Search Results (118)

This work demonstrates the potential of polydimethylsiloxane sponges for removing organic UV filter compounds such as octinoxate from aqueous solutions. The sponges were fabricated using simple templates made of hydrophilic fused or pressed particles (sugar or NaCl salt) with an approximate particle size of 0.4 mm. Among the prepared sponges, those templated with sugar cubes or coarse salt exhibited the highest adsorption capacity, effectively adsorbing up to 0.6% of their own mass in octinoxate. The PDMS sponges were fully regenerable, allowing for the complete removal of octinoxate without any detectable changes in their adsorption properties or dry weight. Due to their simple fabrication, ease of handling, ability to float, and reusability, PDMS sponges present an environmentally friendly and low-maintenance alternative to conventional filtration systems for the removal of octinoxate and potentially other UV filter compounds from environmental surface waters and recreational water bodies. Full article

Due to the lack of sophisticated instruments for monitoring sludge dewatering performance in certain wastewater treatment plants, there is an urgent need to develop cost-effective and rapidly deployable technologies for assessing sludge dewaterability. This study proposed an image-based approach to evaluate sludge dewaterability. Flocculation images of sludge were captured using a smartphone under controlled conditions and processed via MATLAB for grayscale adjustment, contrast enhancement, and size standardization. Fractal image analysis was employed to justify the selection of floc area (rather than floc equivalent diameter) for downstream analyses. Significant correlations were observed between the number of different sludge floc area range and key dewaterability parameters: The number of flocs in area range of 10⁻⁶–10⁻⁵ cm² showed a negative correlation with capillary suction time (CST) (regression coefficient (R) = −0.511, probability (p) < 0.05) and a positive correlation with median particle size (R = 0.470, p < 0.05); the number of flocs in area range of 10⁻⁵–10⁻⁴ cm² exhibited a stronger negative correlation with CST (R = −0.538, p < 0.05) and a positive correlation with median particle size (R = 0.480, p < 0.05). Further validation experiments using a laboratory-scale diaphragm filter press demonstrated that when the proportion of the number of flocs in area range of 10⁻⁵–10⁻⁴ cm² relative to the total number of flocs for conditioned sludge fell below 70%, the dewatered sludge cake achieved a water content of less than 60%. This study highlights the feasibility of using commercially available smartphones as a practical tool for evaluating sludge dewaterability. Full article

This article presents research and a discussion on the proper use of filtration in optical measurements. Measurements were taken using a Werth multisensory machine using a Werth Zoom optical sensor. During optical measurements, the filtration option can be used. The manufacturer defines filters as “Dust”. They allow the machine operator to define the appropriate size depending on the type of inclusions or artifacts created in the production process. They can occur in processes such as punching on presses or production in the injection molding process of plastics. The presented research results and statistical analyses confirm the assumptions regarding the validity of using filters and their values. The use of filters with a higher value significantly affects the obtained results and forces the machine user to make a reasonable choice. Full article

Engineered bamboo has been considered a viable replacement for traditional wood and steel for structural and architectural purposes due to its renewable nature, high strength, and compatibility with different processing techniques. This systematic review analyzed the literature on the mechanical properties and processing techniques of engineered bamboo products, which include bamboo scrimber and laminated bamboo. The literature included in this systematic review was extracted from the Engineering Village platform. The studies retrieved from this platform were filtered to only have been published in top journals (Q1/Q2) related to engineering materials, materials science, and the construction industry. Using this methodology, from the initial 191 identified records, 51 studies that were the most relevant were chosen. The review revealed that bamboo scrimber has better performance for specific mechanical properties, which include its compressive, tensile, and bending strength. Laminated products had higher variability, which was often caused by the type of adhesive, orientation, and quality of adhesion. This study also identified the details of manufacturing processes, such as the adhesive systems, pre-treatment methods, and pressing conditions used. Moreover, the literature exhibited considerable inconsistencies in testing standards, reporting practices, and long-term durability evaluations. This review highlights these challenges and provides recommendations for future research to resolve these issues. Full article

