STM32-Based Organic Pollutant Detection Device Design

Detection of organic pollutants in aqueous media is crucial for guaranteeing water safety. Conventional methods for organic pollutant detection suffer from time-consuming operation procedures (on the order of hours) and expensive devices. Inspired by dog noses, herein, we constructed self-assembled fluorescent nanosheets for rapid and sensitive detection of organic pollutants based on the grasp-report strategy. Tetraphenylethene decorated cyclodextrins (TPE-CDs) self-assembled into nanosheets with hydrophobic TPE layers sandwiched between two hydrophilic cyclodextrin layers. The hydrophobic cavity of the outer cyclodextrin layers grasped and collected organic pollutants, and subsequently transported them to the TPE layers and quenched the fluorescence emission of TPE layers. Such nanosheets allowed rapid detection of xylene (on the order of seconds) at a concentration of 5 μg/L. With the merits of the ease of synthesis, simple operation, and high sensitivity, the fluorescent nanomaterials provide a promising candidate for rapid and sensitive detection of organic pollutants.

近年来，固体废弃物产量大、成分复杂，对环境的危害日益严重，引起了广泛关注。固体废弃物鉴别技术已成被囊入“十三五”规划，加强固体废弃物中有机污染物的检测成为重点任务。固体废物中有机污染物的检测主要依赖于有效的前处理方法和精密的仪器分析方法。本文阐明了我国固体废物管理和检测框架体系以及固体废物和危险废物的鉴别程序。集中总结了近几年关于固体废物中有机物检测的方法标准，对比了固体废物有机污染物检测中不同前处理技术的利弊，讨论了主要的分析方法，并提出了在此领域引进新的前处理技术，结合分析方法进行优化改良创建新方法的必要性。 In recent years, because of the huge production of solid wastes and the complex components, the disruption to the environment is becoming more and more serious, which has attracted wide attention. It is the key task to strengthen the detection of organic pollutants in solid waste. The de-tection of organic pollutants in solid waste is mainly dependent on effective pretreatment methods and precise instrument analysis methods. This paper describes the framework for the management and detection of solid waste in China and the identification procedures of solid waste and hazardous waste. The method standard for the detection of organic matter in solid waste in recent years is summarized. The advantages and disadvantages of different pretreatment techniques in the detection of organic pollutants in solid waste are compared. The main analytical methods are discussed. It also puts forward the necessity of introducing new pretreatment technology in this field and combining with the analysis method to optimize and establish a new method.

Plastic, pharmaceutical, and heavy metal pollution continues to escalate, creating an urgent need for efficient detection and removal of hazardous pollutants. Consequently, emerging technologies are critically required to advance environmental monitoring and remediation. Metal-organic frameworks (MOFs), as porous, flexible, and multifunctional materials with unique structural characteristics, offer viable solutions to these challenges, with MIL-88(Fe) serving as the central focus of this study. In this paper, we review the synthesis and applications of MIL-88(Fe): we summarize and compare its solvothermal, microwave-assisted, electrochemical, ultrasonic, and mechanochemical synthesis methods, and systematically review the removal and detection of organic pollutants, pharmaceuticals, and heavy metals by MIL-88(Fe) and its composites under varying conditions. MIL-88(Fe) exhibits notable advantages: it possesses a stable and tunable structure, enabling adaptation to complex environments; it demonstrates high adsorption capacity and detection sensitivity; it achieves high efficiency through mechanisms including coordination, electrostatic adsorption, and π-π stacking; and it is reusable, thereby reducing application costs. In conclusion, MIL-88(Fe) is a multifunctional material for environmental remediation, and its potential has been validated. Future research should focus on optimizing its performance, scaling up synthesis, integrating it with other materials and technologies, and enhancing environmental protection and pollution control.

Adsorption and detection of organic pollutants by fixed bed carbon nanotube electrochemical membrane

Consumer demand for reliable food product quality and safety is growing. This trend, together with increased public regulation and attention to the legal liability of food processors and retailers creates derived demand for food safety assurance in farm production. In consequence, farms have adopted different measures, voluntarily or compulsorily, in their production practice to ensure reduced food safety risks from the farm product. Multiple individual indicators exist which reflect different facets of food safety practice. In fact, it is likely that production of a safer product is a result of several factors. However, little is known about what practices effect greater food safety control at the farm level, or how farms that take greater food safety control fare in comparison to other farms. This study develops a composite food safety control indicator by aggregating data from a set of individual indicators of food safety control and investigates the variation in food safety practices across farms. Moreover, we show how some relevant variables may influence farm food safety control, thus provide empirical evidence for the design of food safety-enhancing agricultural policy measures.

Food borne illness today still results in high societal costs, even though FBOs have implemented food safety control systems. In addition, the susceptibility to food borne diseases of the human population is likely to increase in the coming decades. Thus food safety control needs to be improved. Since 2005, a new EU food safety policy aims to improve food safety through shifting primary responsibility for food safety from government to FBOs and through shifting food safety control from company level to supply chain level. This research aims to contribute to improvement of food safety by analyzing private incentive mechanisms aimed at food safety control on supply chain level. It focuses on the two stage supply chain between pig producers and slaughter company in the Netherlands. A framework for designing and developing incentive mechanisms for food safety control is developed. An incentive mechanism aimed at food safety control is defined as the set of the performance and compliance measurement system and the compensation scheme between buyer and supplier, which aims to induce the supplier to apply measures to control food safety hazards as the buyer requests. The framework includes all important characteristics of incentive mechanisms for food safety and their relationships. In this thesis the influence on supplier behaviour of four important characteristics of incentive mechanisms for food safety control are analysed, namely 1) the type of performance compensation, 2) the causes for variability in performance between suppliers, 3) the accuracy of a test to determine supplier performance, and 4) the reliability of information provided by the supplier. It uses different food safety hazards in pork: lesioned livers, Mycobacterium avium, and residues of antibiotics. Results show that a penalty for each lesioned liver was more effective to induce pig producers to use control measures than a collective premium. Variability in liver lesion prevalence between pig producers with a penalty for each lesioned liver occurred because each pig producer used different control measures with different effectiveness. The accuracy of a Mycobacterium avium test showed to have a significant impact on pig producer behaviour to use control measures if an incentive mechanism was in place. Finally, information about the use of antibiotics provided by pig producers without an incentive system for the reliability of the information was insufficiently reliable to guarantee absence of residues in pork. It is concluded in this thesis that private incentive mechanisms can be used to reduce opportunistic behaviour of pig producers, thereby improving food safety control on supply chain level and helping to raise food safety control to the next level. For optimal inducement it is important that the performance and compliance measurement system is attuned to the compensation scheme.

