Search Results (1,745)

Volatile compounds (VCs) from fungi can promote plant growth, but their application methods are limited. Edible mushroom fungi beds (FBs) provide a readily available alternative source of fungal VCs, although their biostimulatory functions remain unvalidated. In this study, a novel, non-contact exposure method for applying VCs emitted from FBs to rice seedlings was developed. This marks the first evaluation of mushroom FBs as a direct source of bioactive VCs for plant growth promotion. Volatiles from two different edible mushroom FBs promoted shoot growth and increased biomass for rice seedlings. VCs from shiitake FBs significantly increased biomass by 67.4% while VCs from enokitake FBs by 39.5% compared to the control. The biomass-increasing effects were influenced by the quantity of shiitake FBs applied, with significant increases at 15 g, 30 g and 60 g applications. The VCs effects remained significant even when the FBs were covered with two types of gas-permeable polymer film. Chemical analysis of VCs from FBs identified several organic compounds and subsequent bioassays using synthetic VCs determined key bioactive VCs contributing to biomass increase at specific concentrations. This study presents a utilization method of waste mushroom FBs as sustainable, scalable, and cost-effective agricultural biostimulants. Full article

Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. Experimental data were obtained from drone flights in several case studies in natural and controlled environments. Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. Several algorithms for spectrum calibration, feature selection, and classification were optimized and compared to obtain an optimal solution that has high-quality results under cross-validation. This way, deploying the system in different environments without requiring complicated manual adjustments or re-learning is possible. The results of this work prove the feasibility of the proposed plastic litter detection approach using high-definition aerial remote sensing, with high specificity to plastic polymers that are not obtained using visible and NIR data. Full article

Polymers are known to produce beneficial effects on asphalt mixtures, and lignin biopolymers could further improve them while contributing to sustainability and circularity. In this research, conventional asphalt emulsion was replaced with liquid waste containing lignin from the wood industry in half-warm mix asphalt (HWMA) at varying substitution levels of 0% (control), 5%, 10%, 15%, and 20%. Additionally, 100% reclaimed asphalt pavement (RAP) was used as aggregate. The impact of asphalt emulsion substitution on the mixtures’ adhesion, cohesion, and water resistance was analyzed. Indirect tensile strength tests evaluated the HWMA’s resistance to moisture damage and ductility. Rolling bottle and boiling water tests were conducted to assess the binder-aggregate affinity. Moreover, a Life Cycle Assessment (LCA) was performed to compare the environmental benefits of HWMA with those of Hot Mix Asphalt (HMA). The findings revealed that substituting asphalt emulsion with the waste lignin up to 15% enhances the mixture’s cohesion, while only substitutions up to 5% produce mixtures with enhanced water resistance. Environmental impacts were significantly reduced for all the HWMA studied, with the Global Warming Potential (GWP) showing up to 33.5% reduction compared to a conventional HMA. Full article

The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches for optimizing their composition and properties. This study aimed at the application of machine learning for the prediction and optimization of the functional properties of composites based on a thermoplastic matrix with various fillers (two types of fibrous, four types of dispersed, and two types of nano-dispersed fillers). The experimental methods involved material production through powder metallurgy, further microstructural analysis, and mechanical and tribological testing. The microstructural analysis revealed distinct structural modifications and interfacial interactions influencing their functional properties. The key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate mechanical strength. Carbon fibers at 20 wt. % significantly improved wear resistance (by 17–25 times) while reducing tensile strength and elongation. Basalt fibers at 10 wt. % provided an effective balance between reinforcement and wear resistance (by 11–16 times). Kaolin at 2 wt. % greatly enhanced wear resistance (by 45–57 times) with moderate strength reduction. Coke at 20 wt. % maximized wear resistance (by 9−15 times) while maintaining acceptable mechanical properties. Graphite at 10 wt. % ensured a balance between strength and wear, as higher concentrations drastically decreased mechanical properties. Sodium chloride at 5 wt. % offered moderate wear resistance improvement (by 3–4 times) with minimal impact on strength. Titanium dioxide at 3 wt. % enhanced wear resistance (by 11–12.5 times) while slightly reducing tensile strength. Ultra-dispersed PTFE at 1 wt. % optimized both strength and wear properties. The work analyzed in detail the effect of PTFE content and filler content on composite properties based on machine learning-driven prediction. Regression models demonstrated high R-squared values (0.74 for density, 0.67 for tensile strength, 0.80 for relative elongation, and 0.79 for wear intensity), explaining up to 80% of the variability in composite properties. Despite its efficiency, the limitations include potential multicollinearity, a lack of consideration of external factors, and the need for further validation under real-world conditions. Thus, the machine learning approach reduces the need for extensive experimental testing, minimizing material waste and production costs, contributing to SDG 9. This study highlights the potential use of machine learning in polymer composite design, offering a data-driven framework for the rational choice of fillers, thereby contributing to sustainable industrial practices. Full article

