Search Results (193)

The systems to manipulate a single particle in a microfluidic channel can be adopted to pharmacological and cytological experiments of single-cell observation. The common cell position systems use syringe pumps driven by piezoelectric devices, and these have a flow quantity limit. To achieve single-cell manipulation using actuators without limiting the flow quantity and with a low risk of contamination, we propose a particle control system that uses a sidewall-driven peristaltic micropump driven by pneumatic pressure. The adopted pump was integrated into a single-layer mold with a flow path and was simple to fabricate. Unlike syringe pumps, it not only pumps water forward, but also inhales from the back simultaneously, and can pump indefinitely. We developed a responsive and precise particle position control system using this pump in combination with a high-speed camera. In this system, the pumping pressure is operated by real-time adjustment of a pneumatic pressure supply to realize PID control. This approach moves the particle rapidly when it is far from a designated target position for a quick approach and slowly near the target position to position precisely. Full article

Cationic surfactants from the group of quaternary ammonium compounds (QACs) are widely used in disinfectants, cosmetics, and household and industrial products. Their strong antimicrobial activity and chemical stability make them valuable in applications but also highly persistent and toxic when released into aquatic environments. This problem has become increasingly relevant during and after the COVID-19 pandemic, when global use of QAC-based disinfectants increased drastically, resulting in their frequent detection in municipal, hospital, and industrial effluents. The concentrations of QACs reported in wastewater range from trace levels to several mg/L, often reaching inhibitory thresholds for biological treatment processes. Although surfactants are not listed in any current European directive, the revised Directive (EU) 2024/1440 classifies micropollutants as a priority group, imposing stricter environmental quality standards and mandatory monitoring requirements. Within this regulatory framework, QACs are recognized as compounds of emerging concern, and their effective removal from wastewater has become a critical challenge. This review summarizes the current knowledge on conventional treatment technologies (coagulation, adsorption, ion exchange, advanced oxidation, and biological processes) and membrane-based methods (ultrafiltration, nanofiltration, reverse osmosis, forward osmosis, and hybrid systems) for the removal of cationic surfactants from water and wastewater. Mechanisms of separation, performance, and operational limitations are discussed. Full article

Mobile mapping systems using LiDAR technology are becoming a reliable surveying technique to generate accurate point clouds. Mobile mapping systems integrate several advanced surveying technologies. This research investigated the development of a low-cost, accurate Microelectromechanical System (MEMS)-based INS/GNSS georeferencing system for LiDAR mobile mapping applications, enabling the generation of accurate point clouds. The challenge of using the MEMS IMU is that it is contaminated by high levels of noise and bias instability. To overcome this issue, new denoising and filtering methods were developed using a wavelet neural network (WNN) and an optimal maximum likelihood estimator (MLE) method to achieve an accurate MEMS-based INS/GNSS integration navigation solution for LiDAR mobile mapping applications. Moreover, the final accuracy of the MEMS-based INS/GNSS navigation solution was compared with the ASPRS standards for geospatial data production. It was found that the proposed WNN denoising method improved the MEMS-based INS/GNSS integration accuracy by approximately 11%, and that the optimal MLE method achieved approximately 12% higher accuracy than the forward-only navigation solution without GNSS outages. The proposed WNN denoising outperforms the current state-of-the-art Long Short-Term Memory (LSTM)–Recurrent Neural Network (RNN), or LSTM-RNN, denoising model. Additionally, it was found that, depending on the sensor–object distance, the accuracy of the optimal MLE-based MEMS INS/GNSS navigation solution with WNN denoising ranged from 1 to 3 cm for ground mapping and from 1 to 9 cm for building mapping, which can fulfill the ASPRS standards of classes 1 to 3 and classes 1 to 9 for ground and building mapping cases, respectively. Full article

Microplastics (MPs) pollution assessment must not pollute. Inspired by this catch phrase, we critically evaluated the environmental impact, safety, and effectiveness of various analytical strategies currently used to assess MPs contamination on sand. Density separation enables the isolation of MPs from sand. We highlighted the major drawbacks of using the standard high-density solutions. As we recognized there is room for greenness improvement in this hot research field, we considered 21 reagents able to provide high-density media. We aimed to put forward the green MPs determination protocol to be used in subsequent expert citizen science national campaign. The analytical workflow was optimized studying MPs contamination of composite sand specimens representatively sampled from a large beach-dune complex WWF oasis exposed to the effect of tourism in Venice (Italy). MPs have been quantified and characterized. We suggest calcium nitrate as the best trade-off reagent providing both greenness/safety and efficacy. Calcium nitrate can be upcycled from industrial waste streams according to the circular economy vision. Additionally, we critically reviewed all other critical steps of the MPs isolation to put forward a preeminent green, simple, reliable, and logical approach to the analysis of MPs in sand for expert citizen science campaigns. Full article

