Search Results (17,005)

In response to the increasing demand for environmentally friendly and cost-effective adsorbents in wastewater treatment, this study reports the green synthesis, characterization, and application of a magnetic epichlorohydrin Rosa canina (m-ECH-RC) nanocomposite for removing Safranin O (SO), a commonly used cationic dye in textile effluents. The synthesized material was characterized using Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and zeta potential analyses to reveal its surface morphology, pore structure, functional groups, crystallinity, and colloidal stability. Adsorption performance was systematically tested under various conditions, including pH, adsorbent dose, contact time, ionic strength, and initial dye concentration. Kinetic analyses revealed that the adsorption process of Safranin O dye mainly obeys pseudo-second-order kinetics, but intraparticle and film diffusion also contribute to the process. As a result of the Isotherm analysis, it was found that the adsorption process conformed to the Langmuir model. Testing on real textile wastewater samples demonstrated a removal efficiency of 75.09% under optimized conditions. Reusability experiments further revealed that the material maintained high adsorption–desorption performance for up to five cycles, emphasizing its potential for practical use. These findings suggest that m-ECH-RC is a viable and sustainable adsorbent for treating dye-laden industrial effluents. Full article

Recently, researchers have been focused on the recycling as well as transforming of bio-waste streams into a valuable resource. Banana peels are promising for such application, due to their wide availability. In this context, the integration of banana peel-derived biochar with environmentally benign magnetite has significantly broadened its potential applications as a solar photocatalyst compared to the conventional photocatalysts. The materials are mixed in varied proportions of Ban-Char500-Mag@-(0:1), Ban-Char500@Mag-(1:1) and Ban-Char500@Mag-(2:1) and characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM) augmented with dispersive X-ray spectroscopy (EDX). Such modification is leading to an improvement in its application as a solar photocatalyst using the photochemical solar collector facility. The study discusses the factors controlling acetaminophen removal from aqueous effluent within 30 min of solar illumination time. Furthermore, the highlighted optimum parameters are pH 3.0, using 10 mg/L of the Ban-Char500@Mag-(1:1) catalyst and 100 mg/L of the hydrogen peroxide as a Fenton combination system for removing a complete acetaminophen from wastewater (100% oxidation). Also, the temperature influence in the oxidation system is studied and the high temperature is unfavorable, which verifies that the reaction is exothermic in nature. The catalyst is signified as a sustainable (recoverable, recyclable and reusable) substance, and showed a 72% removal even though it was in the six cyclic uses. Further, the kinetic study is assessed, and the experimental results revealed the oxidation process is following the first-order kinetic reaction. Also, the kinetic–thermodynamic parameters of activation are investigated and it is confirmed that the oxidation is exothermic and non-spontaneous in nature. Full article

Microplastics (MPs), as an emerging persistent contaminant, pose a potential threat to ecosystems and human health. The adsorptive removal of MPs from aqueous environments using magnetic nanoparticles has become a particularly promising remediation technology. Nevertheless, there remain significant knowledge gaps regarding its adsorption mechanism, especially how the key physical properties of magnetic nanoparticles regulate their adsorption behavior towards MPs. This study first investigated the relationship between the particle size of Fe₃O₄ nanoparticles and their adsorption efficacy for MPs. The results demonstrated a non-monotonic, size-dependent adsorption of MPs by Fe₃O₄ nanoparticles, with the adsorption efficiency and capacity following the order: 300 nm > 15 nm > 100 nm. This non-linear relationship suggested that factors other than specific surface area (which would favor smaller particles) are significantly influencing the adsorption process. Isotherm analysis indicated that the adsorption is not an ideal monolayer coverage process. Kinetic studies showed that the adsorption process could be better described by the pseudo-second-order model, while intra-particle diffusion played a critical role throughout the adsorption process. Furthermore, the effect of pH on adsorption efficiency was examined, revealing that the optimal performance occurs under neutral to weak acidic conditions, which is consistent with measurements of surface charges of nanoparticles. These findings suggest that the adsorption is not determined by specific surface area but is dominated by electrostatic interactions. The size-dependent adsorption of MPs by Fe₃O₄ nanoparticles provides new insights for the modification of magnetic adsorbents and offers a novel perspective for the sustainable and efficient remediation of environmental MPs pollution. Full article

