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The increasing quantities of polluted waters are calling for advanced purification methods. Flocculation is an essential component of the water purification process, yet flocculation is commonly not optimal due to our poor understanding of the flocculation process. In particular, there is little knowledge on the mechanisms ruling the migration of pollutants during treatment. Here we have created the first tensor diagram, a mathematical framework for the flocculation process, analyzed its properties with a deep learning model, and developed a classification scheme for its relationship with pollutants. The tensor was constructed by combining pixel matrices from a variety of floc images, each with a particular flocculation period. Changing the factors used to make flocs images, such as coagulant dose and pH, resulted in tensors, which were used to generate matrices, that is the tensor diagram. Our deep learning algorithm employed a tensor diagram to identify pollution levels. Results show tensor map attributes with over 98% of sample images correctly classified. This approach offers potential to reduce the time delay of feedback from the flocculation process with deep learning categorization based on its clustering capabilities. The advantage of the tensor data from the flocculation process improves the efficiency and speed of response for commercial water treatment.

The present work reports application of auxetic yarns in development of abrasion resistant socks which can be used for better serviceability without shoes even on rough surfaces. Socks used in the current research were produced by using helical auxetic yarns which consist of different combinations of high performance and conventional materials e.g. Kevlar P-AR (para-aramid), cotton, elastomeric yarn, polyamide (PA6) and polyester with same linear density of 74 Tex. Three different types of fabrics were produced e.g. flat knit, sandwich half terry short and sandwich half terry long. For investigating the auxeticity, all samples were subjected to tensile loading and the resulting change in their thickness was measured. Abrasion resistance and other comfort related properties e.g. air permeability, vertical wicking and absorbency were also evaluated. The influence of different yarn combinations, twist levels and fabric types on abrasion resistance and breathability of socks were investigated by using fractional (half) factorial design. Statistical analysis was performed for the obtained results by using analysis of variance. Conventional socks samples were also produced in all fabric types for comparison with the auxetic materials. From the results it was concluded that the overall abrasion resistance of auxetic yarn-based sock is higher than conventional cotton sock. Results also revealed that abrasion resistance was increased by 46% in flat knit socks sample, 50% in sandwich half terry short sample and 58% in sandwich half terry long sample as compared to conventional cotton sock. In addition to this all the socks samples exhibited good comfort/breathability properties.

Black-odorous waters are an increasingly common phenomenon characterized by excessive levels of nutrients, the formation of metal sufide precipitates, volatile sulfurous compounds, low dissolved oxygen and high chemical oxygen demand. Black-odorous waters frequently occur in lake and river systems where inputs have restricted circulation. The key remediation issue is the removal of nitrogen and phosphorus. Here, we present a novel aeration-adsorption system using fiber balls and we study treatment parameters and removal mechanism. Kinetics and changes of the solid phase were followed using Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction. Results show complete removal of ammonia N, initially at 31 mg/L, and 92.8% removal of total nitrogen, initially at 29 mg/L, after a 24 h reaction time at pH 9.67. At pH 5.67 and 9.67, total phosphorus and phosphate could be significantly reduced by 90–92% at 3.2–5.2 mg/L after 24 h. Treatment met China’s integrated wastewater discharge standards, demonstrating an effective and robust treatment capability. First-order and second-order kinetic models provided a good fit to the treatment data, indicating physical and chemical adsorption were involved in the treatment reactions. The reaction mechanism involved hydrogen substitution and binding to oxygen. These results present a cost effective and robust approach for the removal of N and P from black, odorous water, providing opportunity to abate environmental contamination.

Conventional methods for water and wastewater treatment are energy-intensive, notably at the stage of coagulation–flocculation, calling for new strategies to predict pollutant reduction because the amount of energy consumed is related to how much of the pollutant is treated. Here we developed a model, named Bio-logic, inspired by ecosystems, where pollutants represent organisms, coagulants are food, and the wider environmental conditions are the living environment. Artificial intelligence was used to learn the biological behavior, which enabled an accurate prediction of the amount of pollutant reduction. Results show that pseudo-biological objects that have a strong affinity for biological food, such as turbidity, total phosphorus, ammonia nitrogen and the potassium permanganate index, induced a strong correlation, between measured pollutant consumption capacity and predicted values. For instance, R2 correlation coefficients are 0.97 for turbidity and 0.92 for the potassium permanganate index in the laboratory; and 0.99 for turbidity, 0.90 for total phosphorus, 0.75 for ammonia nitrogen and 0.63 for the potassium permanganate index in water treatment plants. Overall, our findings demonstrate that artificial intelligence can use the water Bio-logic model to predict the pollutant consumption capacity.