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are emerging contaminants of global concern, requiring sensitive and highly selective detection methods. Stringent demands imposed by the Environmental Protection Agency, with maximum contaminant levels set at 4.0 parts per trillion for PFAS individually in drinking water, are the primary driving force behind the development of novel sensors for PFAS. Pushing towards these ultra-low concentrations, however, reaches the limit of what can be reliably detected by field sensors, with PFAS optical and electrochemical inactivity, making it nearly impossible. Molecularly imprinted polymers and immunoassays offer the best chance of developing such sensors as they interact specifically with the active site, changing the optical or electrochemical response (fluorescence, impedance, voltage). Nanoparticulate metal oxides, carbon materials, including carbon dots, polymer coating, and MXenes have been put forward; however, several of these approaches have failed to achieve either the desired limit of detection, sensitivity, or selectivity. Here, we provide an overview of recent progress in nanomaterial-based PFAS sensors, with particular emphasis on strategies to enhance sensitivity, selectivity, and reliability in complex matrices. Finally, we outline key challenges and future perspectives toward robust, field-deployable PFAS sensing technologies. Full article

Food safety and quality are paramount global concerns, with the complexities of the modern supply chain demanding advanced technologies for monitoring, preservation, and decontamination. Conventional methods often fall short due to limitations in speed, sensitivity, cost, and functionality. Metal–organic frameworks (MOFs), a class of crystalline porous materials, have emerged as a highly universal platform to address these challenges, owing to their unprecedented structural tunability, ultrahigh surface areas, and tailorable chemical functionalities. This comprehensive review details the state-of-the-art applications of multifunctional MOFs across the entire spectrum of food safety and quality enhancement. First, the review details the application of MOFs in advanced food analysis, covering their transformative roles as sorbents in sample preparation (e.g., solid-phase extraction and microextraction), as novel stationary phases in chromatography, and as the core components of highly sensitive sensing platforms, including luminescent, colorimetric, electrochemical, and SERS-based sensors for contaminant detection. Subsequently, the role of MOFs in food preservation and packaging is explored, highlighting their use in active packaging systems for ethylene scavenging and controlled antimicrobial release, in intelligent packaging for visual spoilage indication, and as functional fillers for enhancing the barrier properties of packaging materials. Furthermore, the review examines the direct application of MOFs in food processing for the selective adsorptive removal of contaminants from complex food matrices (such as oils and beverages) and as robust, recyclable heterogeneous catalysts. Finally, a critical discussion is presented on the significant challenges that impede widespread adoption. These include concerns regarding biocompatibility and toxicology, issues of long-term stability in complex food matrices, and the hurdles of achieving cost-effective, scalable synthesis. This review not only summarizes recent progress but also provides a forward-looking perspective on the interdisciplinary efforts required to translate these promising nanomaterials from laboratory research into practical, real-world solutions for a safer and higher-quality global food supply. Full article

Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in fundamental limitations to their application for SSS monitoring. To address this issue, we propose a physics-informed neural network (PINN) approach that directly integrates radiative transfer physical processes into the neural network architecture for SMAP L2 SSS bias correction. This method ensures oceanographically consistent corrections by embedding physical constraints into the forward propagation model. The results demonstrate that PINN achieved a root mean square error (RMSE) of 0.249 PSU, representing a 5.3% to 8.5% relative performance improvement compared to conventional methods—GBRT, ANN, and XGBoost. Further temporal stability analysis reveals that PINN exhibits significantly reduced RMSE variations over multi-year periods, demonstrating exceptional long-term correction stability. Meanwhile, this method achieves more uniform bias improvement in contaminated nearshore regions, showing distinct advantages over the inconsistent correction patterns of conventional methods. This study establishes a physics-constrained machine learning framework for satellite SSS data correction by integrating oceanographic domain knowledge, providing a novel technical pathway for reliable enhancement of Earth observation data. Full article

