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Considering the potential threats posed by oily wastewater to the ecosystem, it is urgently in demand to develop efficient, eco-friendly, and intelligent oil/water separation materials to enhance the safety of the water environment. Herein, an intelligent hydrogel-coated wood (PPT/PPy@DW) membrane with self-healing, self-cleaning, and oil pollution detection performances is fabricated for the controllable separation of oil-in-water (O/W) emulsions and water-in-oil (W/O) emulsions. The PPT/PPy@DW is prepared by loading polypyrrole (PPy) particles on the delignified wood (DW) membranes, further modifying the hydrogel layer as an oil-repellent barrier. The layered porous structure and selective wettability endow PPT/PPy@DW with great separation performance for various O/W emulsions (≥98.69% for separation efficiency and ≈1000 L m-2 h-1 bar-1 for permeance). Notably, the oil pollution degree of PPT/PPy@DW can be monitored in real-time based on the changed voltage generated during O/W emulsion separation, and the oil-polluted PPT/PPy@DW can be self-cleaned by soaking in water to recover its separation performance. The high affinity of PPT/PPy@DW for water makes it effective in trapping water from the mixed surfactant-stabilized W/O emulsions. The prepared eco-friendly and low-cost multifunctional hydrogel wood membrane shows promising potential in on-demand oil/water separation and provides new ideas for the functional improvement of new biomass oil/water separation membrane materials.

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Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods.

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The monitoring of small extracellular vesicles (sEVs) in medical waste is of great significance for the prevention of the spread of infectious diseases and the treatment of environmental pollutants in medical waste. Highly sensitive and selective detection methods are urgently needed due to the low content of sEVs in waste samples and the complex sample composition. Herein, a glycosyl-imprinted electrochemical sensor was constructed and a novel strategy for rapid, sensitive, and selective sEVs detection was proposed. The characteristic trisaccharide at the end of the glycosyl chain of the glycoprotein carried on the surface of the sEVs was used as the template molecule. The glycosyl-imprinted polymer films was then prepared by electropolymerization with o-phenylenediamine (o-PD) and 3-aminophenylboronic acid (m-APBA) as functional monomers. sEVs were captured by the imprinted cavities through the recognition and adsorption of glycosyl chains of glycoproteins on sEVs. The m-APBA molecule also acted as a signal probe and was then attached on the immobilized glycoprotein on the surface of sEVs by boric acid affinity. The electrochemical signal of m-APBA was amplificated due to the abundant glycoproteins on the surface of sEVs. The detection range of the sensor was 2.1 × 104 to 8.7 × 107 particles/mL, and the limit of detection was 1.7 × 104 particles/mL. The sensor was then applied to the determination of sEVs in medical wastewater and urine, which showed good selectivity, low detection cost, and good sensitivity.

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Microplastics (MPs) pollution has emerged as a critical environmental issue with far-reaching consequences for ecosystems and human health. These are plastic particles measuring <5 mm and are categorized as primary and secondary based on their origin. Primary MPs are used in various products like cosmetics, scrubs, body wash, and toothpaste, while secondary MPs are generated through the degradation of plastic products. These have been detected in seas, rivers, snow, indoor air, and seafood, posing potential risks to human health through the food chain. Detecting and quantifying MPs are essential to understand their distribution and abundance in the environment. Various microscopic (fluorescence microscopy, scanning electron microscopy) and spectroscopy techniques (FTIR, Raman spectroscopy, X-ray photoelectron spectroscopy) have been reported to analyse MPs. Despite the challenges in scalable removal methods, biological systems have emerged as promising options for eco-friendly MPs remediation. Algae, bacteria, and fungi have shown the potential to adsorb and degrade MPs in wastewater treatment plants (WWTPs) offering hope for mitigating this global crisis. This review examines the sources, impacts, detection, and biological removal of MPs, highlighting future directions in this crucial field of environmental conservation. By fostering global collaboration and innovative research a path towards a cleaner and healthier planet for future generations can be promised.

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In recent years, the management of atmospheric fine particulate matter(PM2.5) pollution in China has achieved staged success, but ozone(O3) pollution has increased rapidly. Detection and source localization of atmospheric pollutants is the basis and key to controlling the combined pollution of PM2.5 and O3. With the rapid development of UAV technology and sensor technology, air pollution detection based on UAV platforms can effectively obtain the structural characteristics of PM2.5 and O3 near the surface and accurately trace the source of air pollution events by applying the computer algorithms, with the characteristics of high timeliness, flexibility, and spatial and temporal resolution. This will help researchers understand the distribution, changes, and sources of regional pollutants and provide a scientific basis for the synergistic control of combined air pollution. This study reviewed the traditional air pollution detection methods, summarized the types of UAV platforms and detection instruments commonly used in pollution detection, concluded the applications of UAV-based PM2.5 and O3 pollution detection and the algorithms of pollution source localization, and discussed the future trends of UAV-based air pollution detection.

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Solar-to-steam (STS) generation based on plasmonic materials has attracted significant attention as a green method for producing fresh water. Herein, a simple in situ method is introduced to fabricate Au nanoparticles (AuNPs) on cellulose filter papers as dual-functional substrates for STS generation and surface-enhanced Raman spectroscopy (SERS) sensing. The substrates exhibit 90% of broadband solar absorption between 350 and 1800 nm and achieve an evaporation rate of 0.96 kg·m-2·h-1 under 1-sun illumination, room temperature of 20 °C, and relative humidity of 40%. The STS generation of the substrate is stable during 30 h continuous operation. Enriched SERS hotspots between AuNPs endow the substrates with the ability to detect chemical contamination in water with ppb limits of detection for rhodamine 6G dye and melamine. To demonstrate dual-functional properties, the contaminated water was analyzed with SERS and purified by STS. The purified water was then analyzed with SERS to confirm its purity. The developed substrate can be an improved and suitable candidate for fresh water production and qualification.

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Spectrometric analysis is one of the most widely used approaches to characterize the chemical nature of microplastics. Despite recent developments, this key step remains time consuming. The aim of this paper is to propose a new method for the pre-detection of microplastics based on mid-infrared imaging. Plastic particles were mixed with sand particles and placed on a glass filter. Infrared observation with a thermal camera shows a stronger thermal contrast measured between the filter and the plastics than between the filter and the sand, which reveals the plastic particles in a few tens of seconds. An image processing tool is then used to amplify this contrast. Furthermore, this pre-detection method makes it possible to propose hypotheses on the most probable chemical nature of the particles identified. Consequently, pre-detection using active thermography constitutes a promising way of significantly accelerating microplastic study.

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Food safety analysis is an important procedure to control food contamination and supervision. It is urgently needed to construct effective methods for on-site, fast, accurate and popular food safety sensing. Among them, microfluidic chip technology exhibits distinguish advantages in detection, including less sample consumption, fast detection, simple operation, multi-functional integration, small size, multiplex detection and portability. In this review, we introduce the classification, material, processing and application of the microfluidic chip in food safety sensing, in order to provide a good guide for food safety monitoring.

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