A ZnO nanomaterial-based surface acoustic wave (SAW) ultraviolet (UV) detector is highly desirable for UV radiation detection due to its high sensitivity. In this work, firstly the ZnO nanomaterial-based SAW UV detectors operating at three different frequencies (∼50, ∼100 and ∼200 MHz) were fabricated. Then, four ZnO nanomaterial sensing layers with different thicknesses were synthesized on the SAW UV detectors operating at ∼200 MHz. The morphology, crystallization and photoluminescence of ZnO nanomaterial sensing layers were characterized using the scanning electron microscopy, transmission electron microscopy, x-ray diffraction and fluorescence spectrometer, respectively. The SAW UV detectors based on different operating frequencies and ZnO nanomaterial sensing layer's thicknesses were exposed under UV illumination at a wavelength of 365 nm and their UV responses were measured. The experimental results indicate that the frequency shift of ZnO nanomaterial-based SAW UV detector can be significantly improved by increasing operating frequency or ZnO nanomaterial sensing layer's thickness. Furthermore, the detectors exhibit good selectivity of UV illumination, an ultrahigh UV sensitivity of about 9.6 ppm (µW cm−2)−1 and fast transient properties. The experimental results agree well with the acousto-electric effect theory. What deserves to be noted is that, under a UV intensity of 150 µW cm−2, the frequency shift of the SAW UV detector operating at ∼50 MHz with a thin ZnO nanomaterial sensing layer was only ∼50 kHz while that of the SAW UV detector operating at ∼200 MHz with a thick ZnO nanomaterial sensing layer could reach ∼292 kHz. These results suggest the huge potential applications of ultra-sensitive ZnO nanomaterial-based SAW UV detectors for remote wireless UV and radiation monitoring.

Based on the similarity between the working mechanism of memristors and the synapses of neural networks, a design scheme of perceptron neural networks based on memristors is proposed in this article. Using memristor based perceptron neurons as the basic unit, a single layer perceptron neural network based on memristors is implemented by adding perceptron neurons in parallel. A multilayer perceptron neural network based on memristors is implemented by serially adding a single layer perceptron neural network. The multiclassification function of the single layer perceptron neural network based on memristors was verified through experiments to determine the quadrant of points in a two-dimensional plane, with a classification accuracy of 96.67%. Through logical XOR operation experiments, the ability of the multilayer perceptron neural network based on memristors to handle linear indivisible problems is verified, and the error is zero. The experimental results of multiclassification and linear indivisibility problems confirm the feasibility of the memristor based perceptron network design scheme.

In order to overcome the shortcomings of the measurement methods for gas cumulative flow, especially in the long-term continuous measurement, a novel memristor-based gas cumulative flow sensor is proposed. The sensor is mainly composed of a hot-wire resistor, the memristor module, a feedback compensation network and some basic circuit components. According to the thermal balance equation and the memory characteristics of memristor, the sensor can measure the gas cumulative flow by combining the memristor resistance with the cross-sectional area of the pipe. In order to verify the design of memristor-based gas accumulation flow sensor, experiments are carried out under a constant gas flow velocity, a gradually increasing flow velocity, and a randomly varying flow velocity. The active region thickness D of the selected TiO2/TiO2-n memristor is 40 nm. The gas measured is air at a temperature of 300 K in the paper. The experimental results show that the sensor achieves the measurement of gas cumulative flow. When gas flow velocity at the center of the pipe is 5 m/s, the measuring range is approximately 0-20 m3 and the sensitivity within the effective range is approximately 28.44 μ/m3.