This study explores the use of lignocellulosic Tectona grandis seeds (TGs), hydrochar (HC-230-4), and activated carbon (AC-850-5) produced via hydrothermal carbonization and followed by CO₂ activation for removing acridine yellow G (AYG) and acridine orange 14 (ABO) from water. HC-230-4 showed a rich presence of surface functional groups and irregular morphology with some sphere-like structures. In contrast, AC-850-5 exhibited a much higher surface area (729.7 m²/g), though with fewer surface functional groups than HC-230-4. The batch method was used to study the effects of contact time, pH, dye concentration, and temperature. Among the materials, AC-850-5 showed the highest adsorption capacity of 198 mg/g for AYG and 171 mg/g for ABO at 25 °C, around 12% higher than commercial activated carbon. The adsorption process was spontaneous and endothermic, fitting well to the Langmuir isotherm model, suggesting monolayer coverage. The adsorption kinetics followed the pseudo-second-order model, indicating that the rate depends on the surface site availability. Intraparticle diffusion analysis further confirmed a multi-step adsorption process. These findings show the strong potential of TG-derived activated carbon as an effective and sustainable material for removing acridine dyes from polluted water. Full article

Monitoring brain activity through electroencephalography (EEG) has led to significant advancements in the study of brain microstates and their relationship with cognitive processes, such as memory. Methods: A systematic literature review was conducted following the PRISMA methodology, with the aim of identifying and analyzing potential biomarkers of memory functions derived from EEG microstate analysis. Searches were performed in five major databases (PubMed, Scopus, Web of Science, Springer, and institutional registers), covering studies published between 2019 and 2024. The initial search retrieved 179 records; after removing duplicates and ineligible works, 18 full-text articles were evaluated. Finally, 10 original studies met the inclusion criteria. Although primarily focused on other pathologies or baseline conditions, these studies reported relevant findings related to memory processes. This allowed for an exploratory synthesis of the potential role of EEG microstates as indirect biomarkers of memory. Results: The findings revealed that microstates, particularly microstates C and D, show significant alterations in their duration, coverage, and occurrence in various pathologies, such as Alzheimer’s disease, schizophrenia, and attention disorders, highlighting their potential as noninvasive biomarkers. Conclusions: Although methodological variability across studies represents a limitation, this review provides a solid foundation for future research aimed at standardizing the use of EEG microstates in clinical applications, improving diagnostic accuracy in memory-related diseases. Overall, EEG microstates hold great promise in both neuroscientific research and clinical practice. Full article

Due to its efficiency, advanced oxidation processes (AOP), such as photo-Fenton, have become an alternative for removing emerging contaminants like paracetamol. The objective of this work was to perform a life cycle assessment (LCA) according to ISO 14040/44 for a heterogeneous photo-Fenton process catalyzed by Cu/Fe-pillared clays (PILC) for the removal of paracetamol from water. The study covered catalyst synthesis and four treatment scenarios, with inventories built from experimental data and ecoinvent datasets; treatment time was 120 min per functional unit. Environmental impacts for catalyst synthesis were quantified with ReCiPe 2016 (midpoint), while toxicity-related impacts of the degradation stage were assessed with USEtox™ (human carcinogenic toxicity, human non-carcinogenic toxicity, and freshwater ecotoxicity). Catalyst synthesis dominated most midpoint categories, the global warming potential for 1 g of Cu/Fe-PILC was 10.98 kg CO₂ eq. Toxicity results for S4 (photo-Fenton Cu/Fe PILC) showed very low values: 9.73 × 10⁻¹² CTUh for human carcinogenic and 1.29 × 10⁻¹³ CTUh for human non-carcinogenic. Freshwater ecotoxicity ranged from 5.70 × 10⁻⁴ PAF·m³·day at pH 2.7 (≥60 min) to 1.67 × 10⁻⁴ PAF·m³·day at pH 5.8 (120 min). Overall, optimizing pH and reaction time, are key levers to improve the environmental profile of AOP employing Cu/Fe-PILC catalysts. Full article

The rapid rise in atmospheric CO₂ concentrations has intensified the need for scalable, sustainable, and economically viable carbon sequestration technologies. This study introduces a cost-effective CaO/Ca(OH)₂-based mineralization process that not only enables efficient CO₂ removal but also allows the simultaneous recovery of high-purity calcite nanoparticles as value-added products. The process involves hydrating CaO, followed by controlled carbonation under optimized CO₂ flow rates, temperature conditions, and and additive use, yielding nanocrystalline calcite with an average particle size of approximately 100 nm. Comprehensive characterization using X-ray diffraction, transmission electron microscopy, and energy-dispersive X-ray spectroscopy confirmed a polycrystalline structure with exceptional chemical purity (99.9%) and rhombohedral morphology. Techno-economic analysis further demonstrated that coupling CO₂ sequestration with nanoparticle production can markedly improve profitability, particularly when utilizing CaO/Ca(OH)₂-rich industrial residues such as steel slags or lime sludge as feedstock. This hybrid, multi-revenue strategy—integrating carbon credits, nanoparticle sales, and waste valorization—offers a scalable pathway aligned with circular economy principles, enhancing both environmental and economic performance. Moreover, the proposed system can be applied to CO₂-emitting plants and facilities, enabling not only effective carbon dioxide removal and the generation of carbon credits, but also the production of calcite nanoparticles for diverse applications in agriculture, manufacturing, and environmental remediation. These findings highlight the potential of CaO/Ca(OH)₂-based mineralization to evolve from a carbon management technology into a platform for advanced materials manufacturing, thereby contributing to global decarbonization efforts. Full article