Accurate prediction of river runoff is significant for flood control, water resource allocation, and basin ecological management. Despite the promise of integrating signal decomposition with deep learning, current decomposition-based hybrid models face critical forward data contamination: decomposition algorithms improperly access future test data in full-series applications, artificially inflating prediction accuracy. In contrast, the stepwise decomposition method currently proposed leads to high computational costs. To address this limitation, we introduce a novel framework integrating segmented decomposition sampling with a multi-input neural network. Specifically, a hybrid forecasting model combining Seasonal-Trend decomposition using Loess (STL) and Convolutional Long Short-Term Memory (CNN-LSTM) networks was implemented for daily runoff estimation. Method reliability was evaluated using historical runoff data from Huaxian Station in China’s Weihe River Basin, with comparative experiments conducted against established single and hybrid models. The results showed that the proposed framework can effectively avoid future information leakage and simultaneously improve prediction accuracy. For 1–3-day-ahead Nash-Sutcliffe efficiency (NSE) at Huaxian Station, the STL-CNN-LSTM model achieved values of 0.96, 0.83, and 0.80, respectively—representing improvements of 5.49%, 5.06%, and 12.68% over the VMD-CNN-LSTM model. This STL-based configuration outperformed the standalone LSTM counterpart by 23.08%, 9.21%, and 17.65% in NSE, respectively. Therefore, the proposed framework, which incorporates the segmented decomposition sampling method and a multi-input neural network, proves to be both practical and reliable. Full article

Botulinum neurotoxins (BoNTs), produced by the anaerobic spore-forming bacterium Clostridium botulinum, are among the most potent known biological toxins. BoNTs cause lethal botulism via contaminated food, wound infections, or infant intestinal colonization, posing significant threats to public health. Although the mouse bioassay is still being considered as the gold standard for detecting BoNTs, its drawbacks, including the lengthy experimental duration, high costs, and ethical issues, highlight the urgent need to develop alternative methods to fulfill the detection requirements. In recent years, frequent botulism poisoning incidents haves put forward higher requirements for detection technology. On-site detection is expected to be rapid and immediate, while laboratory detection requires high sensitivity and serotype discrimination capabilities. This review comprehensively introduces current detection approaches, including mouse bioassay, cell-based assays, immunological methods, endopeptidase–mass spectrometry, biosensors, chromatography, and mass spectrometry techniques. Notably, cell-based assays have been used for the potency testing of commercialized botulinum toxin type A and are considered the most promising alternative to the mouse bioassay. Biosensors based on nanomaterials demonstrate advantages in real-time detection due to their rapid response and portability, while endopeptidase–mass spectrometry achieves high sensitivity and effective serotype identification by specifically recognizing toxin-cleaved substrates. Future works shall aim to completely replace MBA, developing a detection system suitable for multiple scenarios such as clinical diagnosis, food safety monitoring, and environmental monitoring. The detection methods should also have matrix compatibility and serotype discrimination capabilities. Full article

As an important agricultural ecosystem, greenhouse gas (GHG) emissions and arsenic (As) mobilization in rice paddy fields have gained significant attention on climate change and food safety. There is a certain correlation between the GHG and As migration in rice paddy fields. The oxidation of methane in paddy fields can provide electrons for the reduction and release of arsenate. Nitrate in rice paddy soil can promote the fixation of As by oxidizing Fe (II) to form iron oxide–As complexes or directly oxidize As (III) to As (V) to reduce the toxicity of As. However, incomplete denitrification of nitrate can lead to the emission of N₂O. This review systematically expounds the research advances, influencing factors and simultaneous mitigation measures of GHG emissions and As mobilization in rice paddy fields. It focuses on discussing the influence mechanisms of soil physical and chemical properties, water management measures, fertilization methods, and the addition of soil conditioner on As migration and GHG emission, and it looks forward to future research directions. It aims to provide a theoretical basis and practical guidance for reducing the risk of As contamination in rice fields, reducing GHG emission, and achieving sustainable development of rice production. Full article

Organic and pharmaceutical pollution of water is a serious problem, particularly when it comes to drinking and groundwater. Although some evaluations indicate that these pollutants are unlikely to be at current exposure levels, they are often detected in aquatic systems and can be harmful to human health. Organic contaminants include hazardous micropollutants, aromatic phenols, pesticides, etc. Pharmaceutical contaminants are sulfamethoxazole, diclofenac, doxycycline, amoxicillin, trimethoprim, ciprofloxacin, norfloxacin, lipid regulators, nonsteroidal anti-inflammatory drugs (NSAIDs), hormones, antidepressants, etc. Hydrogel adsorbents’ distinct structural, chemical, and environmentally benign qualities make them a potential and successful option for environmental remediation, especially in wastewater treatment. In the search for clean water resources, they are an important instrument because of their reusability and capacity to be customized for certain contaminants, such as organic and pharmaceutical pollutants. This review focusses on the present state, adsorption sites and surfaces, different adsorption mechanisms, and the prospects and scope of improvement of effective hydrogels for eliminating dangerous aqueous organic and pharmaceutical contaminants. It offers a thorough summary of the area, highlighting its facets and potential paths forward. Full article

