Search Results (315)

Aerobic ceramic membrane bioreactors (AeCeMBR) have shown great potential in treating wastewater (WW) from the textile industry; however, their operation faces challenges such as process variability, membrane contamination, and the need for accurate prediction of treated water quality under varying conditions. In this study, chemical oxygen demand (COD) and turbidity were selected as key indicators, as they directly reflect organic load removal and solids separation efficiency in MBR systems. The effect of four operational parameters: hydraulic retention time (HRT), organic loading rate (OLR), mixed liquor suspended solids (MLSS), and transmembrane pressure (TMP), was investigated using a response surface methodology (RSM) based on a Box–Behnken design. A random forest (RF) model coupled with Monte Carlo simulation (MC) was also developed using 174 experimental data points to enhance predictive power and quantify uncertainty. The RSM model showed strong agreement with experimental results (coefficient of determination (R²) > 0.95), achieving approximately 96% removal for both COD and turbidity, with validation errors of less than 2%. MC simulation (10,000 iterations) was applied to assess the effect of ±10% variance under operating conditions, providing a probabilistic view of system performance. The RF-MC framework demonstrated high predictive accuracy, with strong correlations between predicted and observed values (R² = 0.92 for COD and 0.97 for turbidity) and low uncertainty. Overall, this study proposes an integrated RSM, RF–MC approach for AeCeMBR systems, providing a robust and uncertainty-aware framework for process optimization and performance prediction under changing operating conditions. Full article

Textile dyes are considered emerging pollutants due to their recalcitrant and xenobiotic nature, making them toxic and mutagenic, leading to various environmental impacts. Aspergillus fungi, known for their metabolic diversity and high environmental adaptability, emerge as an alternative for remediating these contaminants. The evolution and trends in research on the application of Aspergillus in the bioremediation of textile dyes were assessed through a scientometric analysis of articles indexed in Web of Science, PubMed, and Scopus, using the Bibliometrix tool. A total of 283 documents were identified over 28 years since the first publication, indicating that although the topic is established, research output remains limited. Publications originated from 43 countries, with India as the leading contributor; however, the low rate of international collaboration (12.37%) highlights the need for stronger global partnerships. Research primarily focused on dye decolorization via biosorption and biodegradation, with Aspergillus niger and Aspergillus flavus as the most frequently studied species. Recent trends emphasize lignolytic enzymes, especially laccase, and integrative approaches combining biological and physicochemical processes. The results also reveal the urgent need for comprehensive toxicological assessments beyond phytotoxicity, considering increasing concerns about textile effluent impacts on ecosystems and human health. Full article

The removal of recalcitrant industrial dyes from wastewater has emerged as a critical environmental challenge, particularly in the context of the accelerating decline of global freshwater reserves. Given that these contaminants originate predominantly from the effluents of textile, chemical, and related manufacturing sectors, the deployment of advanced treatment technologies prior to discharge is imperative to mitigate their ecological impact. This study investigates the photocatalytic degradation of indigo carmine using a synthesized MgAl–LDH material. LDH is shown to act as an active photocatalytic component rather than a support, with its remarkably simple synthesis offering a practical alternative to the complex catalysts dominating the current literature. The catalyst’s structural, morphological, and surface characteristics were comprehensively validated through XRD, SEM, EDX, and BET analyses. The catalyst was evaluated under varying hydrogen peroxide doses and across an initial dye concentration range of 5 × 10⁻⁵ to 5 × 10⁻⁴ M. Increasing the H₂O₂ volume (3.5–20 mL, corresponding to H₂O₂ excess ratios of 17.5–100) significantly enhanced the oxidation rate, whereas higher dye concentrations reduced efficiency due to photon competition and partial saturation of catalytic sites. These experiments provided the basis for extracting kinetic parameters and assessing the mechanistic pathways governing the photocatalytic process. The kinetic behavior of indigo carmine degradation was evaluated by fitting the experimental data to zero-order, first-order, and second-order empirical models to identify the rate law that best describes the reaction. Reusability tests showed that MgAl–LDH maintains high activity over multiple cycles, with only a moderate decline, demonstrating its stability and suitability for practical wastewater treatment applications. Full article

