Search Results (293)

Pesticide residues represent an important group of chemical contaminants in agricultural commodities and require reliable analytical strategies for accurate monitoring. In this study, a high-throughput analytical workflow was applied for the determination of 210 pesticide residues in gherkins. Sample preparation was performed using the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method, including extraction followed by dispersive solid-phase extraction clean-up. Residue determination was carried out using liquid chromatography–tandem mass spectrometry (LC-MS/MS) and gas chromatography–tandem mass spectrometry (GC-MS/MS). The analytical methods were comprehensively validated in the gherkin matrix in accordance with the SANTE 11312/2021 v2 guidelines. Limits of quantification were ≤0.01 mg kg⁻¹ for all compounds. Recovery values ranged from 75.7% to 113.7%, while precision values remained below 20%, demonstrating satisfactory method accuracy and precision. Expanded measurement uncertainty values ranged between 7.6% and 41.3%, confirming the robustness of the validated analytical workflow. The validated methods were subsequently applied to a large-scale monitoring dataset comprising 905 gherkin samples collected from five major production regions in Türkiye. Pesticide residues were detected in 67.6% of the analysed samples, and 37 different compounds were identified. The most frequently detected pesticides were flonicamid (36.2%) and propamocarb (27.5%). Multi-residue contamination was frequently observed, reflecting complex pesticide application patterns in gherkin cultivation systems. Although chronic exposure estimates remained well below toxicological thresholds for both adults and children, certain exposure scenarios indicated that acute exposure for children may warrant further attention. Full article

Epoxy resin (E-51) exhibits excellent adhesion and is widely used in the preparation of functional composite coatings. However, its smooth surface lacking micro/nano composite structures limits its self-cleaning capability and optical properties. Direct incorporation of organic silicone or inorganic fillers often faces severe phase separation and filler agglomeration issues, resulting in defects in coating durability and weather resistance. To address these challenges, this study developed a synergistic modification strategy integrating surface energy modulation with the architectural design of micro/nano-structures. Amino-terminated PDMS undergoes ring-opening addition reactions with epoxy groups in the epoxy resin, while functionalized barium sulfate nanoparticles modified with dual silane coupling agents are incorporated to enhance optical properties. This synergistic approach not only resolved interfacial compatibility but also endowed the PDMS@EP-BaSO₄ coating with outstanding comprehensive properties; the water contact angle increased to 123.5°, demonstrating an easy-to-clean benefit. Visible light reflectance reached 95%, and emissivity rose to 90%. Furthermore, when applied to metal surfaces, the coating exhibited excellent stability against acid–alkali–salt corrosion, extreme temperatures, and ultrasonic agitation. This work provided a novel approach for developing protective coatings that integrated high reflectance, high emissivity, and long-term anti-soiling properties. Full article

Real-time and accurate acquisition of the wheat impurity rate is a key technology for realizing intelligent cleaning operations, and it directly influences the quality of wheat harvest. This study proposes a novel impurity rate regression network named Segmentation is Not The Purpose (SNTP). SNTP integrates a semantic segmentation network and an impurity rate regression network into a single neural architecture and replaces the DeepLabV3+ backbone with MobileNetV4, which serves as the segmentation branch of SNTP. Furthermore, a Transformer block is introduced into the regression branch to enable global feature extraction, and a Generalized Categorical Regression head is designed based on Distribution Focal Loss to improve regression accuracy. The SNTP model ultimately achieves an MIoU of 77.7%, an MPA of 83.3%, an MAE of 0.045, and an MSE of 0.005 on the validation set, with only 9.51M parameters and 17.98 GMACs of computation, successfully solving the overfitting problem in impurity rate regression networks and achieving high regression accuracy. SNTP is easy to optimize, requires no additional prior knowledge, and the performance of the SNTP model is unaffected by camera mounting height, making it exceptionally versatile for deployment and enabling real-time impurity rate detection, which is the key technology for intelligent cleaning. Full article

