Search Results (21,120)

The management of plastic packaging waste needs to be optimized to improve recycling rates. In this article, fourteen categories of non-bottle polyethylene terephthalate (PET) packages were mechanically recycled at laboratory bench scale; the generated data were assessed using a multi-criteria decision analysis (MCDA) approach to identify the categories most suited for the mechanical recycling process from social, technical and legislative viewpoints. Recycling yields varied between 75% and 92% across the 14 categories. The intrinsic viscosity (IV) values of the produced recycled PET (rPET) corresponded to molecular weights ranging from 28,000 to 35,000 g/mol. The MCDA recyclability assessment showed that 7 of the 14 categories (accounting for 72% of the sorted products by mass flow) are often composed of multiple, inseparable materials, resulting in the lowest-quality rPET. Furthermore, only 4 categories (approximately 28% of the categories) were found suitable for closed-loop mechanical recycling. The stakeholders involved in the PET packaging value chain could use these results to support decision-making and the development of a well-organized framework to valorize even the most complex types of plastic waste. Full article

Considering that consumers have dual reference effects of price and goodwill; that is, consumers have psychological expectations for price and brand goodwill when making consumption decisions. A difference game model with reference effects is established for a closed-loop supply chain composed of a manufacturer, a retailer and a recycler, and a bidirectional cost-sharing contract is adopted for coordination. At the same time, the impact of dual reference effects and the bidirectional cost sharing contract on supply chain members’ profits are further analyzed by numerical simulation. We find that: (1) The impact of the price reference effect and the goodwill reference effect on supply chain decisions and market demand exhibits significant cost interval dependence. Notably, within a specific cost interval and under the influence of the dual reference effects, the market exhibits a phenomenon of “high price, high demand.” (2) The price reference effect influences the power structure of the supply chain. Specifically, when the price reference effect exceeds a certain threshold, the retailer’s profit surpasses the manufacturer’s profit. (3) The bidirectional cost-sharing contract coordinates the discrete dynamic closed-loop supply chain under dual reference effects. Consequently, it achieves a double Pareto improvement in supply chain members’ profits and brand goodwill. Full article

Environmental sustainability is crucial for human survival and the future of new generations. Anticipating regret can influence decision-making and promote sustainable behaviors. This study examines the effect of anticipated regret on pro-environmental behaviors among young adults (18–30) using regret-based short videos called “EkoToks.” A total of 128 participants were randomly assigned to an experimental group, receiving regret-evoking videos, or a control group, receiving informational videos. Pro-environmental behaviors were measured at baseline, post-test, and at three-month follow-up. Results showed significant short-term improvements in the experimental group compared to the control group, with higher scores in total pro-environmental behavior, prosocial behavior, reuse, recycling, and pro-environmental actions. At follow-up, the experimental group continued to outperform the control group in terms of total behavior, prosocial behavior, recycling, reuse, pro-environmental actions, and waste reduction. Regression analyses revealed that post-test regret significantly predicted further improvements at follow-up (compared to post-test) in total behavior, prosocial behavior, reuse, and pro-environmental actions. These findings highlight the effectiveness of anticipated regret in improving environmental behaviors, particularly low-cost ones. Full article

Strategies based on the use of recycled materials have been widely discussed as alternatives to reduce environmental impacts in transport infrastructure. In pavement engineering, the use of Reclaimed Asphalt Pavement (RAP) in base layers offers environmental benefits; however, its benefits depend on processing conditions and structural performance. Chemical stabilization techniques, although mechanically effective, tend to introduce environmental hotspots associated with binder production. In this study, controlled thermal conditioning of RAP is evaluated as a warm base solution without chemical stabilizers in the context of airport pavements. A comparative life cycle assessment was conducted under a production- and construction-stage scope (A1–A3 and A5, excluding transportation under equivalent logistical assumptions), considering untreated RAP, heated RAP, and RAP stabilized with emulsion and cement, and was integrated with mechanistic–empirical structural performance analyses. The results indicate that, although heated RAP presents intermediate absolute environmental impacts due to additional energy consumption, it achieves the highest eco-efficiency, expressed as the lowest ratio between global warming potential (IPCC 2023) and estimated structural service life. In the analyzed scenarios, the warm base showed approximately 71% lower environmental impact per year of service than untreated RAP and about 90% lower than the emulsion-stabilized alternative. These findings suggest that performance-based sustainability assessment can reveal environmental advantages in solutions that exhibit moderate increases in production-stage impacts but enhanced structural longevity. It should be noted that the conclusions are conditioned by the adopted production and construction system boundaries, which do not include the use, rehabilitation, or end-of-life phases. Full article