This study focuses on the deposition of microplastics (MPs) on urban beaches along the central São Paulo coastline, utilizing advanced methodologies such as remote sensing, GNSS altimetric surveys, µ-Raman spectroscopy, and machine learning (ML) models. MP concentrations ranged from 6 to 35 MPs/m², with the highest densities observed near the Port of Santos, attributed to industrial and port activities. The predominant MP types identified were foams (48.7%), fragments (27.7%), and pellets (23.2%), while fibers were rare (0.4%). Beach slope and orientation were found to facilitate the concentration of MP deposition, particularly for foams and pellets. The study’s ML models showed high predictive accuracy, with Random Forest and Gradient Boosting performing exceptionally well for specific MP categories (pellet, fragment, fiber, foam, and film). Polymer characterization revealed the prevalence of polyethylene, polypropylene, and polystyrene, reflecting sources such as disposable packaging and industrial raw materials. The findings emphasize the need for improved waste management and targeted urban beach cleanups, which currently fail to address smaller MPs effectively. This research highlights the critical role of combining in situ data with predictive models to understand MP dynamics in coastal environments. It provides actionable insights for mitigation strategies and contributes to global efforts aligned with the Sustainable Development Goals, particularly SDG 14, aimed at conserving marine ecosystems and reducing pollution. Full article

Food waste has a significant social impact but can be revalued as a source of bioactive compounds, such as condensed tannins. This abundant biomass, corresponding to a polymeric antioxidant, must be depolymerized to become bioavailable. Previous studies have investigated polymer degradation into oligomers using high temperatures and expensive nucleophiles, often under conditions unsuitable for food applications. In the present investigation, it is proposed that the depolymerization of condensed tannins can occur under food-grade conditions using a Generally Recognized as Safe (GRAS) solvent by optimizing the reaction’s heating method with microwave assistance and using gallic acid as a nucleophile. Thermal studies indicate that the degradation of total polyphenols content follows first-order kinetics and occurs above 80 °C in microwave. Depolymerization follows second-order kinetics, yielding epicatechin as the primary product with zero-order formation kinetics. The optimized factors were 80% v/v ethanol, 10 mg/mL polymeric tannins, and 5.88 mg/mL gallic acid. Under these conditions, the reaction efficiency was 99.9%, the mean particle diameter was 5.7 nm, the total polyphenols content was 297.3 ± 15.9 EAG mg/g, and the inhibition of ABTS●+ and DPPH● radicals was 93.5 ± 0.9% and 88.2 ± 1.5%, respectively. These results are promising for future scaling processes. Full article

Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to traditional synthetic composites. In addition to natural fiber reinforcement, the usage of recycled aggregates in concrete has been proposed as a remedy to combat the rapidly increasing amount of construction and demolition waste in recent years. However, the accurate prediction of the structural performance metrics, such as tensile strength, remains a challenge for concrete composites reinforced with natural fibers and containing recycled aggregates. This study aims to develop predictive models of natural-fiber-reinforced recycled aggregate concrete based on experimental results collected from the literature. The models have been trained on a dataset consisting of 482 data points. Each data point consists of the amounts of cement, fine and coarse aggregate, water-to-binder ratio, percentages of recycled coarse aggregate and natural fiber, and the fiber length. The output feature of the dataset is the splitting tensile strength of the concrete. Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and extra trees regressor models were trained to predict the tensile strength of the specimens. For optimum performance, the hyperparameters of these models were optimized using the blended search strategy (BlendSearch) and cost-related frugal optimization (CFO). The tensile strength could be predicted with a coefficient of determination greater than 0.95 by the XGBoost model. To make the predictive models accessible, an online graphical user interface was also made available on the Streamlit platform. A feature importance analysis was carried out using the Shapley additive explanations (SHAP) approach. Full article