With the increasing generation of textile waste, efficient chemical recycling methods are urgently needed. This study evaluates a hydrochloric acid-based process for recycling polycotton textiles (polyester/cotton blends), in which cotton is selectively hydrolyzed and converted into 5-(chloromethyl)furfural (CMF), while polyester is recovered. The impact of common non-polycotton fiber contaminants on process performance and product quality was systematically assessed. Cellulose-based fibers did not hinder the process and are suitable for CMF production, while most synthetic fibers were effectively removed without affecting the CMF yield. In contrast, animal fibers reduced the CMF yield and complicated acid recovery, indicating they should be avoided in the feedstocks. Additionally, polyacrylonitrile and wool persisted in the solid fraction, contaminating the recovered polyester and lowering its value. To improve process robustness and product quality, intermediate filtration and extended hydrolysis time are recommended. These findings highlight critical feedstock requirements and operational adjustments for scalable polycotton recycling. Full article

Copper cobalt sulfide (Cu-CoS₂) nanoparticles (NPs) were synthesized via the co-precipitation method in the present study. The synthesized nanoparticles were employed as photocatalysts for the degradation of two hazardous dyes, Eosin B (EB) and Rhodamine B (RB), under sunlight irradiation. The synthesized nanoparticles were characterized using Energy Dispersive X-ray spectroscopy, Scanning Electron Microscopy, UV-Visible spectroscopy, Fourier Transform Infrared spectroscopy, and X-ray Diffraction analysis. The calculated optical band gap of Cu-CoS₂ was 2.06 eV, while the point of zero charge (PZC) was determined to be 7. The XRD results confirmed the crystalline nature of the Cu-CoS₂ nanoparticles with an average crystallite size of 28.23 nm. The catalyst exhibited higher photocatalytic degradation efficiency for EB than for RB in single-dye solutions. In contrast, the presence of EB in the binary dye mixture did not significantly influence the degradation of RB. The effects of various operational parameters, including dye concentration, pH, temperature, and catalyst dosage, were systematically investigated. The photocatalytic degradation efficiency of both dyes decreased with increasing initial dye concentration. Optimum degradation conditions for both single and binary dye systems were obtained at dye concentrations of 40:20 μM, pH 5 for EB, pH 9 for RB, and a temperature of 50 °C. The maximum degradation efficiencies achieved in single-dye solutions were 97% for RB and 92% for EB, whereas degradation efficiencies of 98% for RB and 82% for EB were observed in binary dye systems. Furthermore, first-order and second-order kinetic models were applied to evaluate the photodegradation process, and the experimental data showed better agreement with the second-order kinetic model. Full article

Green synthesis of KGM-capped CeO₂ nanoparticles was successfully achieved through a simple coprecipitation method using Konjac Glucomannan (KGM) as a biopolymeric capping and stabilizing agent. The reaction conditions were optimized by varying pH (9–11) and temperature (30–70 °C) to evaluate their influence on nanoparticle formation and photocatalytic performance. The synthesized KGM–CeO₂ nanoparticles were comprehensively characterized using FTIR, UV–Vis spectroscopy, XRD, SEM–EDS, TEM, DLS, and ZP analysis to investigate their structural, optical, morphological, and surface properties. The characterization results confirmed the successful formation of porous sponge-like branched CeO₂ nanostructures with irregular morphology. XRD analysis revealed the crystalline nature of the nanoparticles with an average crystallite size of approximately 7.7 nm, while DLS analysis showed an average hydrodynamic particle size of 29.7 nm with a biomodal particle size distribution. The positive zeta potential value (+16.75 mV) confirmed good colloidal stability and reduced agglomeration due to effective capping by KGM. The synthesized nanoparticles also exhibited favorable optical properties with band gap values suitable for photocatalytic applications. The adsorption and photocatalytic degradation performance of the KGM–CeO₂ nanoparticles was investigated against synthetic textile dyes, including Naphthol Blue Black (NBB), Methyl Orange (MO), and a mixed NBB–MO dye system under acidic conditions. Using an adsorbent dosage of 50 mg and dye concentrations of 100 mg/L, the material achieved degradation efficiencies of approximately 99% for NBB, 91% for MO, and 52% for the mixed dye system under UV irradiation for 120 min. Adsorption kinetic studies indicated that the pseudo-second-order model provided the best fit, suggesting that chemisorption is the dominant adsorption mechanism involving multifunctional surface interactions. These findings are particularly relevant for industrial wastewater treatment, since actual textile effluents typically contain complex mixtures of dyes and organic contaminants rather than single dye pollutants. The mixed dye experiments, therefore, provide a more realistic simulation of industrial wastewater conditions. Overall, the synthesized KGM–CeO₂ nanoparticles demonstrate excellent potential as an eco-friendly, cost-effective, and sustainable multifunctional material for adsorption-assisted photocatalytic treatment of dye-contaminated wastewater. Further optimization of operational conditions and catalyst surface properties may enhance its efficiency in multicomponent wastewater systems. Full article

