As a new material, graphene shows excellent properties in mechanics, electricity, optics, and so on, which makes it widely concerned by people. At present, it is difficult for graphene pressure sensor to meet both high sensitivity and large pressure detection range at the same time. Therefore, it is highly desirable to produce flexible pressure sensors with sufficient sensitivity in a wide working range and with simple process. Herein, a relatively high flexible pressure sensor based on piezoresistivity is presented by combining the conical microstructure polydimethylsiloxane (PDMS) with bilayer graphene together. The piezoresistive material (bilayer graphene) attached to the flexible substrate can convert the local deformation caused by the vertical force into the change of resistance. Results show that the pressure sensor based on conical microstructure PDMS-bilayer graphene can operate at a pressure range of 20 kPa while maintaining a sensitivity of 0.122 ± 0.002 kPa⁻¹ (0–5 kPa) and 0.077 ± 0.002 kPa⁻¹ (5–20 kPa), respectively. The response time of the sensor is about 70 ms. In addition to the high sensitivity of the pressure sensor, it also has excellent reproducibility at different pressure and temperature. The pressure sensor based on conical microstructure PDMS-bilayer graphene can sense the motion of joint well when the index finger is bent, which makes it possible to be applied in electronic skin, flexible electronic devices, and other fields.

With advances in new materials and technologies, there has been increasing research focused on flexible sensors. However, in most flexible pressure sensors made using new materials, it is challenging to achieve high detection sensitivity across a wide pressure range. Although traditional silicon-based sensors have good performance, they are not formable and, because of their rigidity and brittleness, they are not suitable for fitting with soft human skin, which limits their application in wearable devices to collect various signals. Silicon nanomembranes are ultra-thin, flexible materials with excellent piezoresistive properties, and they can be applied in various fields, such as in soft robots and flexible devices. In this study, we developed a flexible pressure sensor based on the use of silicon nanomembranes (with a thickness of only 340 nm) as piezoresistive units, which were transferred onto a flexible polydimethylsiloxane (PDMS) substrate. The flexible pressure sensor operated normally in the range of 0–200 kPa, and the sensitivity of the sensor reached 0.0185 kPa⁻¹ in the low-pressure range of 0–5 kPa. In the high-pressure range of 5–200 kPa, the sensitivity of the sensor was maintained at 0.0023 kPa⁻¹. The proposed sensor exhibited a fast response and excellent long-term stability and could recognize human movements, such as the bending of fingers and wrist joints, while maintaining a stable output. Thus, the developed flexible pressure sensor has promising applications in body monitoring and wearable devices.

In dynamic temperature measurement, thermocouple sensors are widely used, and their dynamic characteristics directly affect the accuracy of the test results. So before applying thermocouple sensors to dynamic temperature measurement, their dynamic characteristics should be obtained, and their dynamic performance parameters should be analyzed. The time constant is the most important dynamic parameter, which reflects the response speed of a thermocouple sensor. Therefore, it is necessary to measure the time constant. The time constant is closely related to the heat transfer mode, so the heat transfer environment of the time constant measurement system should be similar to the application environment of thermocouple sensor. When using the thermocouple to measure the temperature in various kilns, the heat transfer is mainly through the radiative mode, and existing equipment, such as constant temperature water/oil tank, shock wave tube and other devices, cannot be used to measure the time constant to reflect scenarios in actual measurement applications. Therefore, this paper proposed a new method to measure the time constant of the thermocouple by improved high temperature furnace. In the system, the high temperature furnace was used to generate the stable temperature field, and the fast feed device was used to insert the thermocouple into the high temperature furnace and generates the ramp temperature excitation. The temperature can reach 1500 °C in the furnace, and the temperature error in uniform temperature field is ±1 °C. Finally, the time constant of a K-type thermocouple was measured, and the uncertainty of the measurement result was analyzed.

Wind energy as a renewable energy source is easily available and widely distributed in cities. However, current wind-energy harvesters are inadequate at capturing energy from low-speed winds in urban areas, thereby limiting their application in distributed self-powered sensor networks. A triboelectric–electromagnetic hybrid harvester with a low startup wind speed (LSWS-TEH) is proposed that also provides output power within a wide range of wind speeds. An engineering-implementable propeller design method is developed to reduce the startup wind speed of the harvester. A mechanical analysis of the aerodynamics of the rotating propeller is performed, and optimal propeller parameter settings are found that greatly improved its aerodynamic torque. By combining the high-voltage output of the triboelectric nanogenerator under low-speed winds with the high-power output of the electromagnetic generator under high-speed winds, the harvester can maintain direct current output over a wide wind-speed range after rectification. Experiments show that the harvester activates at wind speeds as low as 1.2 m/s, powers a sensor with multiple integrated components in 1.7 m/s wind speeds, and drives a Bluetooth temperature and humidity sensor in 2.7 m/s wind speeds. The proposed small, effective, inexpensive hybrid energy harvester provides a promising way for self-powered requirements in smart city settings.

Focusing on the problems of complicated sample processing and long detection time of traditional soil composition testing in mining areas, this paper proposes a rapid soil classification method of grid search and cross validation (GSCV) quadratic optimization-seeking network combined with magnetic field enhanced laser-induced breakdown spectroscopy (MF-LIBS), which can quickly and accurately classify standard soil samples from 10 different mining areas. First, the effects of different magnetic field strengths on the enhancement factor, signal-to-noise ratio (SNR) and plasma electron temperature of the characteristic spectral lines were investigated. Next, 5400 12 248-dimensional spectral data under the 0.98 T magnetic field constraint were preprocessed using principal component analysis (PCA). Then the back propagation (BP) network was optimized by combining the sparrow search algorithm (SSA) external optimization network with the genetic algorithm (GA) internal optimization network. Finally, the hyperparameters of the optimal network architecture are refined by GSCV for secondary optimization. By comparing with the unoptimized network, the results show that the classification accuracy of GSCV-SSA-GA-BP is 0.99997, the precision is 0.99977, the recall is 0.99981, and the F1-score is 0.99979. The proposed technique of GSCV quadratic optimization network combined with MF-LIBS soil classification can not only play an important role in soil research, but also provide a new scheme for rapid detection and high-precision qualitative analysis of soil components.