High-performance Strain Research Articles

A Paradigm of Computer Vision and Deep Learning Empowers the Strain Screening and Bioprocess Detection.

High-performance strain and corresponding fermentation process are essential for achieving efficient biomanufacturing. However, conventional offline detection methods for products are cumbersome and less stable, hindering the "Test" module in the operation of "Design-Build-Test-Learn" cycle for strain screening and fermentation process optimization. This study proposed and validated an innovative research paradigm combining computer vision with deep learning to facilitate efficient strain selection and effective fermentation process optimization. A practical framework was developed for gentamicin C1a titer as a proof-of-concept, using computer vision to extract different color space components across various cultivation systems. Subsequently, by integrating data preprocessing with algorithm design, a prediction model was developed using 1D-CNN model with Z-score preprocessing, achieving a correlation coefficient (R2) of 0.9862 for gentamicin C1a. Furthermore, this model was successfully applied for high-yield strain screening and real-time monitoring of the fermentation process and extended to rapid detection of fluorescent protein expression in promoter library construction. The visual sensing research paradigm proposed in this study provides a theoretical framework and data support for the standardization and digital monitoring of color-changing bioprocesses.

Muscle Fiber-Inspired High-Performance Strain Sensors for Motion Recognition and Control.

The rapid development of wearable technology, flexible electronics, and human-machine interaction has brought about revolutionary changes to the fields of motion analysis and physiological monitoring. Sensors for detecting human motion and physiological signals have become a hot topic of current research. Inspired by the muscle fiber structure, this paper proposed a highly stable strain sensor that was composed of stretchable Spandex fibers (SPF), multiwalled carbon nanotubes (MWCNTs), and silicone rubber (Ecoflex). This sensor adopted an immersion coating process in which MWCNTs were conformally deposited on SPF, and Ecoflex was filled into the fiber interstices, completing the encapsulation and filling of the SPF to construct a stable three-dimensional conductive network. Thanks to the filling of Ecoflex, contact between conductive fibers during the stretching process was avoided, resulting in a significant change in the resistance. The sensitivity of the sensor reached 54.84, which is 10 times higher than before the Ecoflex filling with a stretchable strain range of up to 70%. The encapsulation of Ecoflex also prevented the detachment of MWCNTs on the fibers during stretching, improving the mechanical stability. The sensor can be easily attached to the surface of human skin to rapidly monitor various human motion signals. Furthermore, the sensor was related to the manipulator through wireless Bluetooth to realize the intelligent control of the manipulator. This work not only provided a more precise data monitoring method for medical and motion analysis fields but also offered an innovative solution for manipulator control.

Hydrophobic PU fabric with synergistic conductive networks for boosted high sensitivity, wide linear-range wearable strain sensor.

Strain sensing fabrics are able to sense the deformation of the outside world, bringing more accurate and real-time monitoring and feedback to users. However, due to the lack of clear sensing mechanism for high sensitivity and high linearity carbon matrix composites, the preparation of high performance strain sensing fabric weaving is still a major challenge. Here, an elastic polyurethane(PU)-based conductive fabric(GCPU) with high sensitivity, high linearity and good hydrophobicity is prepared by a novel synergistic conductive network strategy. The GCPU fabric consists of graphene sheets(GS)/carbon nanotubes(CNTs) elastic conductive layer and a PU elastic substrate. GS and CNTs can be constructed into a synergistic conductive network, and the fabric is endowed with high conductivity(1.193 S m-1). Simulated equivalent circuits show that GS in the conductive network will break violently under applied strain, making the GCPU fabric extremely sensitive(gauge factor 102). CNTs are spatially distributed in GS lamellae, avoiding the phenomenon that the constructed synergistic conductive network is violently fractured under the applied strain, which leads to the decrease of linearity(0.996). SEBS was used as a dispersant and binder to uniformly disperse and closely bond GS and CNTs into PU fabrics. In addition, the hydrophobicity of SEBS makes the GCPU fabric resistant to water environment(The contact Angle is 123°). Due to the good mechanical stability of GCPU fabric, GCPU fabric has a wide strain range(0-50%) and high cycle stability(over 1000 cycles). In practice, GCPU fabric can accurately simulate and detect the size and deformation motion of human body. Therefore, the successful construction of elastic fabrics with synergistic conductive networks provides a feasible path for the design and manufacture of wearable intelligent fabrics.

Optically Decoupling Electrochromic Dynamics and In Situ Morphological Evolution of a Single Soft Polyaniline Nanoentity.