Microsieving-based advanced primary treatment (APT) has attracted increasing attention as an approach for restructuring carbon and energy flows within wastewater treatment plants (WWTPs). Unlike previous work that has often addressed individual microsieving technologies or specific recovery routes separately, this review provides a unified framework for comparing drum screens (DSs)/drum filters (DFs), cloth disc filters (CDFs), and rotating belt filters (RBFs) with conventional primary sedimentation (PST) in terms of separation mechanisms and pollutant capture. On this basis, it further discusses recent progress in energy and resource recovery from primary screenings, together with their relevance to energy demand reduction and carbon redistribution in WWTPs. Current limitations arise at two levels. Microsieving technologies remain constrained by mesh fouling and limited control over selective pollutant capture, while plant-wide evidence remains insufficient, particularly regarding techno-economic assessment of recovered products and life cycle assessment of full plant performance after replacing primary sedimentation. Future work should therefore focus on targeted process optimization and plant-wide evaluation of economic and environmental feasibility. Full article

The construction sector consumes significant amounts of natural resources and contributes substantially to global CO₂ emissions, making it necessary to develop materials with a reduced environmental impact. In this context, the valorization of reusable industrial waste as secondary raw materials represents a strategic direction for applying circular economy principles and for decarbonizing the construction materials industry. The scientific problem addressed in this review is the urgent need to develop construction materials with a reduced environmental footprint, given that the construction sector is a major consumer of natural resources and a significant contributor to global CO₂ emissions. This challenge requires the identification and critical evaluation of sustainable solutions that support decarbonization and the transition toward a circular economy. The main findings indicate that the valorization of industrial waste offers high decarbonization potential: supplementary cementitious materials (SCMs), such as ground granulated blast furnace slag and fly ash, can reduce CO₂ emissions by approximately 20–50%, while alkali-activated binders and geopolymers achieve reductions of 40–80% compared to Portland cement. These materials also enhance durability, extending service life by 10–20% in aggressive environments, although early-age strength may decrease by 10–30%; recycled aggregates derived from construction and demolition waste (CDW) can substitute up to 100% of natural aggregates, while rubber fibers can increase impact resistance by 30–50% and reduce density by 10–20%. However, key limitations relate to waste variability, heavy metal leaching risks (requiring immobilization efficiencies > 90%), and the relatively low technological maturity of many solutions (TRL < 7), leading to the TRL–CO₂ paradox and highlighting the need for standardization and performance-based regulatory frameworks. The synthesized results indicate that the appropriate integration of industrial waste enables a significant reduction in clinker content, lowers associated CO₂ emissions, and decreases primary energy consumption while maintaining physical–mechanical properties and durability characteristics comparable to or in some cases superior to those of traditional materials, if mix design is based on clear performance criteria, stratified according to the type of waste, dosage used, curing regime, binder chemistry, and the target application. Full article