The growing demand for clean-label bakery products requires a deeper understanding of how functional ingredients and physicochemical properties shape consumer perception. This study characterized nine commercial clean-label breads formulated with alternative flours, oilseeds, and functional ingredients by integrating instrumental analyses (color, porosity, free amino acids, total phenolic content, antioxidant activity) with consumer evaluation using hedonic testing and Check-All-That-Apply (CATA). Sixty-five consumers evaluated the breads under blind conditions. Results showed that flour type and seed inclusion significantly affected color, structure, and bioactive compound levels. Breads with higher phenolic content and antioxidant activity (GB-B, GB-C, GB-D, PB-I) exhibited more complex aroma profiles, whereas breads with higher porosity (GB-A, PB-G) were perceived as softer. Taste and texture showed the strongest correlation with overall liking (r > 0.84). CATA and penalty analysis identified soft, easy to chew, sweet, and umami as key drivers of liking, while dry, adhesive, bran odor, and bitter negatively impacted acceptance. Data revealed that consumer preference depends on the balance between structural attributes, flavor development, and nutritional composition. These findings provide actionable insights for the formulation of clean-label breads that balance health benefits and sensory acceptance. Full article

Cleaning historical silk textiles is a particularly sensitive operation that requires precise control to prevent mechanical or chemical damage. In this study, we investigate using flexible PVA–borax-based gels to remove soot from silk, i.e., polyvinyl alcohol–borax (PVA-B) gels and polyvinyl alcohol–borax–agarose double network gels (PVA-B/AG DN) loaded with different cleaning agents—namely, 30% ethanol and 1% Ecosurf EH-6—in addition to plain gels loaded with water. These gel formulations were tested on simplified model systems (SMS) and were applied using two methods: placing and tamping. The cleaning results were compared with a traditional contact-cleaning approach; micro-vacuuming followed by sponging. Visual inspection, 3D opto-digital microscopy, colorimetry, and machine-learning-assisted (ML) soot counting were exploited for the assessment of cleaning efficacy. Rheological characterization provided information about the flexibility and handling properties of the different gel formulations. Among the tested systems, the DN gel containing only water, applied by tamping, was easy to handle and demonstrated the highest soot-removal effectiveness without leaving residues, as confirmed by micro-Fourier Transform Infrared (micro-FTIR) analysis. Scanning electron microscope (SEM) micrographs proved the structural integrity of the treated silk fibers. Overall, this work allows us to conclude that PVA–borax-based gels offer an effective, adaptable, and low-risk cleaning strategy for historical silk fabrics. Full article

(1) Background: In hospitals, mattresses are often relocated for cleaning or patient transfer, leading to mismatches between actual and recorded bed locations. Manual updates are time-consuming and error-prone, requiring an automatic localization system that is cost-effective and easy to deploy to ensure traceability and reduce nursing workload. (2) Purpose: This study presents a pragmatic, large-scale implementation and validation of a BLE-based localization system using RSSI measurements. The goal was to achieve reliable room-level identification of smart mattresses by leveraging existing hospital infrastructure. (3) Results: The system showed stable signals in the complex hospital environment, with a 12.04 dBm mean gap between primary and secondary rooms, accurately detecting mattress movements and restoring location confidence. Nurses reported easier operation, reduced manual checks, and improved accuracy, though occasional mismatches occurred when receivers were offline. (4) Conclusions: The RSSI-based system demonstrates a feasible and scalable model for real-world asset tracking. Future upgrades include receiver health monitoring, watchdog restarts, and enhanced user training to improve reliability and usability. (5) Method: RSSI–distance relationships were characterized under different partition conditions to determine parameters for room differentiation. To evaluate real-world scalability, a field validation involving 266 mattresses in 101 rooms over 42 h tested performance, along with relocation tests and nurse feedback. Full article