Unique 2D lamellar VS2/MoS2 heterojunctions boost reaction kinetics of Na+ and improve long cycle life for sodium storage.

Polyvinylidene fluoride (PVDF) fibrous membranes with fiber diameter from nanoscale to microscale were prepared by electrospinning. The structural parameters of PVDF fibrous membrane in terms of fiber diameter, pore size and its distribution, porosity or packing density, thickness, and areal weight were tested. The relationship between solution concentration and structural parameters of fibrous membrane was analyzed. The filtration performance of PVDF fibrous membrane in terms of air permeability and filtration efficiency was evaluated. The results demonstrated that the higher solution concentration led to a larger fiber diameter and higher areal weight of fibrous membrane. However, no regular change was found in thickness, porosity, or pore size of fibrous membrane under different solution concentrations. The air permeability and filtration efficiency of fibrous membrane had positive correlations with pore size. The experimental results of filtration efficiency were compared with the predicted values from current theoretical models based on single fiber filtration efficiency. However, the predicted values did not have a good agreement with experimental results since the fiber diameter was in nanoscale and the ratio of particle size to fiber diameter was much larger than the value that the theoretical model requires.

In this study, polypropylene meltblown nonwoven fabrics with different structure parameters such as fiber diameter, pore size, and areal density were prepared by the industrial production line. The morphology of meltblown nonwoven fibers was evaluated by using scanning electron microscope, and the diameter of fibers was analyzed by using image-pro plus software from at least 200 measurements. The pore size of nonwoven fabric was characterized by a CFP-1500AE type pore size analyzer. The filtration efficiency and pressure drop were evaluated by TSI8130 automatic filter. The results showed that the pressure drop of nonwoven fabrics decreased with the increase in pore size; the filtration efficiency and the pressure drop had a positive correlation with the areal density. However, when the areal density is in the range of 27–29 g/m2, both filtration efficiency and pressure drop decreased with the increase of areal density; when the areal density was kept constant, the filtration efficiency decreased as the pore size decreased; when the pore size of the meltblown nonwoven fabric is less than 17 μm, the filtration efficiency increased as the pore diameter decreased; when the pore diameter of the nonwoven fabric is larger than 17 μm. In a wide range, the pressure drop decreased as the fiber diameter decreased.

Chemical oxidation is a key technique used in dye wastewater treatment via the formation of hydroxyl radicals. To obtain optimal treatment effects, it is critical to understand the interaction of the molecular structure of the dye with the hydroxyl radical. We evaluated fluorescence excitation-emission matrix spectroscopy to study the decay of an azo-dye (Procion Red MX-5B) by a hydroxyl radical generated from catalytic Fe (III) on H2O2. Results showed that fluorescence signal reliably indicated the variations of the chemical groups and components during degradation, and the degradation could be divided into three stages: initial degradation (decolorisation), rapid intermediate degradation, and final degradation. Under control of uncorrected matrix correlation, the fluorescence fractions could be fitted successfully by parallel factor model (PARAFAC) model: two fluorescence components in initial degradation including mono substituted benzene and mono substituted naphthalene, three components as multi substituted benzene in rapid degradation, and no components could be resolved in the final degradation. The results from the study demonstrate the utility fluorescence characterization of dye degradation mechanisms and enhance the understanding of the degradation mechanisms.

Removal of dissolved organic matter (DOM) is a major problem in drinking water treatment, because DOM modifies color, taste and odor, and carcinogenic by-products of DOM can be created during disinfection. Efficient water remediation is limited by our poor knowledge of DOM structure and properties, thus calling for advanced methods of characterisation. Here, we tested the influence of two coagulants, polymeric ferric sulfate and cationic polyacrylamide, on the three-dimensional fluorescence excitation–emission wavelength spectra of DOM during coagulation. Results of the parallel factor model reveal 3 fluorophore components C1–3. Cationic polyacrylamide decreases the linear correlation between DOM and fluorescence, e.g., from 0.92 to 0.82 for C1 and from 0.84 to 0.79 for C2. The C3 component, a protein-like material, was the most affected. Findings demonstrate the need to consider residual polymer as a potential interference when using fluorescence to assess DOM removal by coagulation.