Electroresponsive multicolored materials have tremendous potential in flexible electronics and smart wearable devices. Herein, the electrochromic dynamics and in situ morphological evolution of a single soft polyaniline nanoentity can be visualized and decoupled by an opto-electrochemical imaging strategy. The durability, tinting speed, and reversibility down to the single-nanoparticle level are quantified, and the switching of transient intermediate electrochromic states is trapped. The mechanistic studies suggest that the heterogeneity of electrochromic activity is attributed to the nonuniformity of the polymer network interspersed at the nanometric level. Furthermore, the representative Pauli repulsion effect is uncovered from the self-stretching behavior of the conductive state of polyaniline at the oxidized potential. It provides novel insights for advancing high-performance electrochromic devices and flexible strain sensors, which can be dynamically manipulated by external stimuli.

Self-healing, adhesive liquid metal hydrogels based on PNIPAM microgels for high-performance temperature and strain sensors

Self-healing, adhesive liquid metal hydrogels based on PNIPAM microgels for high-performance temperature and strain sensors.

Ultrafast self-healing zwitterionic hydrogels reinforced by carboxymethyl chitosan-oxidized hyaluronic acid and graphene oxide toward high-performance strain sensors

Inspired by the inherent recuperative ability of organisms in nature, researchers have dedicated significant efforts towards developing self-healing hydrogel sensors. Although the works on self-healing hydrogels have made great progress, achieving hydrogel sensors combining with rapid and efficient healing capability, excellent mechanical properties and high sensing sensitivity remains a challenging task. In this study, we proposed a novel approach for fabricating a self-healing conductive zwitterionic hydrogel sensor by adding carboxymethyl chitosan (CMCs) and oxidized hyaluronic acid (OHA) to induce dynamic Schiff base reaction, and nano-reinforced by adding with graphene oxide (GO) nanosheets. This zwitterionic hydrogel exhibited a high tensile strength of 133 kPa and elongation at break of 878 %. By leveraging various dynamic interactions within the system, the hydrogel exhibited ultra-fast and efficient healing property, achieving a self-healing efficiency of 98.9 % within just 15 min. The hydrogel demonstrated exceptional adhesion to diverse substrates especially to glass with a maximum adhesion strength reaching up to 53.7 kPa. The hydrogel exhibited a high gauge factor (GF) value of 23.2, which showed clear and stable monitoring and sensing capabilities across various human movements ranging from swallowing to bending knees. Notably, the hydrogel could also be employed as a Morse code transmitter and flexible tablet. The zwitterionic hydrogel demonstrates its great potential applications in the field of high-performance flexible sensors as well as human-computer interaction.

High-Performance Flexible Strain Sensors: The Role of In-Situ Cross-Linking and Interface Engineering in Liquid Metal-Carbon Nanotube-PDMS Composites.

The increasing demand for high-performance strain sensors has driven the development of innovative composite systems. This study focused on enhancing the performance of composites by integrating liquid metal, carbon nanotubes, and polydimethylsiloxane (PDMS) in an innovative approach that involved advanced interface engineering, filler synergy, and in situ cross-linking of PDMS in solution. Surface modification of liquid metal with allyl disulfide and hydrogen-containing polydimethylsiloxane significantly improved its stability and dispersion within the polymer matrix. Through in situ cross-linking in solution and subsequent segment rearrangement after solvent evaporation, a continuous filler network was formed within the composite. The composites exhibited enhanced thermal stability, achieving a thermal conductivity of up to 2.13 W/(m·K) while simultaneously attaining a high electrical conductivity of 416 S/cm. The composite demonstrated excellent thermal management capabilities, alongside remarkable mechanical properties, including over 400% elongation at break and a low modulus of 0.587 MPa, even at high filler content. These attributes make the composite highly suitable for flexible strain sensor applications. Notably, the composite demonstrated outstanding strain sensing capabilities, effectively monitoring both human motion and handwriting. This work highlighted the critical roles of interface modification, filler interactions, and in situ cross-linking in achieving significant improvements in thermal, electrical, and sensing performance, thereby advancing the potential applications of flexible electronic materials.

High-Performance Flexible Strain Sensor Based on Thermoplastic Polyurethane Melt-Blown Nonwoven with Molybdenum Disulfide for Human Motion Monitoring

High-Performance Flexible Strain Sensor Based on Thermoplastic Polyurethane Melt-Blown Nonwoven with Molybdenum Disulfide for Human Motion Monitoring

High-Performance Stretchable Strain Sensors Based on Auxetic Fabrics for Human Motion Detection.

