Laser-Induced Corrugated Graphene Films for Integrated Multimodal Sensors

Multimodal Flexible Sensor for the Detection of Pressing-Bending-Twisting Mechanical Deformations

Flexible sensors are increasingly significant in applications such as smart wearables and human-computer interactions. However, typical flexible sensors are spatially limited and can generally detect only one deformation mode. This study presents a novel multimodal flexible sensor that combines three sensing units: optoelectronics, ionic liquids, and conductive fabrics. It employs a sophisticated superposition and combination of the three sensing methods to achieve up to eight mechanical deformations, including pressing, bending, twisting, and combinations thereof, all within a very small sensor space. This sensor has excellent detection performance, high sensitivity (optoelectronics 4.312, ionic liquid 8.186, conductive fabric 2.438), a wide measurement range (pressing 0-75 kPa, bending 0-90°, and twisting 0-180°), and good consistency and repeatability. To address the signal coupling problem in multimode sensors, a deep learning method based on the Transformer is combined to provide precise decoupling of multimode signals and high-precision characterization of each mechanical deformation. Finally, the wrist joint experiments demonstrate the sensor's versatile uses in human-computer interaction.

A Prestrained Strategy Enabled Highly Sensitive Strain Sensors for Muscle Strength Assessment

Achieving high sensitivity of stretchable electronics with a wide working range is essential and challenging. While fundamental strategies using topographic design or introducing microstructures (e.g., wrinkles or cracks) can effectively improve the sensitivity, the strain-response range is still rather limited. Here, we propose a tunable and ultrasensitive piezoresistive strain sensor by leveraging a controllable kirigami design and a prestrained strategy. On the one hand, the kirigami structure enhances the sensitivity and structural stability. On the other hand, the prestrained strategy widens the surface crack to generate highly sensitive and continuous linear responses. This strategy allows general stretchable materials to versatilely realize strain sensors with ultrahigh sensitivity (GF > 1000) and good linearity in the low strain range. As a result, the increased sensitivity and conformability to soft surfaces enable the prestrained strain sensor to identify different body motions and hand grips of varying loads. The high amplitude and recognizable signal waveforms provide essential information in muscle strength assessment. With its general applicability to diverse soft materials, this prestrained strategy overcomes the material and manufactural-level limitations for imparting high sensitivity to various strain sensors, presenting a great potential in the fields of medical monitoring and rehabilitation.

Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

Toward Integrated Multifunctional Laser-Induced Graphene-Based Skin-Like Flexible Sensor Systems

The burgeoning demands for health care and human-machine interfaces call for the next generation of multifunctional integrated sensor systems with facile fabrication processes and reliable performances. Laser-induced graphene (LIG) with highly tunable physical and chemical characteristics plays vital roles in developing versatile skin-like flexible or stretchable sensor systems. This Progress Report presents an in-depth overview of the latest advances in LIG-based techniques in the applications of flexible sensors. First, the merits of the LIG technique are highlighted especially as the building blocks for flexible sensors, followed by the description of various fabrication methods of LIG and its variants. Then, the focus is moved to diverse LIG-based flexible sensors, including physical sensors, chemical sensors, and electrophysiological sensors. Mechanisms and advantages of LIG in these scenarios are described in detail. Furthermore, various representative paradigms of integrated LIG-based sensor systems are presented to show the capabilities of LIG technique for multipurpose applications. The signal cross-talk issues are discussed with possible strategies. The LIG technology with versatile functionalities coupled with other fabrication strategies will enable high-performance integrated sensor systems for next-generation skin electronics.

Laser patterned graphene pressure sensor with adjustable sensitivity in an ultrawide response range

Flexible pressure sensors have attracted wide attention because of their applications in wearable electronic, human-computer interface, and healthcare. However, it is still a challenge to design a pressure sensor with adjustable sensitivity in an ultrawide response range to satisfy the requirements of different application scenarios. Here, a laser patterned graphene pressure sensor (LPGPS) is proposed with adjustable sensitivity in an ultrawide response range based on the pre-stretched kirigami structure. Due to the out-of-plane deformation of the pre-stretched kirigami structure, the sensitivity can be easily tuned by simply modifying the pre-stretched level. As a result, it exhibits a maximum sensitivity of 0.243 kPa-1, an ultrawide range up to 1600 kPa, a low detection limit (6 Pa), a short response time (42 ms), and excellent stability with high pressure of 1200 kPa over 500 cycles. Benefiting from its high sensitivity and ultrawide response range, the proposed sensor can be applied to detect physiological and kinematic signals under different pressure intensities. Additionally, taking advantage of laser programmable patterning, it can be easily configured into an array to determine the pressure distribution. Therefore, LPGPS with adjustable sensitivity in an ultrawide response range has potential application in wearable electronic devices.

