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Lithium niobate (LiNbO3) single crystals are a kind of ferroelectric material with a high piezoelectric coefficient and Curie temperature, which is suitable for the preparation of piezoelectric pressure sensors. However, there is little research reporting on the use of LiNbO3 single crystals to prepare piezoelectric pressure sensors. Therefore, in this paper, LiNbO3 was used to prepare piezoelectric pressure sensors to study the feasibility of using LiNbO3 single crystals as a sensitive material for piezoelectric pressure sensors. In addition, chemical mechanical polishing (CMP) technology was used to prepare LiNbO3 crystals with different thicknesses to study the influence of these LiNbO3 crystals on the electric charge output of the sensors. The results showed that the sensitivity of a 300 μm sample (0.218 mV kPa-1) was about 1.23 times that of a 500 μm sample (0.160 mV kPa-1). Low-temperature polymer heterogeneous integration and oxygen plasma activation technologies were used to realize the heterogeneous integration of LiNbO3 and silicon to prepare piezoelectric pressure sensors, which could significantly improve the sensitivity of the sensor by approximately 16.06 times (2.569 mV kPa-1) that of the original sample (0.160 mV kPa-1) due to an appropriate residual stress that did not shatter LiNbO3 or silicon, thus providing a possible method for integrating piezoelectric pressure sensors and integrated circuits.

Rice is a staple food for almost half of the world's population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient. [ABSTRACT FROM AUTHOR]

BiFeO3 (BFO) films with ferroelectricity are the most promising candidates regarding the next generation of storage devices and sensors. The comprehensive understanding of ferroelectric switchable properties is challenging and critical to robust domain wall nanoelectronics. Herein, the domain dynamic was explored in detail under external bias conditions using scanning probe microscopy, which is meaningful for the understanding of domain dynamics and the foundation of ferroelectric devices. The results show that domain reversal occurred under external electric fields with sufficient energy excitation, combined with the existence of a charged domain wall. These findings extend the domain dynamic and current paths in ferroelectric films and shed light on the potential applications for ferroelectric devices. [ABSTRACT FROM AUTHOR]

Silicon based lithium niobate (LiNbO3) single crystal heterogeneous integrated thin films were prepared using chemical mechanical polishing. The film states at different stages of the film preparation process were studied. The surface morphology and elemental content changes of single crystal LiNbO3 were characterized using atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). Large area periodic polarization of LiNbO3 thin films was achieved using high-pressure polarization device. The results indicate that combined with the grinding progresses, the surface roughness of the sample gradually decreases. After completing the final polishing process, the root mean square roughness of the sample surface basically reaches the initial sample level, and the polishing process may lead to the loss of Li, but it can be repaired through subsequent processes. The impurity elements introduced during the grinding process will gradually be removed as the process progresses, ultimately obtaining high-quality silicon based LiNbO3 single crystal films, and using a high-pressure polarization device to polarize LiNbO3 thin film, a period strip domain reversal was obtained. This study is of great significance for the manufacturing of high-performance sensor components based on LiNbO3. [ABSTRACT FROM AUTHOR]

In this study, a novel approach for enhancing the rapid spreading of free liquid film on lithium niobate (LiNbO3) substrate under megahertz (MHz) surface acoustic wave (SAW) excitation is presented by treating it with surfactants. Through the design of a specific interdigital transducer structure, it was discovered that exciting the SAW at a frequency of 32.3 MHz can achieve optimal spreading performance for water droplets on the surface of surfactant-treated LiNbO3 substrate. The maximum average velocity reaches 1.76 mm/s at position P2 = 1250 μm in the water film front, and the stable film spreading speed shows a 204.9% increase compared to the existing research. Simultaneously, through the investigation of the spreading experiment phenomenon of silicone oil and de-ionized water droplets at varying frequencies, we have discovered the dynamic mechanism of "reverse phase" propagation in liquid film for the first time. This entails that the advancing edge of the wetting film demonstrates a spreading motion law that is opposite to the traditional spreading phenomena, with the spreading velocity in the central exceeding that on both sides. Our research demonstrates that this microfluidic device developed by SAWs enhances the spreading efficiency of the free films, enabling rapid expansion of the target liquid to form a high-surface area film layer. This advancement holds promise for overcoming the limitations of low sensitivity and short response time in the field of rapid pathological diagnosis in contemporary medicine. [ABSTRACT FROM AUTHOR]

The study of the properties of ferroelectric materials against irradiation has a long history. However, anti−irradiation research on the ferroelectric domain has not been carried out. In this paper, the irradiation of switched domain structure is innovatively proposed. The switched domain of 700 nm lithium niobate (LiNbO3, LN) thin film remains stable after gamma irradiation from 1 krad to 10 Mrad, which was prepared by piezoresponse force microscopy (PFM). In addition, the changing law of domain wall resistivity is explored through different sample voltages, and it is verified that the irradiated domain wall conductivity is still larger than the domain. This domain wall current (DWC) property can be applied to storage, logic, sensing, and other devices. Based on these, a ferroelectric domain irradiation resistance model is established, which explains the reason at an atomic level. The results open a possibility for exploiting ferroelectric materials as the foundation in the application of space and nuclear fields. [ABSTRACT FROM AUTHOR]

In order to reduce pesticide application, develop green pest prevention and control technology, and ensure the quality and safety of agricultural products, a green pest prevention and control system was designed which composed of multi-functional plant protection machine and information management system, for facility vegetables based on the internet of things (IoT) technology, and combined with ozone sterilization and disease prevention, light source induced pest control, crop health identification, pest identification and early warning and other technologies. The results showed that under the green control system, the incidence rate of strawberry powdery mildew decreased by 27.7%, and the average yield of strawberry increased by 19.8% compared with the conventional application method, which had significant effect on powdery mildew control and potential of strawberry yield increase. Meanwhile, the functions of system early warning service, various disinfection and sterilization methods, remote control and intelligent trusteeship had effectively alleviated the problems of backward plant protection technology and equipment, excessive dependence on pesticide use, structural shortage of labor force, and maintenance and operation difficulties in China. The application of the system realized the greening, specialization and intellectualization of vegetable pest control in facilities to a certain extent. [ABSTRACT FROM AUTHOR]

In micron or nano smart sensing systems, piezoelectric cantilever beams are distributed as major components in microsensors, actuators, and energy harvesters. This paper investigates the performance of four cantilever beam devices with "electric-force" conversion based on the inverse piezoelectric effect of lithium niobate (LiNbO3, LN) single-crystal materials. A new compact piezoelectric smart device model is proposed, designed as a single mass block connected by four beams, where devices exhibit smaller lateral errors (0.39–0.41%). The relationship between the displacement characteristics of cantilever beams and driving voltage was researched by applying excitation signals. The results show that the device has the maximum displacement at a first-order intrinsic frequency (fosc = 11.338 kHz), while the displacement shows a good linear relationship (R2 = 0.998) with driving voltage. The square wave signals of the same amplitude have greater "electrical-force" conversion efficiency. The output displacement can reach 12 nm, which is much higher than the output displacement with sinusoidal excitation. In addition, the relative displacement deviation of devices can be maintained within ±1% under multiple cycles of electrical signal loading. The small size, high reliability, and ultra-stability of Si–LN ferroelectric single-crystal cantilever beam devices with lower vibration amplitudes are promising for nanopositioning techniques in microscopy, diagnostics, and high-precision manufacturing applications. [ABSTRACT FROM AUTHOR]