This study proposes practical methods for estimating the moisture content of sludge, represented by the cake moisture, in the filter press dewatering process. Because the cake moisture and filtrate volume are difficult to measure directly, the proposed approaches utilize indirectly measurable data, including drain outlet images and the differential pressure during the compression phase. By analyzing the correlations between these parameters and the cake moisture, estimation models were developed using mathematical approximations. In the image-based approach, image processing techniques were applied to isolate the dewatered region, and the relationship between the pixel count and actual filtrate volume was analyzed to estimate the cake moisture based on the calculated filtrate volume per minute. In the pressure-based approach, two models were proposed: one that directly estimates the cake moisture from the differential pressure, and another that models the relationship among the differential pressure, filtrate volume, and cake moisture. Unlike complex machine learning techniques, the proposed methods employ simple and interpretable mathematical functions, offering both practicality and reliability. Validation using real-world operational data confirmed the accuracy and effectiveness of the proposed approaches. Full article

With growing global concerns over sustainable wastewater treatment, there is a pressing need for low-cost, eco-friendly filtration solutions. This study conducted a life cycle assessment (LCA) to evaluate the potential of improving slow sand filtration efficiency by integrating alternative materials like clay and oyster shell powder (OSP), while minimizing the environmental footprint. Additionally, the adaptability of three-dimensional (3D) printing was explored to incorporate these materials into innovative filter designs, assessing scalability for broader wastewater applications. Ten filter configurations, including a slow sand filter (SSF) enhanced with OSP (90:10) and 3D-printed clay–OSP bricks (ratios of 90:10, 85:15, 80:20), were assessed across three sourcing distances: local (in situ), regional (161 km), and distant (1609 km). The results showed that SSFs with OSP consistently delivered lower environmental impacts, reducing freshwater ecotoxicity, eutrophication, and human toxicity by up to 4% compared to conventional SSFs, particularly when transport was minimized. Among brick-based systems, single-brick columns offered the best balance of performance and impact, while three-brick columns had the highest environmental burden, largely due to the increased electricity use. Economic analysis reinforced the environmental findings: SSFs with OSP were the most cost-effective option, followed closely by SSFs, while brick-based systems were slightly more expensive, with costs rising sharply when sourcing distances exceeded 161 km. Overall, integrating OSP into SSFs offers an optimal balance of sustainability and affordability, while single-brick columns (90:10) present a promising alternative. Future research should further optimize material blends and design configurations to align with long-term environmental and economic goals. Full article

In response to the pressing need for long-range, non-contact detection in hazardous gas leakage monitoring within chemical industrial parks, this study proposes a gas detection algorithm based on an infrared radiation physical model that utilizes dual-band infrared radiation background reconstruction. The proposed method addresses the issues of the existing detection methods’ lack of physical model support. First, appropriate filter wavelength ranges are selected based on the absorption spectral characteristics of the target gas. Subsequently, a physical model incorporating atmospheric attenuation, background radiation, and gas absorption properties is established based on gas radiative transfer theory. The non-absorption band data are then employed to reconstruct the theoretical background radiation of the absorption band. Furthermore, leveraging the synergistic observation advantages of a dual-band infrared imaging system, gas morphology identification is achieved by inverting the difference between the theoretical background and the actual measured values in the absorption band. Experimental results demonstrate that this method enables gas morphology detection through background reconstruction without requiring pre-collected gas-free background images. By implementing dual-band infrared radiation background reconstruction, this study achieves effective gas detection, providing a reliable technical approach for real-time monitoring and early warning of industrial gas leaks. The proposed algorithm enhances detection capabilities, offering significant potential for applications in industrial safety and environmental monitoring. Full article

In this study, ultrasonic excitation was employed for filter cloth cleaning, with the aim of predicting optimal cleaning conditions and monitoring the efficiency and performance of the cloth under various cleaning parameters. A clogged filter cloth of uniform size (Φ0.11 m) was secured in a prepared cleaning apparatus, and cleaning experiments were conducted by varying the following operational conditions: time (2, 5, 10 min), frequency (34, 76, 120 kHz), and power output (100, 200, 300 W). Through these experiments, this study sought to investigate the cleaning capacity and efficiency of each condition and to evaluate the effectiveness of ultrasonic cleaning. The morphology of the filter cloths before and after cleaning was examined through SEM imaging, and the weight content of the filter cloths was measured before and after the cleaning experiments to incorporate these values into the cleaning efficiency assessment. Additionally, air permeability measurements were taken to predict the impact of permeability on cleaning performance, which was statistically analyzed based on a predictive model’s equation. The experimental results showed that the maximum recovery rate of air permeability for clogged filter cloths was approximately 28.6%. Using Response Surface Methodology (RSM), the air permeability recovery rate and weight reduction rate were 19.8% and 5.8%, respectively, under conditions of 5.2 min, 34 kHz, and 300 W. It is anticipated that the utilization of the filter press cleaning device will enable data acquisition through repeated experiments and that this device can be used in filter cloth management and operational techniques. Full article