Laser Direct Writing of Flexible Thermal Flow Sensors

Thin film-based thermal flow sensors afford applications in healthcare and industries owing to their merits in preserving initial flow distributions. However, traditional thermal flow sensors are primarily applied to track flow intensities based on hot-wire or hot-film sensing mechanisms due to their relatively facile device configurations and fabrication strategies. Herein, a calorimetric thermal flow sensor is proposed based on laser direct writing to form laser-induced graphene as heaters and temperature sensors, resulting in monitoring both flow intensities and orientations. Via homogeneously surrounding spiral heaters with multiple temperature sensors, the device exhibits high sensitivity (∼162 K·s/m) at small flows with an extended flow detection range (∼25 m/s). Integrating the device with a data-acquisition board and a dual-mode graphical user interface enables wirelessly and dynamically monitoring respiration and the motion of robotic arms. This versatile flow sensor with facile manufacturing affords potentials in health inspection, remote monitoring, and studying hydrodynamics.

Templated Laser-Induced-Graphene-Based Tactile Sensors Enable Wearable Health Monitoring and Texture Recognition via Deep Neural Network

Flexible tactile sensors show great potential for portable healthcare and environmental monitoring applications. However, challenges persist in scaling up the manufacturing of stable tactile sensors with real-time feedback. This work demonstrates a robust approach to fabricating templated laser-induced graphene (TLIG)-based tactile sensors via laser scribing, elastomer hot-pressing transfer, and 3D printing of the Ag electrode. With different mesh sandpapers as templates, TLIG sensors with adjustable sensing properties were achieved. The tactile sensor obtains excellent sensitivity (52260.2 kPa-1 at a range of 0-7 kPa), a broad detection range (up to 1000 kPa), a low limit of detection (65 Pa), a rapid response (response/recovery time of 12/46 ms), and excellent working stability (10000 cycles). Benefiting from TLIG's high performance and waterproofness, TLIG sensors can be used as health monitors and even in underwater scenarios. TLIG sensors can also be integrated into arrays acting as receptors of the soft robotic gripper. Furthermore, a deep neural network based on the convolutional neural network was employed for texture recognition via a soft TLIG tactile sensing array, achieving an overall classification rate of 94.51% on objects with varying surface roughness, thus offering high accuracy in real-time practical scenarios.

Textile Electronics with Laser-Induced Graphene/Polymer Hybrid Fibers

The concept of wearables is rapidly evolving from flexible polymer-based devices to textile electronics. The reason for this shift is the ability of textiles to ensure close contact with the skin, resulting in comfortable, lightweight, and compact "always with you" sensors. We are contributing to this polymer-textile transition by introducing a novel and simple way of laser intermixing of graphene with synthetic fabrics to create wearable sensing platforms. Our hybrid materials exhibit high electrical conductivity (87.6 ± 36.2 Ω/sq) due to the laser reduction of graphene oxide and simultaneous laser-induced graphene formation on the surface of textiles. Furthermore, the composite created between graphene and nylon ensures the durability of our materials against sonication and washing with detergents. Both of these factors are essential for real-life applications, but what is especially useful is that our free-form composites could be used as-fabricated without encapsulation, which is typically required for conventional laser-scribed materials. We demonstrate the exceptional versatility of our new hybrid textiles by successfully recording muscle activity, heartbeat, and voice. We also show a gesture sensor and an electrothermal heater embedded within a single commercial glove. Additionally, the use of these textiles could be extended to personal protection equipment and smart clothes. We achieve this by implementing self-sterilization with light and laser-induced functionalization with silver nanoparticles, which results in multifunctional antibacterial textiles. Moreover, incorporating silver into such fabrics enables their use as surface-enhanced Raman spectroscopy sensors, allowing for the direct analysis of drugs and sweat components on the clothing itself. Our research offers valuable insights into simple and scalable processes of textile-based electronics, opening up new possibilities for paradigms like the Internet of Medical Things.