Wearable strain sensors play a pivotal role in real-time human motion detection and health monitoring. Traditional fabric-based strain sensors, typically with a positive Poisson's ratio, face challenges in maintaining sensitivity and comfort during human motion due to conflicting resistance changes in different strain directions. In this work, high-performance stretchable strain sensors are developed based on graphene-modified auxetic fabrics (GMAF) for human motion detection in smart wearable devices. The proposed GMAF sensors, with a negative Poisson's ratio achieved through commercially available warp-knitting technology, exhibit an 8-fold improvement in sensitivity compared to conventional plain fabric sensors. The unique auxetic fabric structure enhances sensitivity by synchronizing resistance changes in both wale and course directions. The GMAF sensors demonstrate excellent washability, showing only slight degradation in auxeticity and an acceptable increase in resistance after 10 standard wash cycles. The GMAF sensors maintain stability under different strain levels and various motion frequencies, emphasizing their dynamic performance. The sensors exhibit superior conformability to joint movements, which effectively monitor a full range of motions, including joint bending, sports activities, and subtle actions like coughing and swallowing. The research underscores a promising approach to achieve industrial-scale production of wearable sensors with improved performance and comfort through fabric structure design.

A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology.

A highly responsive and sensitive flexible strain sensor with conductive cross-linked network by laser direct writing

As a critical component of wearable electronic devices, flexible strain sensors have made significant contributions in the fields of smart healthcare, motion monitoring and communication, garnering escalating research interests. However, the pursuit of ideal materials synergizing high sensitivity with facile and cost-effective manufacturing methodologies remains a persistent challenge, significantly hindering the full realization of the potential of flexible strain sensors. Herein, an innovative hybrid conductive network architecture constituted by multi-walled carbon nanotubes and laser-induced graphene (MWCNTs/LIG) has been explored via a facile, cost-effective, controllable, and environmentally friendly laser direct writing (LDW) technique. Subsequently, the resultant MWCNTs/LIG composite with a cross-linked microstructure has been harnessed for the fabrication of a high-performance strain sensor. Notably, the proposed sensor exhibits remarkable sensitivity with a gauge factor (GF) reaching 192, rapid response and recovery times of 27/20 ms, and favorable durability exceeding 2300 cycles. Furthermore, the developed sensor demonstrates the capability to accurately capture full-range motions in real-time, as well as the ability to transmit information as a portable communication device. This promising discovery offers a new insight for the flourishing development of high-performance flexible strain sensors in the realm of wearable sensing.

Ultrasensitive, highly stretchable and multifunctional strain sensors based on scorpion-leg-inspired gradient crack arrays

Wearable flexible strain sensors based on conductive films have shown great application potential in many high-tech fields. They have high requirements for synergistic performances of sensitivity, flexibility, stability and durability. However, overcoming the trade-off between high sensitivity and high stretchability is still a great challenge in this research. Here we develop a facile strategy to prepare high-performance flexible strain sensors based on gradient crack arrays inspired by the gradient slits on scorpion legs. The gradient crack arrays with branched or hierarchical features controllably form in a periodic thickness-gradient metal film prepared by one-step masking technique on a flexible substrate. The cracks are progressively open from the thickest film region to the thinnest one under mechanical strain, behaving a unique sensing mechanism. As a result, the gradient crack-based strain sensors possess excellent comprehensive performances including high sensitivity (up to 21000), wide sensing range (∼70 %), low detection limit (0.02 %), quick response speed (below to 80 ms), excellent stability and durability (up to 15,000 cycles at 5 % strain). As demonstration of applications, the sensors are used to monitor various human activities and to express and encrypt information by bending bidirectionally. This work provides a novel way to fabricate highly sensitive, stretchable, robust, multifunctional, crack-based flexible strain sensors, which would greatly expand the applications in flexible electronics and wearable devices.

Fabrication of PHFPO Surface-Modified Conductive AgNWs/PNAGA Hydrogels with Enhanced Water Retention Capacity toward Highly Sensitive Strain Sensors.

Conductive hydrogels, characterized by their unique features of flexibility, biocompatibility, electrical conductivity, and responsiveness to environmental stimuli, have emerged as promising materials for sensitive strain sensors. In this study, a facile strategy to prepare highly conductive hydrogels is reported. Through rational structural and synthetic design, silver nanowires (AgNWs) are incorporated into poly(N-acryloyl glycinamide) (PNAGA) hydrogels, achieving high electrical conductivity (up to 0.88 S m-1), significantly enhanced mechanical properties, and elevated deformative sensitivity. Furthermore, surface modification with polyhexafluoropropylene oxide (PHFPO) has substantially improved the water retention capacity and dressing comfort of this hydrogel material. Based on the above merits, these hydrogels are employed to fabricate highly sensitive wearable strain sensors which can detect and interpret subtle hand and finger movements and enable precise control of machine interfaces. The AgNWs/PNAGA based strain sensors can effectively sense finger motion, enabling the control of robotic fingers to replicate the human hand's gestures. In addition, the high deformative sensitivity and elevated water retention performance of the hydrogels makes them suitable for flow sensing. These conceptual applications demonstrate the potential of this conductive hydrogel in high-performance strain sensors in the future.