Fused filament fabrication (FFF) has recently emerged as a sustainable digital manufacturing technology to fabricate polymer composite parts with complex structures and minimal waste. However, FFF-printed composite parts frequently exhibit heterogeneous structures with low mechanical properties. To manufacture high-end parts with good mechanical properties, advanced predictive tools are required. In this paper, Artificial Neural Network (ANN) models were developed to evaluate the mechanical properties of 3D-printed polyamide 12 (PA) and carbon fiber (CF) and glass fiber (GF) reinforced PA composites. Tensile samples were fabricated by FFF, considering two input parameters, such as printing orientation and infill density, and tested to determine the mechanical properties. Then, single- and multi-target ANN models were trained using the forward propagation Levenberg–Marquardt algorithm. Post-training performance analysis indicated that the ANN models work efficiently and accurately in predicting Young’s modulus and tensile strength of the 3D-printed PA and fiber-reinforced PA composites, with most relative errors being far less than 5%. In terms of mechanical properties, such as Young’s modulus and tensile strength, the 3D-printed composites outperform the unreinforced PA. Printing PA composites with 0° orientation and 100% infill density results in a maximum increase in Young’s modulus (up to 98% for CF/PA and 32% for GF/PA) and tensile strength (up to 36% for CF/PA and 18% for GF/PA) compared to the unreinforced PA. This study underscores the potential of the ANN models to predict the mechanical properties of 3D-printed parts, enhancing the use of 3D-printed PA composite components in structural applications. Full article

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is thermoplastic, biodegradable, and derived from renewable-source polymers; thus, it can be used as an alternative to traditional synthetic polymers to reduce damage to the environment. The production of cassava starch generates a high amount of cassava bagasse (about 93% of processed roots) in the separation step of starch. The utilization of this waste is essential due to the difficulty of transportation and storage, besides the detriment caused to the environment by its incorrect disposal. This work aimed to evaluate the possibility of using cassava bagasse as a reinforcement in the production of biocomposites with PHBV matrices by compression molding. The physical–chemical and thermal properties of these biocomposites were characterized. The residue can be used as a filler in compression-molded PHBV biocomposites. The most suitable formulation was 10 wt. %, despite the presence of some cassava bagasse (CB) agglomerations. This film could be used as rigid packaging for chilled or shelf-aqueous food. Full article

The textile industry consumes large volumes of freshwater, producing enormous wastewater containing chemicals from dyeing and bathing, but also microplastics concentrations that have not been deeply studied. Liquid wastes from the synthetic and natural textile manufacturers were treated with a new disruptive technology (Adiabatic Sonic Evaporation and Crystallization, ASEC), which completely removed contaminants from water, providing distilled water and crystallized solids. The current study presents the characterization of the industrial residues and the obtained by-products: microplastics and organic matter contained in the solid residue were analyzed and characterized through chromatography. The results of the analyses displayed that compounds such as benzene, benzoic acid and 2,4-dymethyl-1-heptene were found in the synthetic industry water samples as degraded compounds of polyester and polypropylene. Meanwhile, the natural industry water also contained polyester, nylon and PMM polymer. After the depuration of samples, microplastics were completely retained in the solid phase, together with the organic matter (sulfate and surfactants) resulting on clean water. This is the first study focused on the study of microplastics generated by the textile industry and their prevention by removing them as solid waste. Full article

Waste stone sludge generated by the extractive industry has traditionally posed significant disposal challenges. This study redefines stone sludge as a valuable raw material by incorporating it into polylactic acid (PLA) to create sustainable composite materials. Pellets and filaments composed of up to 50% by weight of limestone powder and PLA were successfully produced using melt blending in a twin-screw extruder. Scanning electron microscopy (SEM), X-ray fluorescence (XRF), and X-ray diffraction (XRD) analyses revealed a uniform distribution of stone particles within the PLA matrix and confirmed the chemical and structural compatibility of the components. Thermogravimetric analysis (TGA) showed that the composites retained thermal stability, while mechanical testing demonstrated significant enhancements in stiffness, with an increase in elastic modulus for composites containing 50% limestone powder. The melt flow rate (MFR) decreases with increasing filler content. The brittleness also increased, reducing impact resistance. Mechanical tests were performed on injected and 3D-printed specimens. The filament produced was successfully used in 3D printing, with a small XYZ calibration cube. Full article

Resistivity-type humidity sensors, which detect changes in electrical resistance in response to variations in environmental humidity, have garnered significant interest due to their widespread application in industry, agriculture, and daily life. These sensors rely on diverse materials for fabrication, but their increasing variety has contributed to the accumulation of electronic waste. As a biodegradable polymer, cellulose offers unique advantages, including a naturally hydrophilic structure and a large specific surface area. These properties enable cellulose to reduce e-waste generation while facilitating the efficient adsorption of water molecules. However, despite these benefits, humidity sensors based solely on cellulose often suffer from poor sensitivity due to its limited hydrophilicity and non-adjustable structure. To overcome these limitations, the development of composite materials emerges as a promising solution for enhancing the performance of cellulose-based humidity sensors. Combining the complementary properties of cellulose and TiO₂, this work presents the development of a cellulose/TiO₂ composite humidity sensor through a sustainable approach. The resulting composite material exhibits significantly improved sensitivity compared with a sensor fabricated purely from cellulose. To achieve this, TiO₂ nanoparticles were incorporated into cellulose extracted from potato peels, and the composite film was fabricated using the casting method. The sensor’s performance was evaluated by analyzing the dependence of its complex impedance, measured over a frequency range between 2 kHz and 10 MHz, while varying relative humidity (RH). Full article