Point cloud processing of raw scan data is a critical step to enhance the accuracy and efficiency in computer-aided inspection and remanufacturing of damaged aero-engine blades. This paper presents a new methodology to obtain a noise-reduced and simplified dataset from the raw scan data while preserving the underlying geometry of the damaged blade in high-curvature and damaged regions. At first, outliers are removed from the scan data, and measurement noise is reduced through local least-squares quadric surface/plane fitting on the adaptive support domain of measured points under the measurement uncertainty constraint of inspection data. Then, a directed Hausdorff distance-based region growing scheme is developed to progressively search within the support domain of denoised data points to obtain a down-sampled dataset while preserving the local geometric shape of the surface. Numerical and experimental case studies have been conducted to evaluate the accuracy and computation time of scan-to-CAD rigid registration and CAD-to-scan non-rigid registration processes using the down-sampled dataset of damaged blades. The results have demonstrated that the proposed methodology effectively removes the measurement noise and outliers and provides a down-sampled dataset from the scan data that can significantly reduce the time complexity of the computer-aided inspection and remanufacturing process of the point cloud of damaged blades with a negligible loss of accuracy. Full article

Cellular senescence, a state of stable cell cycle arrest accompanied by a complex senescence-associated secretory phenotype (SASP), is a fundamental biological process implicated as a key driver of lung aging and lung age-related diseases (LARDs). This review provides a comprehensive overview of the rapidly evolving field of senotyping based on cellular heterogeneity in lung development and aging in health and disease. It also delves into the molecular mechanisms driving senescence and SASP production, highlighting pathways such as p53/p21, p16^INK4a/RB, mTOR, and p38 MAPK as therapeutic targets. The involvement of various novel SASP proteins, such as GDP15, cytokines/chemokines, growth factors, and DNA damage response proteins. We further highlight the effectiveness of senotherapeutics in mitigating the detrimental effects of senescent cell (SnC) accumulation within the lungs. It also outlines two main therapeutic approaches: senolytics, which selectively trigger apoptosis in SnCs, and senomorphics (also known as senostatics), which mitigate the detrimental effects of the SASP without necessarily removing the senescent cells. Various classes of senolytic and senomorphic drugs are currently in clinical trials including natural products (e.g., quercetin, fisetin, resveratrol) and repurposed drugs (e.g., dasatinib, navitoclax, metformin, rapamycin) that has demonstrated therapeutic promise in improving tissue function, alleviating LARDs, and extending health span. We discuss the future of these strategies in lung research and further elaborate upon the usability of novel approaches including HSP90 inhibitors, senolytic CAR-T cells, Antibody drug conjugate and galactose-modified prodrugs in influencing the field of personalized medicine in future. Overall, this comprehensive review highlights the progress made so far and the challenges faced in the field of cellular senescence including SnC heterogeneity, states of senescence, senotyping, immunosenescence, drug delivery, target specificity, long-term safety, and the need for robust cell-based biomarkers. Future perspectives, such as advanced delivery systems, and combination therapies, are considered critical for translating the potential of senotherapeutics into effective clinical applications for age-related pulmonary diseases/conditions. Full article

Pollution and freshwater scarcity, coupled with the energy sector’s continued dependence on fossil fuels, constitute a dual challenge to sustainable development. A promising response is biosystems that jointly address wastewater treatment and the production of renewable products. This review centers on a managed consortium of aquatic macrophytes and microalgae, in which the spatial architecture of plant communities, rhizosphere processes, and the photosynthetic activity of microalgae act in concert. This configuration simultaneously expands the spectrum of removable pollutants and yields biomass suitable for biorefinery, thereby linking remediation to the production of energy carriers and bioproducts within a circular bioeconomy. The scientific novelty lies in treating the integrated platform as a coherent technological unit, and in using the biomass “metabolic passport” to align cultivation conditions with optimal valorization trajectories. The work offers a practical framework for designing and scaling such consortia that can reduce the toxicological load on aquatic ecosystems, return macronutrients to circulation, and produce low-carbon energy carriers. Full article