Highly Robust and Self-Adhesive Soft Strain Gauge via Interface Design Engineering.

Highly robust soft strain gauges are rapidly emerging as a promising candidate in the fields of vital signs and machine conditions monitoring. However, it is still a key challenge to achieve high-performance strain sensing in these sensors with mechanical/electrical robustness for long-term usage. The multilayer structural design of sensors enhances sensing performance while the interfacial connection of heterogeneous materials between different layers is weak. Herein, inspired by the efficient perception mechanism of scorpion slit sensilla with tough interface interconnections, the synergy of ultra-high electrical performance and mechanical robustness is successfully achieved via interface design engineering. The developed multilayer soft strain gauge (MSSG) exhibits a strain sensitivity beyond 105, a lower detection limit of 8.3µm, a frequency resolution within 0.1Hz, and cyclic stability over 63000 strain cycles. Also, the tough interface improves the level of heterogeneous integration in the MSSG which allows to endure different stresses. Furthermore, an MSSG-based wireless strain monitoring system is developed that enables applications on different complex dynamic surfaces, including accurate identification of human throat activity and monitoring of rolling bearing conditions.

High-Performance Multidirectional Flexible Strain Sensor for Human Motion and Health Monitoring.

Multidirectional strain sensors are pivotal for wearable electronic devices and human-computer interaction. In this investigation, we translocate carbon/graphene (CB/Gr) conductive nanocomposites onto an Ecoflex flexible substrate via a facile technique encompassing reverse molding and spraying, culminating in the fabrication of a 45° strain rosette-shaped multidirectional flexible strain sensor. The sensor distinguishes itself with extraordinary performance characteristics, including high sensitivity (boasting a gauge factor of 35), an extensive strain range from 0 to 100%, exceptional linearity, a rapid response time of merely 200 ms, remarkable stability, and outstanding durability, effortlessly withstanding over 5000 stretch-release cycles. The sensor exhibits its exceptional capability to discern intricate movements, particularly in detecting human hand and neck motions. The sensor's remarkable comprehensive performance and strain direction recognition ability underscore its significant potential for diverse applications, notably in human-computer interaction, human motion monitoring, and health monitoring.

Sensitive strain sensors with wide-range detection and high stability for intelligent human motion monitoring

Miniaturized wearable electronic devices with strain sensors as core sensing elements hold significant applications in health monitoring and human-machine interaction. However, it remains a challenge to develop strain sensors with optimal response linearity, a wide detection range, and stability. In this study, to satisfy the above requirements, we construct high-performance wearable strain sensors (rGO-CNTOH@PSE) by a strategy of hybridizing conductive fillers with stretchable substrates. In detail, a synergistic conductive network is developed by adopting OH-containing carbon nanotube (CNTOH) and reduced graphene oxide (rGO) fillers with an appropriate mass ratio. After the network is integrated into a porous silicone elastomer substrate formed by the salt templating method, the developed strain sensor exhibits distinct resolution, consistent repeatability, and superior response linearity across a wide strain detection range of 0.3–303.03 %. Furthermore, the sensor also maintains good response stability over 15000 cycles of strain. We also demonstrate that this strain sensor showing outstanding exceptional detection performance has the capability for full-range human motion detection. A wireless smart electronic glove is developed based on the sensor, which can accurately recognize gesture movements and control a bionic robotic hand to replicate human hand actions, offering the potential for replacing manual tasks (such as, grasping in hazardous environments) in the future.

Construction of “island-bridge” microstructured conductive coating for enhanced impedance response of organohydrogel strain sensor

Flexible conductive hydrogels (CHs) have received widespread attention in the field of wearable strain sensor, electronic skin, and artificial intelligence owing to their excellent stretchability, biocompatibility, and real-time sensing performance. However, it still remains a critical challenge to design and construct the satisfactory wearable strain sensor integrated with high sensitivity, wide sensing range, and environmental stability, especially at extreme temperatures. Herein, ionic conductive polyacrylamide/sodium alginate nanocomposite organohydrogel anchored with electronic conductive “island-bridge” microstructured carbon nanotubes (CNTs) conformal coating was designed and fabricated for high-performance strain sensor, of which the existence of “island-bridge” microstructured electronic conductive layer and its special structure evolution upon external stretching can significantly enhance the alternating current impedance response behavior of the sensor, featuring with a high sensitivity of 76.54 at 600 % strain, wide sensing range (0–600 % strain), fast responsiveness (110 ms), extremely low detection limit (0.1 %), and favorable stability and reproducibility of over 3200 cycles. Meanwhile, the resultant organohydrogel strain sensor can precisely detect and differentiate complicated human activities even at the environment temperatures of −30 °C and 50 °C, opening a promising avenue for next-generation smart wearable electronics.