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

Fluorescent nanoparticles of amphiphilic block copolymers for sensitive and rapid detection of N-ethylaniline in water

With the rapid development of autonomous driving technology, traditional object detection algorithms often face challenges such as the trade-off between model parameters and recognition accuracy, insufficient detection accuracy, and slow detection speed when dealing with vehicle and pedestrian detection tasks in complex scenarios. To address these challenges, we propose a novel lightweight real-time driving scene detection network, FEEP-YOLO. Based on the YOLOv8 algorithm, our network not only maintains high detection accuracy but also achieves faster training speed and fewer model parameters. FEEP-YOLO employs a lightweight network, Faster Net, to significantly reduce model parameters, thereby accelerating detection speed and better meeting real-time requirements. To further improve detection accuracy, we introduce an EMA attention module into the network and optimize the bounding box loss function, adopting the EIOU loss function to enhance target features and reduce background interference. Additionally, to detect small objects more accurately, we add feature layers rich in semantic information to the detection end. Experimental results demonstrate that FEEP-YOLO outperforms YOLOv8 in terms of both detection speed and accuracy on the KITTI and SODA datasets, while significantly reducing the number of model parameters and model size. FEEP-YOLO achieves high detection speed while improving detection accuracy and reducing computational resource consumption, meeting the practical application requirements under limited computing power, memory space and power consumption. The key contribution of FEEP-YOLO lies in its ability to balance accuracy speed, and efficiency, making it a robust solution for real-time autonomous driving applications in complex environments.

This paper designs a detection method for real-time acquisition of traffic intersection parameters based on image processing. Firstly, two virtual coils were drawn at the beginning and the end on the acquired video stream image. Next, the presence of the vehicle was judged based on the change of the gray value in the two virtual coils. The background difference algorithm was adopted to detect and obtain various traffic parameters. The experimental video samples were collected in the actual road environment. Our algorithm relies on the first and last two coils for information processing, and completes vehicle detection, speed detection, and important parameter information acquisition of body color. Experiments show that, compared with the traditional machine vision-based traffic information detection algorithm, our traffic parameter detection method, which is based on double virtual coils, has high detection accuracy, and can detect more parameter information, while satisfying real-time performance.

Improvement of water pollution detection method based on convolution neural network

Pets are the most intimate companions of humans, as pets and people share most of their lives indoors. Based on the connections between pet animals, humans, and the environment, pet cats and dogs are often recommended as sentinels for the detection of environmental contaminants and for comorbidity tracking. However, their suitability as sentinels is yet to be established. Persistent organic pollutants and environmental hormones have replaced particulate matter (PM2.5) and volatile organic compounds (VOCs) in causing indoor air pollution. This review summarises the differences in the types and concentrations of indoor organic environmental pollutants detected in pet dogs and cats. This includes an analysis of the main exposure routes of different types of pollutants. To identify which of the two pet species are better sentinels, cats and dogs were compared based on their metabolic capacities of various indoor organic pollutants. In addition to PM2.5, a range of organic compounds including polychlorinated biphenyls, polybrominated diphenyl ethers (PBDEs), bromophenols, perfluoroalkyl substances, organochlorine pesticides, fungicides, polycyclic aromatic hydrocarbons, phthalic acid esters, organophosphate pesticides, organophosphorus flame retardants, and melamine have been detected in both dogs and cats. Pets often accumulate PBDEs from dust; however, traces of PBDEs are present in their diet. The indoor pollutant contamination levels in internal cats were generally higher than those in dogs. Cats accumulate organic pollutants associated with indoor environments, but they are sensitive to their toxicity because, unlike dogs, cats cannot metabolise most of the accumulated pollutants. Moreover, cats share similar clinical symptoms of thyroid diseases in humans. Based on the above observations of detection of indoor organic pollutants, it could be said that cats are better sentinels than dogs.

With the continuous improvement of modern living standards, people pay more and more attention to food safety issues. It is undoubtedly cumbersome and prone to omissions to ensure food safety only by human resources. Food safety constantly threatens people’s health, and frequent food safety problems also bring new difficulties and challenges to China’s food supervision. The application of computer information technology has effectively improved the actual effect of food safety control, but there are also some problems. Therefore, we must strive to find an effective way to apply computer information technology in food safety control, so as to play a real control role. This paper introduces the importance of computer information technology in food safety management of food enterprises, analyzes the application methods of computer information technology in food safety management of food enterprises, and puts forward effective measures for the application of computer information technology in food safety management of food enterprises under the background of big data.

Sample-extraction methods for ion-mobility spectrometry in water analysis

A new ratiometric electrochemical sensor for sensitive detection of bisphenol A based on poly-β-cyclodextrin/electroreduced graphene modified glassy carbon electrode