Phosphorus-based compounds play a crucial role in agricultural productivity. However, excessive phosphorus discharge into water bodies contributes to eutrophication. This study proposes a circular approach for phosphorus recovery and reuse through the thermal valorization of Posidonia oceanica residues, an abundant marine biomass along Mediterranean coasts. After energy recovery from this waste (12.3 MJ kg⁻¹), the resulting ash was assessed as an effective adsorbent for aqueous phosphorus removal. Batch experiments were conducted to evaluate adsorption kinetics and equilibrium, considering the influence of key operational variables, such as temperature, pH, and adsorbent dosage. Under optimal conditions, the material achieved a maximum retention of approximately 55–60 mgP g⁻¹. The Freundlich model successfully describes the equilibrium isotherm data, indicating a heterogeneous adsorbent and an overall endothermic process. Phosphorus removal was favored at basic pH values (9.5–10.5), where the monohydrogen phosphate predominates. Leaching tests further revealed that saturated material releases phosphorus and other minerals in a manner clearly dependent on the final pH, with higher phosphorus release under more acidic conditions. These results suggest that Posidonia ash could serve as a low-cost adsorbent while also acting as a potential phosphorus source in soils. Full article

Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) to decompose and reconstruct the originally acquired acceleration signals, effectively eliminating high-frequency noise and significantly enhancing signal quality. After obtaining a purified acceleration signal, the study further refines the velocity signal based on the velocity characteristics at the beginning and end of the loading process, aiming to mitigate the influence of initial and boundary conditions on the velocity data. This process yields a highly accurate displacement time history curve. To validate the superiority of VMD in acceleration signal processing, a signal model test was conducted. Comparative experimental results demonstrate that the displacement time history curve derived from VMD-processed signals not only exhibits smaller relative errors and higher precision but also shows significant waveform improvements compared to curves obtained through direct integration of filtered signals. The research indicates that for marine pile foundations, using VMD to decompose and reconstruct the signals, and applying the continuous mean square error theory to identify the critical components of noise and effective signals has significant advantages in the processing of displacement signals using RLT. Compared with traditional analysis methods, the study successfully achieved the effective removal of high-frequency noise in the signal by applying the VMD technique to the decomposition and reconstruction of acceleration signals, significantly improving the quality of the signal. The assumption of zero pile head velocity before and after loading enables accurate determination of the actual pile head displacement Full article

Artificial intelligence-based image processing is critical for sensor fusion and image transformation in mobility systems. Advanced driver assistance functions such as forward monitoring and digital side mirrors are essential for driving safety. Degradation due to raindrops, fog, and high-dynamic range (HDR) imbalance caused by lighting changes impairs visibility and reduces object recognition and distance estimation accuracy. This paper proposes a diffusion framework to enhance visibility under multi-degradation conditions. The denoising diffusion probabilistic model (DDPM) offers more stable training and high-resolution restoration than the generative adversarial networks. The DDPM relies on large-scale paired datasets, which are difficult to obtain in raindrop scenarios. This framework applies the Palette diffusion model, comprising data augmentation and raindrop-removal modules. The data augmentation module generates raindrop image masks and learns inpainting-based raindrop synthesis. Synthetic masks simulate raindrop patterns and HDR imbalance scenarios. The raindrop-removal module reconfigures the Palette architecture for image-to-image translation, incorporating the augmented synthetic dataset for raindrop removal learning. Loss functions and normalization strategies improve restoration stability and removal performance. During inference, the framework operates with a single conditional input, and an efficient sampling strategy is introduced to significantly accelerate the process. In post-processing, tone adjustment and chroma compensation enhance visual consistency. The proposed method preserves fine structural details and outperforms existing approaches in visual quality, improving the robustness of vision systems under adverse conditions. Full article

An accelerometer mounting technique has large implications on the frequency range and accuracy of the measurement, with stiffness and the mass relative to the monitored structure as the primary concerns. The International Organization for Standardization (ISO) gives an extensive list in 5348:2021, detailing mounting methods, and provides recommendations for testing mounts that are not specifically defined. In the nuclear industry on the laboratory scale, there is a need for vibration measurements for predictive maintenance and process monitoring that are nondestructive and capable of working in high-temperature environments. Commercial adhesive products with easy application and removal were tested as nondestructive methods, while quick mounts to a commonly used aluminum frame were tested as nondestructive and have potential applicability in high-temperature environments. The sinusoidal excitation method was used, measuring frequencies from 50 Hz to 10 kHz in one-third octave band intervals, utilizing three accelerometers and comparing the results to those obtained with the stud-mounting method. Using the lowest ±3 dB threshold across each accelerometer, foam dots and poster strips were not successful, and foam tapes were accurate up to 2000 Hz, hose clamps and zip ties up to 800 Hz, and a custom 3D printed mount up to 1000 Hz. Knowing the limitations of each mounting technique allows for accurate measurements within the appropriate range. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article