Flexible Dual-Mechanism Pressure Sensor Based on Ag Nanowire Electrodes for Nondestructive Grading and Quality Monitoring of Fruits

Abstract

To extend the application range of flexible pressure sensors and to be widely used as an economical and convenient way, researchers have made efforts toward high sensitivity, wide detection range, and low power consumption. However, most sensors can only work based on a single mechanism, thus limiting the sensing range and applicability. In view of this, we developed a dual-mechanism pressure sensor (DMPS) based on Ag nanowire/PDMS electrodes and ZnO nanoparticle-based force-sensitive membranes, which has a sandwich structure and complete flexibility under the effect of chemical bonding. It can simultaneously sense high-frequency dynamic pressure as well as static load by integrating piezoelectric and piezocapacitive effects, thereby complementing the inability of conventional piezoelectric-type sensors to continuously detect static pressure. The prepared flexible DMPS containing 20 wt % ZnO nanofiller has a pressure frequency-independent electrical output of about 2 V under 20 N force conditions and also has a long-term stability of more than 5000 pressure cycles. Moreover, it exhibits good linearity, repeatability, and stability in the 0–100 kPa detection range in the piezocapacitive mode. On the basis of multimechanism and modularity, the flexible DMPS was demonstrated for fruit quality monitoring, including avocado ripeness grading, tactile sensory gloves, and moisture loss assessment, indicating its promising applications in intelligent agricultural machines and wearable sensing systems.

Highly Sensitive and Flexible Capacitive Pressure Sensors Based on Vertical Graphene and Micro-Pyramidal Dielectric Layer

“…The inset in Figure 3 f shows that the single peaks maintained the same shape and intensity, confirming the high repeatability and durability of the sensor. Comparing the VG1-P(20-20) sensor with the reported sensors, as listed in Table S6 [ 17 , 21 , 56 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ], the former exhibits superior sensing performance.…”

“…Table S6: Comparison of the performances of the VG based sensor with other pressure sensors in the literature. References [ 17 , 21 , 56 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] are cited in the supplementary materials.…”

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

“…Lastly, the scalability and cost effectiveness of the device are also a topic worth exploring. , The CSP is based on a simple laser engraving and heat treatment technology for forming and costs less than $1 per piece. As an application extension, regression prediction of fish shelf life was performed by using a back-propagation neural network (BPNN) model.…”

Intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) for kiwifruit ripeness classification

“…A manipulator with tactile perception can judge the firmness in orchard, fruit production line and other scenes. 27,28 The data sensed by the tactile sensor is combined with a machine learning algorithm to establish the best gripping prediction model and target classification model, which can play a satisfactory role in the study of gripping the tactile manipulator. 29 Li et al predicted the stability through pressure distribution feedback, and combined with support vector machine (SVM), the accuracy reached 98% 30 ; Zhang et al integrated a manipulator with a tactile sensor to identify the firmness through perception, and combined with principal component analysis-support vector machine (PCA-SVM) classifier, the online recognition rate was 90% 31 ; Song et al identify the firmness and type of objects through perception, and combined with RN (Res Net), the accuracy of firmness and type identification can reach 100 and 99.7% respectively.…”

“…A manipulator with tactile perception can judge the firmness in orchard, fruit production line and other scenes 27,28 . The data sensed by the tactile sensor is combined with a machine learning algorithm to establish the best gripping prediction model and target classification model, which can play a satisfactory role in the study of gripping the tactile manipulator 29 .…”