The textile industry consumes a significant quantity of water and produces effluent containing water-soluble dyes and heavy metals such as Lead (Pb), Cadmium (Cd), Chromium (Cr), Copper (Cu), and Zinc (Zn), among others. Heavy metal contamination of water bodies and their impact on aquatic life, as well as on human health, is of prime importance. This review examined the potential of phytoremediation, a low-cost and eco-friendly process for removing contaminants from textile effluent. This review also investigated the impact of heavy metal toxicity on aquatic plants used for biogas production post phytoremediation application. This review evaluated textile effluent characteristics, efficiency evaluation of phytoremediation of textile wastewater, metal uptake mechanisms of aquatic plants, and anaerobic digestion processes with emphasis on Water hyacinth (Eichhornia crassipes), Duckweed (Lemna minor), and Water lettuce (Pistia stratiotes). The findings indicated that these aquatic plants possess immense potential for removing heavy metals and other impurities by employing phytoextraction and rhizofiltration methods. Their rapid growth rate makes them preferred candidates for anaerobic digestion. However, accumulation of heavy metals in plant tissues inhibits microbial activities during anaerobic digestion, resulting in fluctuations in biogas and methane production. Findings also showed that these aquatic plants are efficient in the removal of heavy metals in water while yielding considerable biomass that can be used to produce bioenergy through anaerobic digestion. However, the sequestration of heavy metals in plant biomass may affect the rate of methane generation efficiency. The findings of this review suggest that phytoremediation has promising potential for the recycling of textile wastewater and, when coupled with biogas production, contributes towards a circular bioeconomy, an approach that integrates closed-loop resource utilization with renewable biological systems to minimize waste. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

A rapid accumulation of plastic waste has created an urgent need for efficient and sustainable recycling technologies. Among various approaches, pyrolysis stands out as promising method of thermochemical recycling of plastic waste; however, the process needs optimization and further research to make it more energy-efficient and sustainable. The conventional approaches for optimization focus on the enhancement of yield, only overlooking efficiency and system-level sustainability. In this study, a machine learning-enabled surrogate-assisted multi-objective artificial intelligence (AI) optimization framework is developed for plastic pyrolysis to maximize product recovery and minimize energy consumption. The model integrates energy return on investment (EROI) and higher heating value (HHV) into process design. A curated dataset of 312 experimental cases covering polyolefins, PET, nylon, and mixed plastics was used to train multiple machine learning algorithms, such as polynomial regression, Gaussian process regression, and Random Forest models. The Random Forest algorithm demonstrated superior predictive robustness across oil yield, HHV, char formation, and EROI. Pareto front analysis using NSGA-II revealed that moderate reaction severities (400–450 °C, 40–70 min) maximize net energy performance while minimizing solid residues. The conditional variational autoencoder as a GenAI model was incorporated to work as a generative proposal engine, which enhances the exploration of chemically feasible operating regions under uncertainty-aware active learning. The integration of techno-economic and life-cycle assessment demonstrates that energy-positive configurations outperform high-yield scenarios, achieving IRR > 15%, energy intensity < 10 MJ kg⁻¹, and CO₂ reductions up to 47% relative to incineration. The proposed framework establishes a data-driven methodology for aligning polymer pyrolysis optimization with circular economy and energy sustainability objectives. Full article

4-(2-Aminophenyl)-4-oxobutyronitriles in the presence of formic acid (HCOOH) and p-toluenesulfonic acid (TsOH) undergo an unusual rearrangement providing access to a range of 3-(quinazolin-4-yl)propionic acids that have been poorly represented in the literature, with only a few isolated examples known to date. The reaction demonstrates a new route to the quinazoline core through transfer of the nitrile group nitrogen, mediated through the formation of a pyrrolidine cycle. The availability of 4-oxobutyronitrile precursors—2′-aminochacones—provides high variability of substituents in the required positions of product. The transformation is general and can be extended to the preparation of 2-substituted 3-(quinazolin-4-yl)propionic acids through preliminary acylation or to the synthesis of 4-(quinazolin-4-yl)butyric acids. Full article

