No abstract

One-dimensional and two-dimensional materials are widely used to compose the conductive network atop soft substrate to form flexible strain sensors for several wearable electronic applications. However, limited contact area and layer misplacement hinder the rapid development of flexible strain sensors based on 1D or 2D materials. To overcome these drawbacks above, we proposed a hybrid strategy by combining 1D carbon nanotubes (CNTs) and 2D graphene nanoplatelets (GNPs), and the developed strain sensor based on CNT-GNP hierarchical networks showed remarkable sensitivity and tenability. The strain sensor can be stretched in excess of 50% of its original length, showing high sensitivity (gauge factor 197 at 10% strain) and tenability (recoverable after 50% strain) due to the enhanced resistive behavior upon stretching. Moreover, the GNP-CNT hybrid thin film shows highly reproducible response for more than 1000 loading cycles, exhibiting long-term durability, which could be attributed to the GNPs conductive networks significantly strengthened by the hybridization with CNTs. Human activities such as finger bending and throat swallowing were monitored by the GNP-CNT thin film strain sensor, indicating that the stretchable sensor could lead to promising applications in wearable devices for human motion monitoring.

We demonstrate an ultra-sensitive photodetector based on a graphene/monolayer MoS2 vertical heterostructure working at room temperature.

Laser heating provides an effective method to produce thermally reduced graphene oxide (rGO), it can also pattern the designed layout on the surface of graphene oxide (GO) during the reduction process. In this work, we demonstrated a flexible strain sensor based on the morphologically modulated laser-patterned reduced graphene oxide (LPG) film with a one-step process. Compared with the strain sensor using flat patterned rGO (0%-1.2%) and curved-grid patterned rGO (CGPG) (0%-4.1%), the strain sensor based on rectangular-grid patterned rGO (RGPG) have highest gauge factor (GF), up to 133 under 2.7% of physical deformation. Meanwhile, the RGPG strain sensors exhibit extraordinary linearity in a relatively large range of deformation (0%-2.7%) and excellent endurance for over 1000 stretching-releasing circles. The RGPG strain sensor was used to monitor human fatigue. By analyzing eye blinking frequency and duration, it is possible to evaluate the fatigue level. We anticipate that the RGPG based strain sensor, prepared via a relatively simple and cost-effective process, may open up a broad spectrum of practical applications, such as driver fatigue evaluation and smart monitoring of human body movements.