A kind of eco-friendly micro-encapsulated phase change materials (MEPCM) with dodecanol dodecanoate as core material were synthesized via the interfacial polymerization of toluene-2, 4-diisocyanate (TDI) and diethylenetriamine (DETA). During the preparation, a facile solvent free synthesis route was developed owing to the good compatibility of TDI and dodecanol dodecanoate. The synthesized MEPCM were characterized by Field Emission Scanning Electron Microscopy (FE-SEM), Particle Size Distribution (PSD) analyzer, Differential Scanning Calorimetry (DSC), Thermogravimetric/Infrared spectrometry (TG-IR) and thermal constants analyzer. It is found that the resultant MEPCM in nearly spherical shape are in the size scope from 10.0 to 40.0 μm, have the average latent heat in the range of 103.4–140.3 J/g, and exhibit high temperature resistance with an onset evaporation temperature at 234.0 °C. Moreover, the MEPCM possess good thermal conductivity from 0.17 W/m K to 0.21 W/m K. Therefore, it is confirmed that the prepared MEPCM can be regarded as a promising thermal energy storage material appropriate to the thermo-regulating fields such as renewable solar heating system.

Filtration is one of the important technologies for separating suspended particles. Under the condition of gravity compression, the filtration density can be increased and the separation effect of suspended particles can be improved. Considering the complex composition and the difficulty in degrading dye in industrial wastewater, a gravity compression aeration system with a modified polyester fibre ball (denoted as MPFB) was evaluated for the separation of dye from water. Congo red azo dye solution (0–40 mg/L) was selected as the model treatment compound. The MPFB was prepared by adjusting the concentrations of alkali (Quality score 0–25%), β-cyclodextrin (0∼80 g/L), reaction temperature (40–90°C), and silane coupler concentration (Concentration fractions 0–0.8%). We used Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) to characterise the MPFB. The separation was affected significantly by adsorption conditions such as MPFB dose and pH. The lower the MPFB dose, the higher the expected adsorption capacity. For the treatment of a dye solution at 500 mg/L, 100% removal was achieved with 48 g/L MPFB, at pH 8 during adsorption under non-circulation aeration. For 24 h of reaction, the system could reach the maximum adsorption capacity of 11.2 mg/g, which followed the pseudo-first order kinetics model and the intraparticle diffusion model. We discovered that circulation aeration provided the best adsorption and electrostatic and hydrogen bonding were the dominant components of adsorption. Overall, the system is a promising technology and has the potential to treat large volumes of dye wastewater.

Natural organic matter affect water environmental security and posed a potential threat to human health, and thus it has long been considered as a key index to evaluate water treatment performance. Dissolved organic nitrogen is one of the NOM, which produces some disinfection byproducts having more toxic than those carbon-based materials. Coagulation is a key unit of drinking water purification and has received wide attention. However, conventional flocculation technology on removal of DON is so poor that we have to seek more effective improving measurement. The combined use of conventional flocculant and organic polymer can improve treatment efficiency to a certain extent, and enhanced coagulation can also improve the DON removal rate, but their DON removal performance is still not dreamful. At present, there is a lack of systematic research on flocculation to remove DON. Although some achievements have been made, there is still a big gap between the preparation technology of flocculant and the goal of efficient removal of DON in water.For treatment of secondary effluent of industrial wastewater, some studies show that the use of Fe3O4 mainly has the effect of accelerating separation, but the adsorption effect is not good. However, with the synergistic flocculation of amino functionalized Fe3O4 it has a good effect on removing water protein, polysaccharide and humic acid, which can meet the water quality discharge standard and reduce the dosage of flocculant. The above results show that functional nanoparticle materials are of great significance to improve the adsorption and flocculation performance. Therefore, the functional modification of magnetic nanoparticles plays an important role.

A novel method for fabricating thermo-regulating alginate fiber by wet spinning from phase change material (PCM) microemulsions was proposed and carried out. In order to synthesize the PCM microemulsion successfully, different emulsifiers (alkylphenol polyoxyethylene ether (OP-10), sodium dodecyl sulfate (SDS) and their mixture) were added into the stock solution system. The solution systems with emulsifiers were observed under optical microscope and evaluated by using a differential scanning calorimeter (DSC); the results showed that only the solution system with the mixture of OP-10 and SDS transformed into PCM microemulsion, corresponding to the success of fiber formation by wet spinning. In addition, the microemulsion had a stable thermal property based on the DSC result, in which the latent heat capacity remained at 97.3% after 100 cycles of heating and cooling. The thermo-regulating alginate fiber was evaluated in terms of morphology, thermogravimetric (TG) analysis and differential scanning calorimetry. The results showed that the fiber had a smooth surface and porous structure in the cross-section, the bimodal TG curve of alginate fiber indicated that the PCM was successfully embedded into fiber and the DSC results demonstrated that the thermo-regulating alginate fiber had a comfortable phase change temperature of 25–35℃, and an acceptable phase change enthalpy of about 20 J/g.