This review provides a comprehensive evaluation of recent advances in miniaturized Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) sample preparation techniques applied across food, environmental, and biological matrices. Covering developments within 2020–2025, it focuses on analytical performance, environmental impact, and alignment with principles of sustainable and green analytical chemistry. Central to this review is the significant reduction in solvent and sample volumes achieved through miniaturization, thus decreasing the reagent consumption and hazardous waste generation. The integration of eco-friendly extraction solvents and sorbent materials enhances selectivity and reduces the environmental footprint. These methods are often coupled with high-resolution mass spectrometers, enabling sensitive, multi-residue, and suspect analysis. Challenges associated with complex matrices, low analyte concentrations, and the need for robust clean-up procedures are addressed through innovative hybrid workflows and advanced materials, e.g., polymeric electrospun fibers and deep eutectic solvents. The growing adoption of greener protocols is highlighted. Moreover, it underscores their potential to improve routine analytical workflows while reducing environmental burden. Future research should focus on the development of sustainable sample preparation with improved sensitivity, broader applicability, and minimal ecological impacts. This comprehensive assessment supports the ongoing transformation of analytical chemistry towards more sustainable practices without compromising analytical reliability and efficacy. Full article

Mine accidents pose severe threats to worker safety and sustainable mining development in China. However, existing mine accident data in China are often scattered, unstructured, and lack systematic integration, which limits their application in safety research and practice. This study constructed a standardized structured dataset using 532 mine accident reports from official channels covering the period 2010–2025. The dataset went through four stages: data collection, standardized cleaning, structured annotation, and quality validation. It is stored in JSON Lines (JSONL) format for easy reuse. The dataset covers 27 provinces/autonomous regions/municipalities in China. Among accident levels, general accidents account for 65.6%; among accident types, roof accidents account for 20.3%. Accidents are geographically concentrated, with 11.7%, 8.3%, and 7.7% occurring in Shanxi, Gansu, and Inner Mongolia, respectively. Official data have shown an annual average decrease of 9.7% in mine accidents from 2018 to 2022, reflecting improved safety governance. This dataset addresses the gap of a full-element structured mine accident database in China, providing high-quality data for accident causation modeling, regional risk early warning, and safety policy evaluation. It also supports mine enterprises in targeted risk prevention and regulatory authorities in precise regulatory enforcement. Full article

Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed lands continues to increase. Evidence-based, climate-adaptive forest and fire management practices are critical for the responsible stewardship of public resources and require the continued availability of long-term ecological monitoring data. The US National Park Service has been collecting long-term fire monitoring plot data since 1998, and has continued to add monitoring plots, but these data are housed in databases with limited access and minimal analytic capabilities. To improve the availability and decision support capabilities of this monitoring dataset, we created the Trends in Forest Fuels Dashboard (TFFD), which provides an implementation framework from data collection to web visualization. This easy-to-use and updatable tool incorporates data from multiple years, plot types, and locations. We demonstrate our approach at Rocky Mountain National Park using the ArcGIS Online (AGOL) software platform, which hosts TFFD and allows for efficient data visualizations and analyses customized for the end user. Adopting interactive, web-hosted tools such as TFFD allows the National Park Service to more readily leverage insights from long-term forest monitoring data to support decision making and resource allocation in the context of environmental change. Our approach translates to other data-to-decision workflows where customized visualizations are often the final steps in a pipeline designed to increase the utility and value of collected data and allow easier integration into reporting and decision making. This work provides a template for similar efforts by offering a roadmap for addressing data availability, cleaning, storage, and interactivity that may be adapted or scaled to meet a variety of organizational and management use cases. Full article

In this review paper, we briefly discuss the occurrence of oil spills and their behavior under natural sea conditions and clean-up methods, as well as their environmental and economic impacts. We discuss methodologies for oil spill modeling used to predict the fate of a spill under dynamic physical and chemical processes. Weathering processes such as evaporation, emulsification, spreading, dissolution, dispersion, biodegradation, and sedimentation are considered within easy-to-use modeling frameworks. We present simple models based on the principles of thermodynamics, mass transfer, and kinetics that under certain conditions can predict oil thickness, volume, area, composition, and the distribution of toxic compounds in water and air over time for various types of oil and their products. Modeling approaches for underwater oil jets, including applications related to the 2010 BP oil spill in the Gulf of Mexico, are reviewed. The influence of sea surface velocity and wind speed on oil spill mapping, spill location, oil spill trajectory over time, areas affected by light, medium, and heavy oil, and comparisons between satellite images and model predictions are demonstrated. Finally, we introduce several recently published articles on more recent oil spill incidents and the application of predictive models in different regions. We also discuss the challenges, advantages, and disadvantages of various models and offer recommendations at the end of the paper. Full article