The textile industry contributes significantly to environmental pollution through massive water usage and toxic synthetic dye effluents. Bioremediation offers a sustainable solution by using microorganisms, such as bacteria, to transform complex contaminants into simpler substances. This study evaluated the bioremediation potential of fifteen halotolerant endophytic bacteria isolated from black beans (Phaseolus vulgaris L.) against various textile dyes. The strains included Bacillus cereus, Bacillus amyloliquefaciens, Priestia megaterium, and Staphylococcus warneri. Initial screenings across different TSA (Tryptic Soy Agar) medium concentrations (10%, 50%, 100%) revealed that bacterial growth and discoloration—assessed via halo formation—were most pronounced in 50% medium. While several dyes showed no reaction, Malachite Green and Congo Red were successfully decolorized. In liquid medium assays TSB (Tryptic Soy Broth) (50%) quantitative analysis via spectrophotometry showed that strains PV57, PV107, and PV112 achieved approximately 45% discoloration for Congo Red. Most notably, PV18 and PV114 achieved discoloration efficiencies of 91.69% and 88.72%, respectively, for Malachite Green after 72 h. These findings indicate that salt-tolerant endophytic bacteria are promising candidates for the decolorization of textile dyes. However, further studies are required to determine whether the observed discoloration results from biodegradation, biotransformation, or biosorption. This study underscores the potential of agricultural endophytes in managing industrial waste effectively. Full article

Valorization of agro-industrial waste into functional materials is fundamental to the circular economy, especially for addressing the persistent contamination by anionic azo dyes in textile wastewater. This study evaluates guava seeds modified with hexadecyltrimethylammonium bromide (GS-M) as low-cost biosorbents for the removal of Direct Blue 71 (DB71), comparing their performance with that of natural seeds (GS-N) in batch systems and fixed-bed columns. Characterization by infrared spectroscopy (FTIR) and electron microscopy (SEM-EDS) confirmed successful surfactant immobilization, thereby creating a cationic surface with strong electrostatic affinity for anionic dye molecules. Batch experiments showed that GS-M achieved 98% DB71 removal within 120 min, whereas GS-N reached only 58% after 300 min. For GS-M, both pseudo-first-order and pseudo-second-order models fit the kinetic data well, consistent with concurrent electrostatic and hydrophobic interactions; GS-N was best described by the Elovich model, indicating rate limitation by electrostatic repulsion. GS-M maintained removal efficiency above 84% across pH 3–9, whereas GS-N was effective under acidic conditions. Langmuir maximum adsorption capacity (Q_o) values for GS-M were 6.02 mg/g at pH 4 and 7.87 mg/g at pH 8, a 1.5- to 2.2-fold increase over GS-N under matched conditions. Three adsorption–desorption cycles retained ~49% of the initial GS-M capacity, supporting a short-cycle reuse profile rather than indefinite multi-cycle operation. Fixed-bed column performance was highly sensitive to the hydraulic loading rate (v_c), with breakthrough times increasing nearly eightfold as v_c decreased. The Bed Depth Service Time (BDST), Thomas, and Yoon–Nelson models described the dynamic data consistently, yielding a maximum dynamic capacity of 165.6 mg/L under optimal conditions and providing a quantitative basis for scale-up. These results establish surfactant-modified guava seeds as a low-cost, pH-resilient biosorbent system aligned with circular-economy principles for the sustainable remediation of textile wastewater. Full article

Water contaminated by textile dyes is a tremendous risk to human health and the environment due to its toxic and carcinogenic nature, thus requiring advanced and efficient removal strategies. Therefore, this study aimed to investigate the photo-oxidation performance of few- and multi-layer Ti₃C₂T_x nanosheets (MXenes) decorated with TiO₂ nanoparticles for methyl orange removal from synthetic solutions. The quantification of photogenerated hydroxyl radicals by fluorescence revealed much higher OH^• production for TiO₂-decorated samples, especially for multi-layer MXene, in which it was 2.8 times higher than that of few-layer MXene. However, photocatalysis was morphology-controlled: despite lower OH^•, the few-layer MXene achieved the highest dye conversion (~45% after 5 h), attributed to shorter charge migration distances and more accessible TiO₂ active sites, enabling effective h⁺ and superoxide-driven pathways. Moreover, the detected -OH surface terminations verified on MXenes promoted a notable adsorption capacity, especially for the multi-layer samples (~31%) via interlayer trapping and H-bonding. Therefore, our results demonstrate that few-layer MXenes are promising candidates for the efficient removal of methyl orange and highlight the potential of TiO₂-decorated MXenes as promising photocatalysts for environmental remediation. Full article

