Search Results (2,060)

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

This study investigates the simultaneous elimination of ethyl acetate (EA), a representative volatile organic compound (VOC), and Escherichia coli aerosols from indoor air using a continuous-flow dielectric barrier discharge (DBD) plasma reactor coupled with a photocatalytic luminous textile system (Cu/TiO₂-coated fibers). The effects of applied voltage, relative humidity, and air-flow rate on pollutant removal and disinfection performance were systematically evaluated. Optimal DBD operation at 18 kV, 1 m³ h⁻¹ airflow, and 70% relative humidity achieved single-process removal efficiencies of 77% for EA and 2 log reduction (CFU mL⁻¹) for E. coli. When photocatalysis was coupled with DBD plasma, a significant combined effect was observed, increasing EA degradation to 87% and bacterial inactivation to 3.8 log (CFU mL⁻¹). The coupling enhanced active-species generation, improved CO₂ selectivity (up to 53%), and reduced residual ozone concentration. Humidity positively affected microbial inactivation due to °OH radical formation but slightly decreased VOC degradation by limiting ozone regeneration. Results demonstrate the efficiency and scalability of the DBD–photocatalysis hybrid system for multi-pollutant indoor air purification, offering rapid, low-temperature treatment suitable for industrial-scale applications. 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

In this study, activated biochars derived from spent coffee grounds (CAC-600) and lemon pomace (LAC-600) were prepared through pyrolysis with FeCl₃ activation and evaluated for the selective adsorption of Acid Blue 74 (AB74), a dye widely used in the denim textile industry. FeCl₃ activation significantly increased the surface area and pore development relative to the pristine biochars, while also promoting the formation of Fe₂O₃ phases on the activated biochars surfaces. The activated biochars exhibited comparable adsorption capacities of 39.44 and 37.16 mg·g⁻¹ for CAC-600 and LAC-600, respectively, indicating that adsorption performance was governed mainly by the activation process rather than by the precursor biomass. Isotherm and kinetic models revealed heterogeneous adsorption behavior involving surface interactions combined with internal diffusion. The materials showed stable adsorption performance within a pH range of 4–10. Competitive adsorption experiments demonstrated preferential adsorption of AB74 over Acid Red 1 (AR1), confirming the selectivity of LAC-600 and CAC-600. Density Functional Theory (DFT) calculations revealed a cooperative adsorption mechanism combining π-surface interactions with localized Fe-oxide anchoring sites on the graphene-based model, increasing the adsorption energy by approximately 24 kcal·mol⁻¹ relative to carbon-only systems. These findings demonstrate the potential of Fe-activated agro-industrial biochars as adsorbents for dye removal from aqueous media. Full article

Hemp plants are precious resources for the textile industry, being considered a sustainable and more economical alternative to cotton. Sustainable dyeing processes should minimize the consumption of water, energy, and chemicals while ensuring high color intensity and reducing the pollution load of residual baths. Black carrot (Daucus carota L. ssp. sativus) is a valuable source of dyes for dyeing hemp materials because it is rich in anthocyanins and anthocyanidins, which generate colors ranging from red-orange and muted magenta to blue, depending on the pH. In this article, the dye extraction process was colorimetrically monitored for 26 days to determine the optimal fermentation/storage period that generates the most intense color during the dyeing process. The dyeing parameters tested were temperature (40–100 °C), pH (4.33–9.15), duration (1–24 h), concentration (2.5–10%), and the presence of organic acids (ascorbic and citric acids). Virgin baths and the first three residual baths were used in the dyeing process. While the results of FTIR, SEM, and EDX analyses confirmed the dyeing process, the CIEL*a*b* measurements quantified the characteristics of the colors obtained using virgin and residual baths. The 12 principles of green chemistry were also discussed, together with their implementation in hemp dyeing. 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

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

The exponential growth of the global textile industry, largely driven by the demand for synthetic polymers such as poly(ethylene terephthalate) (PET), polyamides, and polyurethanes, has led to severe environmental consequences, notably the accumulation of persistent microplastics and solid waste. While conventional mechanical and chemical recycling methods are widely employed, they are often hindered by harsh processing conditions and the deterioration of material properties. Consequently, there is a critical need for sustainable end-of-life management strategies. This review provides a comprehensive analysis of the biodegradability of synthetic textile fibres, with a primary focus on emerging biotechnological and enzymatic recycling approaches. It systematically examines the intrinsic polymer characteristics that govern biodegradation—including molecular orientation, crystallinity, functional groups, and fibre chemistry—as well as extrinsic factors such as textile finishings, yarn twist, polymer blends, and chemical additives. Furthermore, the current landscape of microbial and enzymatic degradation routes is critically assessed, highlighting the specific mechanisms of biocatalysts (e.g., lipases, cutinases, PETase, and MHETase) in depolymerising complex synthetic matrices into recoverable monomers. Finally, this review identifies the existing literature gap between bulk plastic and textile-specific biodegradation, discussing future perspectives. By bridging polymer science and textile engineering, this work underscores the potential of enzymatic recycling to close the loop in synthetic fibre production and advance the transition toward a circular economy. Full article