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, COD, Na⁺). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSE_WQI = 0.1205, RMSE_{CCME WQI} = 0.1305) and the highest determination coefficient (R²_WQI = 0.9916, R²_{CCME WQI} = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R² = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns. Full article

Produced water, a byproduct of oil and gas extraction, poses significant environmental challenges due to its complex composition and high salinity. Conventional treatment technologies often struggle to achieve efficient contaminant removal while maintaining long-term operational stability. Membrane-based separation processes, particularly forward osmosis (FO), offer a promising alternative due to their low hydraulic pressure requirements, high selectivity, and ability to mitigate fouling and scaling effects. This study fabricated and evaluated a novel dual-layer zwitterion-modified electrospun nanofibrous membrane for enhanced produced water (PW) treatment. The dual-layer design consists of a highly porous electrospun nanofibrous support layer for improved permeability and mechanical strength, coupled with a zwitterionic-modified selective layer to enhance antifouling properties and selective contaminant rejection. The zwitterionic surface modification imparts superior hydration capacity, reducing organic and biological fouling while improving water transport efficiency. The membranes are characterized using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, X-ray diffraction (XRD), contact angle and tensile strength measurements, and nuclear magnetic resonance (NMR) spectroscopy to assess their morphological, structural, and chemical properties. The performance evaluations demonstrated significantly higher water flux (up to 16.05 L m⁻² h⁻¹ for SPW (synthetic produced water) and 6.00 L m⁻² h⁻¹ for PW using NaBr) and excellent solid rejection (up to 96.02% for SPW and 88.90% for PW), reduced concentration polarization, and superior antifouling performance compared to conventional FO membranes. Experimental results from bench-scale trials demonstrate that this advanced membrane technology offers enhanced water recovery and contaminant removal efficiency, making it a viable solution for industrial-scale PW treatment and reuse. The findings underscore the potential of next-generation dual-layer FO membranes in promoting sustainable water resource management within the oil and gas sector while minimizing environmental impact. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

Magnetic nanoparticles (MNPs), especially iron oxide (Fe₃O₄), display distinctive superparamagnetic characteristics and elevated surface-area-to-volume ratios, facilitating improved physicochemical interactions with solutes and pollutants. These characteristics make MNPs strong contenders for use in water treatment applications. This research investigates the application of iron oxide MNPs synthesized via co-precipitation as innovative draw solutes in forward osmosis (FO) for treating synthetic produced water (SPW). The FO membrane underwent surface modification with sulfobetaine methacrylate (SBMA), a zwitterionic polymer, to increase hydrophilicity, minimize fouling, and elevate water flux. The SBMA functional groups aid in electrostatic repulsion of organic and inorganic contaminants, simultaneously encouraging robust hydration layers that improve water permeability. This adjustment is vital for sustaining consistent flux performance while functioning with MNP-based draw solutions. Material analysis through thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR) verified the MNPs’ thermal stability, consistent morphology, and modified surface chemistry. The FO experiments showed a distinct relationship between MNP concentration and osmotic efficiency. At an MNP dosage of 10 g/L, the peak real-time flux was observed at around 3.5–4.0 L/m²·h. After magnetic regeneration, 7.8 g of retrieved MNPs generated a steady flow of ~2.8 L/m²·h, whereas a subsequent regeneration (4.06 g) resulted in ~1.5 L/m²·h, demonstrating partial preservation of osmotic driving capability. Post-FO draw solutions, after filtration, exhibited total dissolved solids (TDS) measurements that varied from 2.5 mg/L (0 g/L MNP) to 227.1 mg/L (10 g/L MNP), further validating the effective dispersion and solute contribution of MNPs. The TDS of regenerated MNP solutions stayed similar to that of their fresh versions, indicating minimal loss of solute activity during the recycling process. The combined synergistic application of SBMA-modified FO membranes and regenerable MNP draw solutes showcases an effective and sustainable method for treating produced water, providing excellent water recovery, consistent operational stability, and opportunities for cyclic reuse. Full article