The widespread release of microplastics (MPs), especially fibrous microplastics (FMPs) originating from synthetic textiles, poses a growing threat to environmental systems due to their persistence, mobility, and potential for bioaccumulation in aquatic and terrestrial ecosystems. Conventional gravimetric methods (GMs) remain the primary approach for assessing FMP shedding, yet they are hindered by moisture-sensitive filters, false positives from detergents and minerals, environmental contamination, and the labor-intensive manual measurement of individual fibers. To address these limitations, we developed an automated image analysis (AIA) framework that integrates an attention-based U-Net architecture with meta-learning modules to quantify FMP number, length, diameter, and mass from stitched microscopic images of entire filter membranes. This approach enables detection of fibers down to 28 μm in diameter with the spatial resolution of 2.17 µm/pixel, supports both target-color and multi-color analysis, and eliminates the need for manual characterization or extrapolation from partial membrane segments. The method achieved the highest accuracy of approximately 98% in color-specific fiber detection, correctly identifying 257 of 263 white fibers, and demonstrated similarly robust performance for black, red, and green fibers, while minimizing interference from non-target colors, even when their fibers overlapped. Multi-color detection was further validated using effluent water samples containing mixed-color fibers. Overall, the developed system enhances the accuracy, efficiency, and reproducibility of FMP analysis, offering a standardized and scalable approach for environmental monitoring of MP pollution. Full article

Surface stain contamination poses a critical barrier to the automated, high-precision fiber identification required for industrial-scale waste textile recycling. In this study, a dataset comprising 120 physical specimens (yielding 1200 regions of interest, ROIs) across 12 contamination categories was constructed by contaminating cotton, polyester, and poly-cotton blend textiles with carbon black, protein, and oil stains. The spectral interference effects of stains—including baseline drift and spectral overlapping induced by physical shielding and chemical absorption—were systematically analyzed. To identify the optimal classification pipeline, three mathematical preprocessing methods (First Derivative, FD; Standard Normal Variate, SNV; and Multiplicative Scatter Correction, MSC) were evaluated alongside Support Vector Machine (SVM) and One-Dimensional Convolutional Neural Network (1D-CNN) models. Results show that among the SVM-based pipelines, the FD-SVM model effectively resolves overlapping absorption peaks, achieved an average accuracy of 98.17% ± 1.33%, but remains highly dependent on mathematical preprocessing. In contrast, the 1D-CNN model employing a progressive stacking architecture of multi-scale convolutional kernels attains a highly robust mean accuracy of 99.58% ± 0.56% under a strict specimen-level 10-fold cross-validation. It achieves this by directly utilizing radiometrically calibrated raw spectra, thereby effectively bypassing manual spectral feature engineering. These findings demonstrate that Hyperspectral Imaging coupled with end-to-end deep learning provides a feasible and industrially deployable solution for simultaneous stain detection and fiber identification in waste textile sorting. Full article

Water resources face a significant environmental challenge: pollution from microplastics (MP) and heavy metals (HM). These elements pose a dual threat to ecosystems and public health. Microplastics, defined as particles smaller than 5 mm, are of anthropogenic origin, resulting from the degradation of plastics by environmental factors such as solar radiation and friction with the surrounding environment, as well as from their addition to cosmetic and textile products. These materials have been widely detected in drinking water and everyday foods. Heavy metals, high-density elements $(>5{\mathrm{g}/\mathrm{cm}}^3)$, while naturally present in the Earth’s crust, are also generated in large quantities through human activity. Their toxicological risk lies in their ability to accumulate and efficiently move through the trophic chain. Due to the risks to public health and the impacts these pose to ecosystems, it is necessary to continue seeking solutions that enable their monitoring and detection. As a contribution, this work presents a methodology for detecting microplastics and heavy metals in seawater using different machine learning models and an electronic tongue coupled to a sensor network. Two different types of heavy metals, primarily zinc (Zn) and cadmium (Cd), as well as microplastic particles composed of expanded polystyrene (EPS), were detected under controlled conditions simulating different types of water. Atomic absorption spectroscopy (AAS) confirmed the concentrations of the heavy metals studied, supporting machine-learning classification of contaminated waters. Microplastics exhibited strong metal adsorption, influenced by the physicochemical properties of the water. Overall, AUC values above $90\%$ were obtained for seven different models, demonstrating the reliability of the electronic tongue in conjunction with classical machine learning techniques for detecting these elements. Full article