Efficient solid–liquid separation is crucial in industries like mining, but traditional chamber filter presses depend heavily on manual monitoring, leading to inefficiencies, downtime, and resource wastage. This paper introduces a machine learning-powered digital twin framework to improve the operational flexibility and predictive control of a traditional chamber filter press. A key challenge addressed is the degradation of the filter medium due to repeated cycles and clogging, which reduces filtration efficiency. To solve this, a neural network-based predictive model was developed to forecast operational parameters, such as pressure and flow rates, under various conditions. This predictive capability allows for optimized filtration cycles, reduced downtime, and improved process efficiency. Additionally, the model predicts the filter medium’s lifespan, aiding in maintenance planning and resource sustainability. The digital twin framework enables seamless data exchange between filter press sensors and the predictive model, ensuring continuous updates to the training data and enhancing accuracy over time. Two neural network architectures, feedforward and recurrent, were evaluated. The recurrent neural network outperformed the feedforward model, demonstrating superior generalization. It achieved a relative $L^2$-norm error of $5\%$ for pressure and $9.3\%$ for flow rate prediction on partially known data. For completely unknown data, the relative errors were $18.4\%$ and $15.4\%$, respectively. Qualitative analysis showed strong alignment between predicted and measured data, with deviations within a confidence band of $8.2\%$ for pressure and $4.8\%$ for flow rate predictions. This work contributes an accurate predictive model, a new approach to predicting filter medium cycle impacts, and a real-time interface for model updates, ensuring adaptability to changing operational conditions. Full article

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R² = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications. Full article

Informational divergence emerged as a significant phenomenon during the COVID-19 health crisis. This period was characterized by information overload and changes in the communication of public health recommendations and policies by authorities and media outlets. This study examines the impact of such divergence on the population’s mental health, focusing on primary emotions expressed in comments across digital ecosystems. A media EMIC approach was used to analyze digital ecosystems during March and April 2020. Data were collected from Twitter, YouTube, Instagram, official press websites, and internet forums, yielding 3,456,387 communications. These were filtered to extract emotion-expressing content, resulting in 106,261 communications. Communications were categorized into primary emotions (anger, disgust, joy, fear, and sadness) using an exclusionary emotion assignment procedure. Analysis techniques included polarity and term frequency calculation, content analysis using Natural Language Understanding, emotion intensity measurement using IBM Watson Analytics, and data reliability assessment using the ISMA-OWA operator. The findings suggest that exposure to informational divergence from governments, health organizations, and media negatively affected mental health, evidenced by sadness, fear, disgust, and anger, which are associated with elevated levels of stress, anxiety, and information fatigue. In contrast, information perceived as reflecting coordination, support, and solidarity elicited positive emotional responses, particularly joy. Full article

Copper nanowires (Cu NWs) are considered a promising alternative to indium tin oxide (ITO) and silver nanowires (Ag NWs) due to their excellent electrical conductivity, mechanical properties, abundant reserves, and low cost. They have been widely applied in various optoelectronic devices. In this study, Cu NWs were synthesized using copper chloride (CuCl₂) as the precursor, monoethanolamine (MEA) as the complexing agent, and hydrated hydrazine (N₂H₄) as the reducing agent under strongly alkaline conditions at 60 °C. Notably, this is the first time that MEA has been employed as a complexing agent in this synthesis method for Cu NWs. Through a series of experiments, the optimal conditions for the CuCl₂–MEA–N₂H₄ system in Cu NWs synthesis were determined. This study revealed that the presence of amines plays a crucial role in nanowire formation, as the co-ordination of MEA with copper in this system provides selectivity for the nanowire growth direction. MEA prevents the excessive conversion of Cu(I) complexes into Cu₂O octahedral precipitates and exhibits an adsorption effect during Cu NWs formation. The different adsorption tendencies of MEA at the nanowire ends and lateral surfaces, depending on its concentration, influence the growth of the Cu NWs, as directly reflected by changes in their diameter and length. At an MEA concentration of 210 mM, the synthesized Cu NWs have an average diameter of approximately 101 nm and a length of about 28 μm. To fabricate transparent conductive films, the Cu NW network was transferred onto a polyethylene terephthalate (PET) substrate by applying a pressure of 20 MPa using a tablet press to ensure strong adhesion between the Cu NW-coated mixed cellulose ester (MCE) filter membrane and the PET substrate. Subsequently, the MCE membrane was dissolved by acetone and isopropanol immersion. The resulting Cu NW transparent conductive film exhibited a sheet resistance of 52 Ω sq⁻¹ with an optical transmittance of 86.7%. Full article