This study comparatively evaluates the effectiveness of remarketing strategies under digital transformation in Uzbekistan’s service (tourism and hospitality) and manufacturing (textile) sectors, grounded in the Resource-Based View (RBV) and the Technology Acceptance Model (TAM). Using a sequential explanatory mixed-methods design, 280 enterprises (140 per sector) from four regions of Uzbekistan were surveyed, integrating quantitative analysis (OLS regression, t-test, χ²-test, PLS-SEM) and Monte Carlo simulation (20,000 iterations) with qualitative in-depth interviews (n = 32). The textile sector exhibited higher but more volatile returns (ROI = 82.1%; CV = 0.18), whereas the tourism sector achieved more stable yet lower returns (ROI = 48.3%; CV = 0.11) (t(278) = −22.84; p < 0.001; Cohen’s d = 2.73). AI-based personalization was positively associated with ROI (β = 0.28, p < 0.001) and with reduced revenue volatility through an indirect pathway (indirect effect = 5.04, 95% CI [4.10, 6.00]), with significantly stronger associations in the textile sector (Δ = 1.64, p < 0.05). This study contributes to digital marketing theory by demonstrating sector-specific heterogeneity in AI personalization mechanisms, providing empirical evidence of the infrastructure–ROI variability relationship in a transition economy, and demonstrating the value of integrating Monte Carlo–based uncertainty analysis with mixed-methods evidence as a robustness device. The findings carry direct implications for sustainable economic development in transition economies: by demonstrating how sector-specific digital marketing strategies are linked to and can enhance the long-term viability and resource efficiency of enterprises, this study contributes to advancing Sustainable Development Goal 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). Full article

Buildings account for a significant share of global energy consumption and carbon emissions, creating an urgent need for advanced energy retrofit technologies. This review critically examines the role of plasma-modified textile polymer materials in improving the energy efficiency and durability of building retrofit systems. Various textile polymers, including polyester (polyethylene terephthalate, PET), polypropylene (PP), polytetrafluoroethylene (PTFE), polyamide (PA), and fiber-reinforced composites, are evaluated in relation to plasma surface engineering approaches, including atmospheric plasma, dielectric barrier discharge (DBD), and plasma jet treatment. Reported studies demonstrate that plasma treatment significantly alters surface morphology and chemistry, resulting in increased surface roughness, enhanced wettability, improved coating adhesion, and superior hydrophobic behavior. Water contact angles increased from approximately 70° to 145° depending on polymer type and plasma conditions, while reflective coating performance improved with solar reflectance enhancements of approximately 10–15%. Plasma-treated reflective roofing and shading textiles also showed reductions in building cooling energy demand of approximately 18–25% and roof temperature decreases of 10–15 °C. Furthermore, plasma-induced surface activation improved durability, ultraviolet (UV) resistance, and weather stability of textile membranes used in facade and roofing applications. The review also discusses industrial challenges related to scalability, plasma aging effects, energy consumption, and long-term performance. Plasma-modified systems demonstrate strong potential for multifunctional, lightweight, and sustainable building envelope technologies for future energy-efficient construction. Full article

Background: Live-streaming e-commerce has emerged as a significant retail channel, especially in the apparel industry, characterized by high impulse-driven purchase rates and elevated product returns. Reverse logistics processes associated with these returns generate considerable environmental impacts that require systematic evaluation. Methods: This study performs a gate-to-gate Life Cycle Assessment (LCA) using SimaPro software, with a functional unit of 1 kg for one pair of returned jeans. Secondary inventory data were obtained primarily from the Ecoinvent database and supplemented with literature-based estimates for transport distances and packaging masses. Results: Key hotspots analyzed include transportation modes, packaging materials, and waste disposal pathways. Transportation mode selection was the dominant environmental hotspot, with air freight exhibiting the highest impacts across most midpoint and endpoint categories. Low-density polyethylene (LDPE) packaging and landfill disposal of textile waste were also major contributors to global warming, ozone formation, and resource depletion. Conclusions: The findings underscore the necessity of integrating Circular Supply Chain (CSC) principles into reverse logistics network design for live-streaming platforms. Optimizing transportation modes and packaging choices can effectively balance operational responsiveness with environmental sustainability. This study offers empirical evidence and practical decision-supporting insights for more sustainable return management in high-return digital retail environments. Full article