Design of flexible micro-porous fiber with double conductive network synergy for high-performance strain sensor

Flexible wearable strain sensor is a vital part of intelligent wearable systems due to their broad applications in human–machine interaction and smart clothes. Flexible strain sensor fabricated via conventional blending conductive polymer composites has some drawbacks, for example, low sensitivity, large hysteresis, and “shoulder-peak” phenomenon, which limit their practical applications greatly. Herein, we prepared a fiber-shaped strain sensor with a micro-porous, three-layer structure together with a dual conductive network based on reduced graphene oxide (rGO) and multi-walled carbon nanotubes (MWCNTs). The strain sensor demonstrates excellent response behaviors, including splendid stretchability (686 %), wide detection range (120 %), high sensitivity (gauge factor = 300), fast response time (300 ms) and excellent durability (over 9000 cycles). The unique dual conductive network structure endows the strain sensor with remarkable recoverability and reproducibility. The resistance of the strain sensor can recover to nearly initial conditions after the releasing process, exhibiting lower hysteresis. The “shoulder-peak” phenomenon has also been weakened based on the microstructure and conductive network design. Based on the high sensitivity and excellent response behavior, the fiber-shaped strain sensor performs real-time monitoring of breathing, finger bending, walking, and other human motions, providing a good candidate for its application in intelligent wearable devices.

Biodegradable poly(lactic acid) blocked polyurethane/carbon nanotubes coated cotton fabric prepared by ultrasonic-assisted inkjet printing for high performance strain sensors

In order to fulfill the demands for degradability, a broad working range, and heightened sensitivity in flexible sensors, biodegradable polyurethane (BTPU) was synthesized and combined with CNTs to produce BTPU/CNTs coated cotton fabric using an ultrasonic-assisted inkjet printing process. The synthesized BTPU displayed a capacity for degradation in a phosphate buffered saline solution, resulting in a weight loss of 25 % after 12 weeks of degradation. The BTPU/CNTs coated cotton fabric sensor achieved an extensive strain sensing range of 0–137.5 %, characterized by high linearity and a notable sensitivity (gauge factor (GF) of 126.8). Notably, it demonstrated a low strain detection limit (1 %), rapid response (within 280 ms), and robust durability, enabling precise monitoring of both large and subtle human body movements such as finger, wrist, neck, and knee bending, as well as swallowing. Moreover, the BTPU/CNTs coated cotton fabric exhibited favorable biocompatibility with human epidermis, enabling potential applications as wearable skin-contact sensors. This work provides insight into the development of degradable and high sensing performance sensors suitable for applications in electronic skins and health monitoring devices.

Tunable porous fiber-shaped strain sensor with synergistic conductive network for human motion recognition and tactile sensing

With the rapid development of portable electronic products, smart wearable devices show great application potential in personalized motion monitoring, health monitoring and even in human-machine interaction. In particular, high-performance flexible fiber-shaped strain sensors are receiving more and more attention because they can be well integrated with clothes or human skin for real-time human activities monitoring. However, achieving improved sensitivity, wide detection range, diverse applications and easy operation of them remains a challenge. Here, we adopt a simple wet-spinning technique to prepare a porous fiber-shaped carbon nanotubes (CNTs)/silver nanoparticles (AgNPs)/thermoplastic polyurethane (TPU) fiber (CATF) for high-performance strain sensor, of which the synergistic conductive network of CNTs and AgNPs enables a wide detection range (∼248 %), high sensitivity (GF > 4295), fast response/recovery time (120 ms/140 ms) and excellent stability. Interestingly, strain sensing range and sensitivity of the sensor can be tuned by changing the AgNPs loading to make it suitable for different application scenarios. All the merits of the sensor make it capable for diverse human movements monitoring, pronunciation recognition, and gesture recognition. More importantly, our prepared CATF strain sensor can be easily weaved into wearable smart textiles, showing great potential for tactile sensing to realize multi-touch and pressure tracking. Finally, the CATF also displays efficient photothermal conversion capability even under different tensile strains up to 150 %, showing great potential in human body thermal management to improve the wearing comfort of wearable electronic fabric.