This work introduces a promising approach with the least material complexity for soft microfiber strain gauges.

Fabrication and characterization of highly sensitive flexible strain sensor based on biodegradable gelatin nanocomposites and double strain layered structures with crack for gesture recognition

Flexible sensors have attracted extensive attention in the field of human-computer interaction. However, it is still a challenging task to realize accuracy gesture recognition with flexible sensor, which requires sensor not only have high sensitivity, but also have appropriate strain detection range. Here, a high gauge factor flexible sensor (gauge factor ∼ 1296 under 12-20 % strain) based on crack structure is reported. The sensor is made of a biodegradable and stretchable gelatin composite combined with fabric bases, with good repeatability (6000 cycles) and a fast response (60 ms). Because of the double-layer structure, it has a suitable detection range (20 % strain). The sensor is manufactured by a screen-printing process, and it has been used to make data gloves and has realized 9 gestures recognition with machine learning algorithm (99.6 % accuracy). In general, this study offers a wearable gestures recognition scheme through the proposed sensor.