The harmful effects of solar radiation on the skin are known and scientifically proven, with recent studies indicating that not only ultraviolet (UV) radiation but also infrared (IR) radiation contributes to skin photoaging and increases the risk of carcinogenesis. Infrared radiation is also responsible for the degradation of protective carotenoids in the skin, the disruption of calcium homeostasis, and the activation of apoptosis pathways. The biological mechanisms underlying these effects include an increased level of reactive oxygen species and increased expression of metalloproteinases in the skin. The aim of this study was to evaluate the photoprotective properties of 10 cold-pressed plant oils in the infrared spectral range from 1000 nm to 2500 nm by assessing their impact on the directional–hemispherical reflectance (DHR) of human skin after their topical application. This study was conducted in vivo on the skin of 12 volunteers, with non-invasive DHR measurements taken before and directly after the application of the oil and 30 min later. Additionally, the correlation between the oil’s compounds (chlorophyll a, chlorophyll b, lycopene, and β-carotene) and antioxidant activity, expressed as the DPPH free radical scavenging capacity, was analyzed in relation to the differences in the skin’s DHR observed. An interesting result was obtained in the context of protecting the skin against IR radiation. A statistically significant increase in the skin’s reflectance after the penetration of the oil (p < 0.05) was observed in the 1700–2500 nm range for the chokeberry, fig, pomegranate, and perilla oils, suggesting their potential as photoprotective agents against IR. These findings indicate that chokeberry, fig, pomegranate, and perilla oils may serve as ingredients in cosmetic formulations designed for broad-spectrum skin photoprotection, complementing traditional UV filters with additional protection against infrared radiation. However, further research is needed to confirm these findings in a larger population. Full article

Current technological progress in automation and robotics allows human kinematics to be used to control any device. As part of this study, a sensory glove was developed that allows for a delta robot to be controlled using hand movements. The process of controlling an actuator can often be problematic due to its complexity. The proposed system solves this problem using human–machine interactions. The sensory glove allows for easy control of the robot by detecting the rotation of the hand and pressing the control buttons. Conventional buttons have been replaced with SMART materials such as conductive thread and conductive fabric. The ESP32 microcontroller placed on the control glove collects data read from the MPU6050 sensor. It also facilitates wireless communication with the Raspberry Pi microcontroller supporting the Modbus TCP/IP protocol, which controls the robot’s movement. Due to the noise of the data read from the gyroscope, the signals were subjected to a filtering process using basic recursive filters and an advanced algorithm with Kalman filters. Full article

This study explored how combining supercritical fluid extraction (SFE) and enzymatic hydrolysis influences the structure and functionality of peptides recovered from filter-pressed shrimp waste. Freeze-dried press cake (PC) was defatted via SFE and hydrolyzed using Alcalase (ALC) and trypsin (TRYP). ALC-treated PC achieved the highest protein recovery (63.49%), extraction yield (24.73%), and hydrolysis degree (18.10%) (p < 0.05). SFE-treated hydrolysates showed higher zeta potential (−47.23 to −49.93 mV) than non-SFE samples (−25.15 to −38.62 mV) but had larger droplet sizes, indicating lower emulsion stability. SC-ALC displayed reduced fluorescence intensity and a red shift in maximum wavelength. TRYP hydrolysates reduced interfacial tension (20 mN/m), similar to sodium caseinate (Na-Cas, 13 mN/m), but with lesser effects. Dilatational rheology showed TRYP hydrolysates formed stronger, solid-like structures. These results emphasize protease efficacy over SFE for extracting functional compounds, enhancing shrimp waste valorization. Full article