Water contamination by arsenic(V) [As(V)] and Congo red (CR) dye poses concurrent threats to public health and aquatic ecosystems, particularly in regions where metallurgical and textile industries coexist. Developing a single adsorbent capable of simultaneously addressing these chemically distinct pollutants, while recovering value from the spent material remains an open challenge in sustainable water treatment. This study reports the synthesis and evaluation of a novel ternary MgZnFe-LDH/1,2,4-triazole composite (TM-LDH/TZ), engineered for the concurrent adsorptive removal of As(V) and CR, and the subsequent repurposing of the pollutant-loaded material as an electrocatalyst for the urea oxidation reaction (UOR). The composite was prepared via co-precipitation and triazole surface grafting, then characterized by FTIR, XRD, BET, TGA, FESEM, and HRTEM. Batch adsorption experiments examined the influence of pH, adsorbent dose, initial concentration, and temperature, with equilibrium data modeled through Langmuir, Freundlich, Temkin, and the statistically grounded Advanced Monolayer Model (AMM); kinetics were assessed using pseudo-first/second-order and Elovich models. Maximum Langmuir adsorption capacities reached 204.75 mg g⁻¹ for As(V) and 499.72 mg g⁻¹ for CR simultaneously at pH 5 and 25 °C, surpassing the majority of previously reported single-pollutant adsorbents. Elovich and pseudo-second-order kinetics confirmed chemisorption as the governing pathway for As(V) and CR, respectively, while AMM thermodynamic analysis verified spontaneous adsorption across all experimental conditions. The spent composite delivered a UOR peak current density of 184.67 mA cm⁻² that is nearly twice that of the fresh material, with a reduced charge-transfer resistance of 1.19 Ω, and removal efficiency remained above 85% through three successive regeneration cycles. The bifunctional design, coupling high-capacity dual-pollutant removal with catalytic valorization of waste, positions TM-LDH/TZ as a circular-economy-aligned platform for advanced water remediation. Full article

The conservation of cultural heritage artifacts requires precise and controlled cleaning strategies to remove surface contaminants while preserving the structural and aesthetic integrity of the original materials. Over time, artifacts made of stone, paper, textiles, and other materials are exposed to environmental pollution, chemical reactions, and microbial colonization, which lead to the accumulation of complex contaminant layers and progressive material degradation. In recent years, significant advances in materials science have introduced innovative cleaning approaches, including polymer gels, microemulsions, nanomaterials, and enzyme-assisted systems, which enable selective contaminant removal with reduced risk of substrate damage. These methods provide improved control over solvent release, contaminant dissolution, and interaction with sensitive surfaces compared to conventional mechanical and chemical cleaning techniques. In addition, advanced analytical tools such as Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and X-ray fluorescence (XRF) have enabled quantitative evaluation of cleaning efficiency and more accurate monitoring of conservation processes. This review summarizes the major contamination mechanisms affecting cultural heritage materials and discusses recent developments in cleaning technologies, functional materials, and evaluation methods. The analysis shows that selective cleaning methods can significantly minimize damage to the underlying substrate, while environmentally friendly functional materials combined with multi-dimensional quantitative evaluation provide an effective and sustainable framework for cultural heritage conservation. Full article

Textile wastewater is among the most challenging industrial effluents due to its complex composition, high pollutant load, and low biodegradability. Conventional treatment methods often fall short in achieving complete removal of dyes and emerging contaminants. Photocatalytic composite membranes have emerged as a promising solution by integrating membrane separation and advanced oxidation processes. This review provides a comprehensive overview of the design, fabrication, and performance of photocatalytic composite membranes for textile wastewater treatment. Key aspects include the types of photocatalysts employed, methods of incorporation into membranes, and their synergistic role in pollutant removal and membrane fouling mitigation. Recent advancements in materials science, such as visible-light-responsive catalysts, carbon-based nanocomposites, and self-cleaning surfaces, are discussed, along with current limitations related to catalyst stability, operational scalability, and cost. This review underscores the potential of photocatalytic composite membranes as a next-generation platform for sustainable and effective textile wastewater treatment. Full article