This study explores the adoption of circular supply chain practices in China’s textile industry, a sector that accounts for over half of global output and has high carbon emissions. It differentiates the effects of external policy pressure and internal corporate capacity on practice implementation under pre-enforcement regulations. By integrating the STOPE framework and Innovation Resistance Theory, this paper identifies adoption drivers and barriers using PLS-SEM analysis on data from 258 firms across four Chinese provinces. The results show that internal strengths, including strategy, technology, organization, and human resources, act as key enablers. By contrast, external policies such as the EU Ecodesign for Sustainable Products Regulation and China’s dual-carbon targets, alongside regulatory, financial, cultural and industrial barriers, exert negligible effects. This study first applies the combined STOPE-IRT model to research on China’s textile circular supply chain. It extends sustainability theories to pre-enforcement contexts beyond developed economies and offers a reusable research framework for emerging economies. The findings fill gaps in the literature on contextual heterogeneity and innovation mechanisms. They provide practical implications for firms and policymakers to optimize internal capacity building and supportive regulations, accelerating sustainable circular industrial transformation. Full article

This study presents a cradle-to-gate Life Cycle Assessment (LCA) of silk manufacturing across the Eurasian value chain, covering yarn-dyed, open-width, and printed fabrics. Based on foreground data collected from Chinese companies and thirteen Italian manufacturers in the Como silk district, the analysis was performed in OpenLCA using CML 2001, ReCiPe Endpoint and Midpoint, and USEtox, with background data from Ecoinvent v3.8. The study compares dry and fresh cocoon use in silk reeling and examines the environmental profiles of the three fabric routes. Results show that cocoon reeling is the main environmental hotspot, while yarn-dyeing, fabric dyeing, and printing also contribute significantly, especially through water and chemical consumption. The comparison highlights both common patterns and route-specific differences. The findings provide a baseline for environmental improvement in silk manufacturing and support future harmonization efforts in environmental labelling, certification, and PCR-aligned assessment. Full article

Emission factor matching is the most critical step in product carbon footprint (PCF) accounting based on life cycle assessment (LCA). However, this step has long been hindered by several major challenges: a lack of standardization, overreliance on expert judgment, inconsistencies in raw data, and complex processing workflows. To address these issues, this study proposes an automated emission factor matching algorithm that combines large language models (LLMs) with semantic retrieval. The algorithm proceeds in two stages: first, an LLM identifies the reference product within the LCA database; then, an embedding model retrieves the most relevant emission factors through high-precision matching. Depending on practical requirements, the algorithm can either automatically select a single best-match factor or rank multiple best-match candidates in descending order of match precision to assist LCA experts in decision-making. We evaluate the algorithm on eight industrial products—tires, cement, ammonium phosphate, wood products, textiles, electronics and electrical appliances, steel, and lithium batteries—using the Ecoinvent 3.10 LCA database. Results demonstrate that the algorithm achieves high precision and low processing latency, significantly outperforming manual expert screening. These findings confirm that the proposed algorithm enables efficient and accurate emission factor matching, thereby providing a reliable technical solution and decision-making pathway for large-scale, automated PCF accounting. Full article

This paper integrates the Elastic Net-TVP-VAR-BK framework and constructs a high-dimensional multilayer frequency-domain network, including short-, medium-, and long-term layers, to investigate extreme risk spillovers among 56 industries in the Chinese stock market. We examine the topology of the multilayer network at the system, cross-sector, and industry levels, as well as from both static and dynamic perspectives. Using daily data on 56 industry indices from 1 March 2007 to 30 September 2024, our empirical results show that: (1) All multilayer network topologies, including edge structures, node characteristics, and spillover strengths, exhibit significant frequency heterogeneity, and the dynamic topology of the three-layer network shows fluctuations and directional differences during critical periods. (2) In most periods, the short-term layer exhibits stronger average spillover intensity and denser inter-industry linkages, suggesting that short-horizon risk transmission plays a more prominent role in rapid contagion. However, the medium- and long-term layers remain important for identifying persistent and structural risk transmission. (3) At the industry level, capital markets and textiles, apparel, and luxury goods within the short-term layer, food products, household products, and road and rail in the medium-term layer as well as construction and engineering, industrial conglomerates, trading companies and distributors, metals and mining, and distributors in the long-term layer, all demonstrate high cross-industry systemic importance and total systemic importance, thereby establishing themselves as key nodes within their respective frequency domains. The findings provide theoretical support for policymakers in formulating strategies to address market risks and offer important references for investors in asset allocation and risk management decisions. Full article