In the field of liquid composite moulding (LCM) simulations, a long-standing assumption has dominated–the belief in constant resin viscosity. While effective in many cases, this assumption may not hold for the infusion process, which lasts for an extended period. This impacts the mechanical properties of the cured epoxy, which are crucial for load transfer in polymer structures. The majority of epoxy resins operate on a bipartite foundation, wherein their viscosity undergoes dynamic alterations during the process of cross-linking. Temperature and cross-linking intricately interact, with elevated temperatures initially reducing viscosity due to kinetic energy but later increasing it as cross-linking accelerates. This interplay significantly influences the efficiency of the infusion process, especially in large and intricate moulds. This article explores the significant temperature dependence of epoxy resin viscosity, proposing an accurate model rooted in its non-linear evolution. This model aligns with empirical evidence, offering insights into determining the optimal starting temperature for efficient mould filling. This study presents an advanced infusion model that extends existing non-linear dual-split viscosity approaches by incorporating the experimental validation of viscosity variations. Unlike previous models that primarily focus on theoretical or numerical frameworks, this work integrates experimental insights to optimize infusion temperature for efficient resin infusion in large and complex parts. Building on these findings, a novel mould-filling technique is proposed to enhance efficiency and reduce material waste. Full article

The development of sustainable and mechanically versatile polymeric materials is essential to meet the growing demand for eco-friendly, high-performance products. This study investigates the mechanical properties of blends comprising polyvinyl chloride (PVC), thermoplastic polyurethane (TPU), and glycerol diacetate monolaurate, a bio-based plasticizer derived from waste cooking oil, addressing the underexplored combined effects of these components. By varying the proportions, the blends’ tensile strength, elasticity, elongation at break, and hardness were tailored for diverse applications. Incorporating the bio-plasticizer significantly enhanced the PVC’s flexibility and elongation at break, while reducing its tensile strength and rigidity. The addition of TPU further enhanced the elasticity, toughness, and resilience, with the final properties governed by synergistic interactions between PVC’s rigidity, TPU’s elasticity, and the plasticizer’s softening effects. Dynamic mechanical analysis (DMA) confirmed that the bio-plasticizer enhanced the compatibility between the PVC and TPU, leading to ternary PVC/TPU/bio-plasticizer blends with an improved elasticity and elongation at break, without a significant loss in tensile strength. These blends exhibited a broad range of tunable properties, enabling applications from flexible films to impact-resistant components. Overall, these findings highlight the potential of PVC/TPU/bio-plasticizer systems to deliver high-performance materials with enhanced sustainability. This work offers valuable insights for developing greener polymer systems and advancing the creation of tailored materials for diverse industrial applications in alignment with global sustainability goals. Full article

The growing environmental impact of textile waste, fueled by the rapid rise in global fiber production, underscores the urgent need for sustainable end-of-life solutions. This review explores cutting-edge pathways for textile waste management, spotlighting innovations that reduce reliance on incineration and landfilling while driving material circularity. It highlights advancements in collection, sorting, and pretreatment technologies, as well as both established and emerging recycling methods. Smart collection systems utilizing tags and sensors show great promise in streamlining logistics by automating pick-up routes and transactions. For sorting, automated technologies like near-infrared and hyperspectral imaging lead the way in accurate and scalable fiber separation. Automated disassembly techniques are effective at removing problematic elements, though other pretreatments, such as color and finish removal, still need to be customized for specific waste streams. Mechanical fiber recycling is ideal for textiles with strong mechanical properties but has limitations, particularly with blended fabrics, and cannot be repeated endlessly. Polymer recycling—through melting or dissolving waste polymers—produces higher-quality recycled materials but comes with high energy and solvent demands. Chemical recycling, especially solvolysis and pyrolysis, excels at breaking down synthetic polymers like polyester, with the potential to yield virgin-quality monomers. Meanwhile, biological methods, though still in their infancy, show promise for recycling natural fibers like cotton and wool. When other methods are not viable, gasification can be used to convert waste into synthesis gas. The review concludes that the future of sustainable textile recycling hinges on integrating automated sorting systems and advancing solvent-based and chemical recycling technologies. These innovations, supported by eco-design principles, progressive policies, and industry collaboration, are essential to building a resilient, circular textile economy. Full article