The presented research work proposed a simple technique to metalize the milife fabric (very fine thickness) with silver coating. The polyester fabrics were firstly sensitized with copper, and then silver plating was performed on the surface. The presence of silver over the fabric was observed by scanning electron microscope and respective EDX spectra. The change in the electrical conductivity, electromagnetic shielding and joule heating properties were examined for different electroplating time. The electrically conductive fabrics showed low electrical resistance with high EMI shielding over frequency range of 30 MHz to 1.5 GHz. To have an idea about the durability of electrical conductivity under stretch load, the 75 cycles of stretch and release were carried out. Furthermore, the heating performance of silver plated fabric was studied through measuring the change in temperature at the surface of the fabric while applying a voltage difference across the fabric. Lastly, the effect of ageing parameters like washing, oxidation and sulfidation were examined on the functional and comfort properties (i.e. thermal conductivity and stiffness) of conductive fabric.

In drinking water plants, accurate control of flocculation dosing not only improves the level of operation automation, thus reducing the chemical cost, but also strengthens the monitoring of pollutants in the whole water system. In this study, we used feedforward signal and feedback signal data to establish a back-propagation (BP) model for the prediction of flocculant dosing. We examined the effect of the particle swarm optimization (PSO) algorithm and data type on the simulation performance of the model. The results showed that the parameters, such as the learning factor, population size, and number of generations, significantly affected the simulation. The best optimization conditions were attained at a learning factor of 1.4, population size of 20, 20 generations, 8 feedforward signals and 1 feedback signal as input data, 6 hidden layer nodes, and 1 output node. The coefficient of determination (R2) between the predicted and measured values was 0.68, and the root mean square error (RMSE) was lower than 20%, showing a good prediction result. Weak time-delay data enhanced the model accuracy, which increased the R2 to 0.73. Overall, with the hybridized data, PSO, and weak time-delay data, the new architecture neural network was able to predict flocculant dosing.

COVID-19, classified as SARS-CoV-2, is causing an ongoing global pandemic. The pandemic has resulted in the loss of lives and has caused economic hardships. Most of the devices used to protect against the transmission of the novel COVID-19 disease are related to textile structures. Hence, the challenge for textile professionals is to design and develop suitable textile structures with multiple functionalities for capturing viruses, passivating them, and, at the same time, having no adverse effects on humans during the complete period of use. In addition to manufacturing efficient, biocompatible, and cost-effective protective face masks, it is also necessary to inform the public about the benefits and risks of protective face mask materials. The purpose of this article is to address the concerns of efficiency and efficacy of face masks by primarily reviewing the literature of research conducted at the Technical University of Liberec. The main focus is on the presentation of problems related to the specification of aims of face mask applications, mechanisms of capture, durability, and modes of sterilization. The recommendations, instead of conclusions, are addressed to the whole textile society because they should be leading players in the design, creation, and proper treatment of face masks due to their familiarity with the complex behavior of textile structures and targeted changes of structural hierarchy starting from polymeric chains (nano-level) and ending in planar textile structures (millimeter level) due to action by mechanical, physical and chemical fields. This becomes extremely critical to saving hundreds of thousands of lives from COVID-19.

Fluorescence excitation-emission matrix spectroscopy (EEMs) has become a very popular technique in characterization of aquatic dissolved organic matter (DOM) coupled with a parallel factor (PARAFAC) model, denoted as (EEMs-PARAFAC). This research addresses the poorly researched relationship correlation between dissolved ions and fluorescence in a natural water environment. The relationship between the EEMs-PARAFAC components and ionic composition was studied in freshwater lakes, rivers, and seawater from locations in China. The natural water environment is different from a simulated environment having a fixed ionic composition. We used electrical conductivity (EC) to reflect the ionic strength as an indicator to evaluate the relationship in a series of water bodies. Results show that the EC generally had a positive correlation with DOM in natural water environment, but no correlation was found with water from the highly saline Yellow Sea. The Chaohu Lake samples contained one component having a significant negative correlation with EC, i.e., r 0.6, p 0.05, while other surface waters contained components having both positive and negative correlations (r 0.5, p 0.05). The negative correlation with EC also highlighted that humic acid-like components and protein-like materials (c1-c3) were positively correlated with DOM, while the protein-like component (c4) was negatively correlated with DOM. The EC equation proposed provided a good fit with the EC values of surface waters. The use of EC would be a useful and rapid method for analyzing the variation in the fluorescence component and its effect on water quality. This study highlights the need to account for variation in EC when assessing EEMs-PARAFAC of natural waters.