Highly sensitive detection of daminozide using terahertz metamaterial sensor

Most self‐powered electrochemical sensors (SPESs) are limited by low open circuit voltage and power density, leading to a narrow detection range and low sensitivity. Herein, a photoinduced Zn‐air battery‐assisted SPES (ZAB‐SPES) is proposed based on cobalt and sulfur co‐doped carbon nitride with the cyano group (Co, S‐CN). The cyano functionalization remarkably enhances visible light utilization, and the cyano moiety acts as an electron‐withdrawing group to promote electron enrichment. Co and S co‐doping can create a p–n homojunction within carbon nitride, enabling the efficient migration and separation of carriers, thereby significantly improving the performance of the oxygen reduction reaction. The synergistic effects endow Co, S‐CN photocathode with an open circuit voltage of 1.85 V and the maximum power density of 43.5 µW cm⁻² in the photoinduced ZAB. Employing heavy metal copper ions as the target model, the photoinduced ZAB‐SPES exhibited dual‐mode and sensitive detection. Furthermore, a portable detection device based on the photoinduced ZAB‐SPES is designed and exhibits high linearity in the range of 5 ~ 600 nM with a detection limit of 1.7 nM. This work offers a portable detection method based on the photoinduced ZAB‐SPES in the aquatic environment.

Developing a new functional sensitized structure for improving the inherent property of photoactive materials and selecting an efficient electron donor as a photoelectrochemical (PEC) signal amplification strategy are crucial for achieving excellent PEC biosensors. Herein, we present a facile one-pot hydrothermal strategy for fabricating ZnO nanorod-decorated 3D graphene aerogels (ZnO/GAs). In this nanoarchitecture, GAs act as a new generation enhanced carrier, which can effectively improve the photoactivity of ZnO under visible light by enhancing the interfacial charge transfer. In addition, the as-prepared ZnO/GA nanocomposites exhibited two times higher photocurrent intensity than that of ZnO/graphene. Furthermore, we developed a novel self-powered PEC biosensor based on a ZnO/GAs sensitized structure with the amplification of enzymolysis products for the detection of parathion-methyl. Thiocholin, as a sacrificial electron donor, which is produced from the hydrolysis of acetylthiocholine catalyzed by acetylcholinesterase (AChE), can further significantly improve the photocurrent. Then, the inhibition of AChE activity by parathion-methyl prevented the production of thiocholine, resulting in an obvious decrease in photocurrents. Based on the above results, we fabricated an AChE-based self-powered PEC biosensor for the sensitive and selective determination of parathion-methyl with a linear range of 0.1 ng mL⁻¹ to 0.1 μg mL⁻¹ and a detection limit of 0.03 ng mL⁻¹ (S/N = 3). This PEC biosensing strategy not only gives insights into using GAs as a novel sensibilizer to improve the PEC nature of semiconductors but also provides a promising flexible platform for organophosphorus pesticide detection.

As the main working part of a combine harvester, the cleaning device affects the cleaning performance of the machine. The simulation of flow fields in a cleaning chamber has become an important part of the design. Currently, post-processing analyses of flow field simulation still rely on the researchers’ experience, so it is difficult to obtain information from post-processing automatically. The experience of researchers is difficult to describe and disseminate. This paper studied an intelligent method to analyze simulation result data which is based on the object detection algorithm and the reasoning mechanism. YOLOv8, one of the deep learning object detection algorithms, was selected to identify key-point data from the flow field in a cleaning chamber. First, the training dataset was constructed via scatter plot drawing, data enhancement, random screening, and other technologies. Then, the flow field in the cleaning chamber was divided into six key areas by identifying the key points of the flow field. And, an analysis of the reasonable wind velocity in the areas was conducted, and the cleaning results of the grain were obtained by using the reasoning mechanism based on rules and examples. Finally, a system based on the above method was established in Python 3.10 software. With the help of the method and the system in this paper, the flow field characteristics in a cleaning chamber and the effects of wind on the cleaning effect can be obtained automatically if the physical properties of the crop, the geometric parameters of the cleaning chamber, and the working parameters of the machine are given.

Optimization synthesis of semiconductor nanostructures with controlled crystallinity, size and morphology for improving inherent property of photoactive materials is crucial for achieving excellent PEC biosensor. Here, we tried to synthesize ZnO/graphene nanocomposite (ZnO/GR) via thermal decomposition using zinc sulfate and graphene oxide (GO) as starting materials. It was found that the annealing temperature greatly affects the phase transition and nanocrystallites size of the as-prepared nanostructure. Interestingly, the existence of GO can greatly decrease the decomposition temperature of zinc sulfate from 930°C to 530°C, solving the problem of zinc sulfate rarely used as a precursor for preparing ZnO because of its high decomposition temperature at 930°C. Further, we investigated the photoelectrochemical (PEC) behavior of these different nanostructures obtained under variety of temperatures. And the results indicated that nanocomposite obtained under temperature of 700°C have significant PEC efficiency, which was attributed to more uniform and well distributed nanocrystallite size and sulfur doping into the graphene under the temperature of 700°C. Finally, a “signal-off” PEC sensor was designed for Adenosine triphosphate (ATP) detection based on a ZnO/GR-700°C nanocomposite as photoactive materials and an ATP-binding aptamer as the recognition element. The proposed “signal-off” PEC aptasensing exhibited a wide linear range from 5 to 3000 nM with a low detection limit of 1.66 nM.