Understanding the Cycling Performance Degradation Mechanism of a Graphene-Based Strain Sensor and an Effective Corresponding Improvement Solution

Ultrasensitive Flexible Strain Sensor Made with Carboxymethyl-Cellulose-Anchored Carbon Nanotubes/MXene for Machine-Learning-Assisted Handwriting Recognition

The combination of wearable sensors with machine learning enables intelligent perception in human-machine interaction and healthcare, but achieving high sensitivity and a wide working range in flexible strain sensors for signal acquisition and accurate recognition remains challenging. Herein, we introduced carboxymethyl cellulose (CMC) into a carbon nanotubes (CNTs)/MXene hybrid network, forming tight anchoring among the conductive materials and, thus, bringing enhanced interaction. The silicone-rubber-encapsulated CMC-anchored CNTs/MXene (CCM) strain sensor exhibits an excellent sensitivity (maximum gauge factor up to 71 294), wide working range (200%), ultralow detection limit (0.05%), and outstanding durability (over 10 000 cycles), which is superior to most of the recently reported counterparts also based on a conductive composite film. Moreover, the sensor achieves seamless integration with human skin with the help of a poly(acrylic acid) adhesive layer, successfully obtaining stable and clear waveforms with meaningful profiles from the human body. On this basis, we proposed and realized a novel in-air handwriting recognition method via extracting multiple features of high-quality strain signals assisted by deep neural networks, achieving a high classification accuracy of 98.00 and 94.85% for Arabic numerals and letters, respectively. Our work provides an effective approach for significantly improving strain sensing performance, thereby facilitating innovative applications of flexible sensors.

Spray-lithography of hybrid graphene-perovskite paper-based photodetectors for sustainable electronics

Paper is an ideal substrate for the development of flexible and environmentally sustainable ubiquitous electronic systems. When combined with nanomaterial-based devices, it can be harnessed for various Internet-of-Things applications, ranging from wearable electronics to smart packaging. However, paper remains a challenging substrate for electronics due to its rough and porous nature. In addition, the absence of established fabrication methods is impeding its utilization in wearable applications. Unlike other paper-based electronics with added layers, in this study, we present a scalable spray-lithography on a commercial paper substrate. We present a non-vacuum spray-lithography of chemical vapor deposition (CVD) single-layer graphene (SLG), carbon nanotubes (CNTs) and perovskite quantum dots (QDs) on a paper substrate. This approach combines the advantages of two large-area techniques: CVD and spray-coating. The first technique allows for the growth of SLG, while the second enables the spray coating of a mask to pattern CVD SLG, electrodes (CNTs), and photoactive (QDs) layers. We harness the advantages of perovskite QDs in photodetection, leveraging their strong absorption coefficients. Integrating them with the graphene enhances the photoconductive gain mechanism, leading to high external responsivity. The presented device shows high external responsivity of ∼520 A W-1at 405 nm at <1 V bias due to the photoconductive gain mechanism. The prepared paper-based photodetectors (PDs) achieve an external responsivity of 520 A W-1under 405 nm illumination at <1 V operating voltage. To the best of our knowledge, our devices have the highest external responsivity among paper-based PDs. By fabricating arrays of PDs on a paper substrate in the air, this work highlights the potential of this scalable approach for enabling ubiquitous electronics on paper.

Performance Deficiency Improvement of CNT-Based Strain Sensors by Magnetic-Induced Patterning

As one of the most promising candidates, ubiquitous cycling degradation seriously affects the accuracy of carbon nanotube (CNT)-based sensors, and the reason for which is still unclear. Herein, the cycling degradation mechanism of CNT-based strain sensors has been detected by comparatively investigating the difference between the sensing behavior of CNT- and silver nanowire (Ag-NW)-based sensors, from which the microcrack-disconnection and unfolding-tunneling effects have been clarified as the sensing mechanism for Ag-NWs and CNT-based strain sensors, respectively. Furthermore, sliding and unfolding behaviors resulting from the weak interaction between CNTs have been proven to cause degradation. Correspondingly, a creative magnetically induced patterning method is proposed by utilizing magnetic nanoparticles as obstacles to prevent the CNTs from relative sliding. Benefiting from the advantageous factor, the performance deficiency of the CNT-based sensor has been overcome, and the sensitivity was significantly improved up to 5.2 times with accurate human activity detection. The competitive sensing performance of the CNTs demonstrates the reference value of the deficiency mechanism and solution scheme obtained in this study.

Interface-engineered reduced graphene oxide assembly on nanofiber surface for high performance strain and temperature sensing

Conductive polymer nanofiber composites (CPNCs) based wearable sensing electronics have aroused great attention of scientists in recent years. However, it is still difficult to obtain CPNCs with good water proof, excellent durability, and multiple sensing performance. Herein, we develop a multifunctional CPNC with a wrinkled reduced graphene oxide (RGO) shell and polymer nanofiber core, which is prepared by ultrasonication induced decoration of RGO onto the pre-stretched polyurethane (PU) nanofibers, followed by the release of the strain. The RGO assembly with a wrinkled structure not only greatly increases the surface roughness and thus the hydrophobicity but also enhances the strain sensing sensitivity (with a gauge factor of 154.8 in the strain range of 85%-100%) of the nanofibrous membrane. The obtained CPNC strain sensor also shows excellent sensing durability (over 1000 cycles) and can be used for body motion monitoring. The CPNC shows a negative temperature coefficient effect, which holds promising applications in high performance temperature sensors.

Srivastava S. Wearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors

Wearable technology and mobile healthcare systems are both increasingly popular solutions to traditional healthcare due to their ease of implementation and cost-effectiveness for remote health monitoring. Recent advances in research, especially the miniaturization of sensors, have significantly contributed to commercializing the wearable technology. Most of the traditional commercially available sensors are either mechanical or optical, but nowadays transdermal microneedles are also being used for micro-sensing such as continuous glucose monitoring. However, there remain certain challenges that need to be addressed before the possibility of large-scale deployment. The biggest challenge faced by all these wearable sensors is our skin, which has an inherent property to resist and protect the body from the outside world. On the other hand, biosensing is not possible without overcoming this resistance. Consequently, understanding the skin structure and its response to different types of sensing is necessary to remove the scientific barriers that are hindering our ability to design more efficient and robust skin sensors. In this article, we review research reports related to three different biosensing modalities that are commonly used along with the challenges faced in their implementation for detection. We believe this review will be of significant use to researchers looking to solve existing problems within the ongoing research in wearable sensors.