Smart Multiplex Point-of-Care Platform for Simultaneous Drug Monitoring

Recently, illicit drug use has become more widespread and is linked to problems with crime and public health. These drugs disrupt consciousness, affecting perceptions and feelings. Combining stimulants and depressants to suppress the effect of drugs has become the most common reason for drug overdose deaths. On-site platforms for illicit-drug detection have gained an important role in dealing, without any excess equipment, long process, and training, with drug abuse and drug trafficking. Consequently, the development of rapid, sensitive, noninvasive, and reliable multiplex drug-detecting platforms has become a major necessity. In this study, a multiplex laser-scribed graphene (LSG) sensing platform with one counter, one reference, and three working electrodes was developed for rapid and sensitive electrochemical detection of amphetamine (AMP), cocaine (COC), and benzodiazepine (BZD) simultaneously in saliva samples. The multidetection sensing system was combined with a custom-made potentiostat to achieve a complete point-of-care (POC) platform. Smartphone integration was achieved by a customized application to operate, display, and send data. To the best of our knowledge, this is the first multiplex LSG-based electrochemical platform designed for illicit-drug detection with a custom-made potentiostat device to build a complete POC platform. Each working electrode was optimized with standard solutions of AMP, COC, and BZD in the concentration range of 1.0 pg/mL-500 ng/mL. The detection limit of each illicit drug was calculated as 4.3 ng/mL for AMP, 9.7 ng/mL for BZD, and 9.0 ng/mL for COC. Healthy and MET (methamphetamine) patient saliva samples were used for the clinical study. The multiplex LSG sensor was able to detect target analytes in real saliva samples successfully. This multiplex detection device serves the role of a practical and affordable alternative to conventional drug-detection methods by combining multiple drug detections in one portable platform.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Self-Patterning of Highly Stretchable and Electrically Conductive Liquid Metal Conductors by Direct-Write Super-Hydrophilic Laser-Induced Graphene and Electroless Copper Plating

Stretchable electrodes are desirable in flexible electronics for the transmission and acquisition of electrical signals, but their fabrication process remains challenging. Herein, we report an approach based on patterned liquid metals (LMs) as stretchable electrodes using a super-hydrophilic laser-induced graphene (SHL-LIG) process with electroless plating copper on a polyimide (PI) film. The LMs/SHL-LIG structures are then transferred from the PI film to an Ecoflex substrate as stretchable electrodes with an ultralow sheet resistance of 3.54 mΩ per square and excellent stretchability up to 480% in elongation. Furthermore, these electrodes show outstanding performances of only 8% electrical resistance changes under a tensile strain of 300%, and strong immunity to temperature and pressure changes. As demonstration examples, these electrodes are integrated with a stretchable strain sensing system and a smart magnetic soft robot toward practical applications.

3D printing of bone and cartilage with polymer materials

Damage and degeneration to bone and articular cartilage are the leading causes of musculoskeletal disability. Commonly used clinical and surgical methods include autologous/allogeneic bone and cartilage transplantation, vascularized bone transplantation, autologous chondrocyte implantation, mosaicplasty, and joint replacement. 3D bio printing technology to construct implants by layer-by-layer printing of biological materials, living cells, and other biologically active substances in vitro, which is expected to replace the repair mentioned above methods. Researchers use cells and biomedical materials as discrete materials. 3D bio printing has largely solved the problem of insufficient organ donors with the ability to prepare different organs and tissue structures. This paper mainly discusses the application of polymer materials, bio printing cell selection, and its application in bone and cartilage repair.

Recent Advances on High-Performance Polyaryletherketone Materials for Additive Manufacturing

Polyaryletherketone (PAEK) is emerging as an important high-performance polymer material in additive manufacturing (AM) benefiting from its excellent mechanical properties, good biocompatibility, and high-temperature stability. The distinct advantages of AM facilitate the rapid development of PAEK products with complex customized structures and functionalities, thereby enhancing their applications in various fields. Herein, the recent advances on AM of high-performance PAEKs are comprehensively reviewed, concerning the materials properties, AM processes, mechanical properties, and potential applications of additively manufactured PAEKs. To begin, an introduction to fundamentals of AM and PAEKs, as well as the advantages of AM of PAEKs is provided. Discussions are then presented on the material properties, AM processes, processing-matter coupling mechanism, thermal conductivity, crystallization characteristics, and microstructures of AM-processed PAEKs. Thereafter, the mechanical properties and anisotropy of additively manufactured PAEKs are discussed in depth. Their representative applications in biomedical, aerospace, electronics, and other fields are systematically presented. Finally, current challenges and possible solutions are discussed for the future development of high-performance AM polymers.

Research Progress on the Preparation and Applications of Laser-Induced Graphene Technology

Graphene has been regarded as a potential application material in the field of new energy conversion and storage because of its unique two-dimensional structure and excellent physical and chemical properties. However, traditional graphene preparation methods are complicated in-process and difficult to form patterned structures. In recent years, laser-induced graphene (LIG) technology has received a large amount of attention from scholars and has a wide range of applications in supercapacitors, batteries, sensors, air filters, water treatment, etc. In this paper, we summarized a variety of preparation methods for graphene. The effects of laser processing parameters, laser type, precursor materials, and process atmosphere on the properties of the prepared LIG were reviewed. Then, two strategies for large-scale production of LIG were briefly described. We also discussed the wide applications of LIG in the fields of signal sensing, environmental protection, and energy storage. Finally, we briefly outlined the future trends of this research direction.