Wellbore washing technology is a basic operation in wellbore maintenance. Problems such as low automation levels, long processing times, the fact that it is easy to cause downhole falling, and cleaning blind areas greatly affect the use and maintenance of traditional cleaning equipment. These problems usually come from design defects such as a complicated installation process, a lack of an anti-impact structure, and a fixed jet direction. To address the aforementioned issues, this paper proposes an efficient and integrated rapid-disassembly and -assembly automatic filtration rotary jet cleaning device. The device is divided into two main units and further subdivided into four modules. The quick-assembly unit comprises an elastic connection module and a downstroke quick-assembly module, which can automatically compensate for deviations in equipment position during the installation process, ensuring the reliability of the installation process and the sealing of the equipment and facilitating the rapid connection and separation of the tool string. The wellbore cleaning unit includes a hydraulic rotary washing module and a rotary filtration storage module. The wellbore is jet-flushed by hydraulic drive, and the solid particles are separated and filtered during the cleaning fluid circulation process to realize the purification and reuse of the cleaning fluid. The device reduces the installation operation time and labor cost, improves the reliability of equipment in the well, improves the flushing coverage area and the cleaning efficiency, realizes the reuse of the cleaning liquid in the wellbore, reduces the energy consumption of the flowback treatment, and comprehensively improves the cleaning efficiency and the energy utilization efficiency. Full article

H₂ has become one of the most attractive alternatives to replace fossil fuels in clean energy production, but large-scale production remains a challenge. A key step toward this goal is to develop new efficient electrocatalysts for H₂ production. This work presents a new mixed metal oxides-decorated CNT paste electrode (MMO@C), which is highly electrocatalytic, for use in the hydrogen evolution reaction (HER). MMO@C is synthesized by a solvothermal method and used as an easy-to-prepare paste electrode. XPS and X-ray analysis indicate that the electrocatalyst corresponds to a mixed surface of Ga₂O₃-CuO-Cu₂O-Cu(OH)₂@C. The MMO@C electrocatalyst shows a positive E_o of 0.12 V vs. RHE at −10 mA cm⁻² towards the HER in a neutral medium. In neutral and alkaline media, the presence of Ga₂O₃ facilitates the reduction of CuO to Cu(I) species, which is followed by the formation of Cu(s) active sites. Therefore, the excellent electrocatalytic performance toward the HER in a neutral medium is attributed to the synergistic effect between gallium and copper oxides on the electrode surface. The prominent H₂ production using MMO@C electrocatalyst is 1.31 × 10⁻² mol cm⁻², with a turnover number (TON) of 39,423, a turnover frequency (TOF) of 13,141 h⁻¹, and a faradaic efficiency (FE) of 94.3%. Although the Tafel slope reveals slow reaction kinetics, the outstanding onset potential allows for the coupling of the electrocatalyst to renewable energy production systems, making it an attractive candidate for producing green H₂ and for application in membrane water electrolyzers. Full article

With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Although membrane technology has been widely applied in water treatment, membrane fouling is an inevitable issue that has largely limited its application. Benefiting from the advantages of green power, easy integration and low chemical consumption, electrocatalytic membrane (ECM) technology received attention, using it to enable electrically driven self-cleaning performance recently, making it highly desirable for sustainable fouling control. In this work, we comprehensively summarized the conventional (e.g., carbonaceous materials, metal and metal oxide) and emerging (e.g., metal–organic framework and MXene) materials for the fabrication of an ECM. Then the fabrication methods and operating modes of an ECM were emphasized. Afterwards, the application of different ECM materials in membrane fouling control was highlighted and the corresponding mechanism was revealed. Based on existing research findings, we proposed the challenges and future prospects of ECM materials for practical application. This study provides enlightening knowledge into the development of ECM materials for sustainable fouling control. Full article