The increasing use of pressure-sensitive labels (PSLs), driven by growth in the packaging sector, raises concerns regarding material consumption and end-of-life management under evolving European packaging regulations. This study investigates the biodegradation potential of sustainable PSL facestocks produced from 15% agro-industrial by-products, 40% post-consumer recycled fibers, and 45% virgin wood pulp. Their biodegradation behavior was compared with bio-based polyethylene (PE) facestocks using laboratory-scale aerobic soil burial tests conducted for up to 28 days. Biodegradation was assessed through weight loss measurements, visual evaluation, Fourier transform infrared (FTIR) spectroscopy, and fluorescence analysis. Fiber-based facestocks exhibited significant degradation, reaching approximately 50–55% weight loss after 28 days, accompanied by structural changes in the cellulose matrix and reduced fluorescence intensity. In contrast, bio-based polyethylene facestocks showed negligible weight loss and only minor spectroscopic changes, indicating high stability under the tested conditions. The results demonstrate that fiber-based samples derived from agro-industrial and recycled sources possess substantially higher biodegradation potential than bio-based polymeric alternatives. These findings support the use of fiber-based PSL facestocks in applications requiring improved environmental compatibility. Full article

The development of filled photopolymer composites for Digital Light Processing (DLP) additive manufacturing requires optimizing processing parameters to achieve the desired mechanical properties. Traditional experimental approaches are time-intensive, while physics-based models often struggle to capture the complex interactions among parameters. This study presents a physics-informed machine learning framework that combines Random Forest with Bayesian optimization (RF-BO) to predict the ultimate tensile strength in recycled thermoset resin composites manufactured via DLP. A validation dataset of 19 systematically varied formulations (each with n = 5 measurement replicates for reliability) was generated and augmented with 1500 physics-informed synthetic samples to enable robust model training. The limited experimental dataset, while insufficient for traditional statistical inference, provided critical validation of physical trends, including non-monotonic particle-size effects and optimal processing windows. Six machine learning algorithms were evaluated, with RF-BO achieving superior performance (R² = 0.9125, MSE = 1.07 MPa). The framework identified optimal processing conditions of 59–64 μm particle size, 5.0 ± 0.5 wt.% concentration, and 60 min cure time, predicting a maximum UTS of 43.84 MPa with a prediction error of less than 1.0 MPa. Feature importance analysis revealed that cure time was the dominant parameter (40%), followed by particle size (30%), validating the physical interpretability. This approach demonstrates significant potential for accelerating materials design in composite additive manufacturing while maintaining physically meaningful predictions. Full article

Recycling spent lithium-ion batteries (LIBs) is critical for resource sustainability and carbon neutrality. This work presents a green strategy in which NaA zeolite is used to preferentially recover lithium from leachate of spent NCM111 batteries, combined with temperature-regulated stepwise separation of transition metals. Benefiting from the distinct hydrated ionic radii and charge density between Li⁺ and divalent metal ions, NaA zeolite selectively adsorbs Ni²⁺, Co²⁺ and Mn²⁺, leaving Li⁺ in the raffinate. Under optimized conditions, two-stage adsorption achieves 95.6%, 96.7% and 99.7% removal of Ni²⁺, Co²⁺ and Mn²⁺, respectively, with 11% Li⁺ co-adsorption. Thermodynamic analysis reveals that the adsorption process is endothermic and thermodynamically spontaneous. The interaction strength between metal ions and NaA zeolite follows the order Ni²⁺ > Co²⁺ > Mn²⁺, and ion exchange is identified as the dominant mechanism. It is determined that 96.8% of Mn²⁺ can be recovered at 0 °C, followed by the desorption of 93.5% of Co²⁺ at 90 °C, and the sequential separation of Mn, Co and Ni is realized. Three consecutive adsorption–desorption cycles demonstrate the acceptable reusability of the Ni-loaded NaA adsorbent. High-purity Li₂CO₃ (purity 96.7%, yield 93.5%), MnO₂ (purity 99.3%, yield 98.4%) and Co₃O₄ (purity 98.8%, yield 97.6%) are obtained from the corresponding solutions. This approach provides a scalable closed-loop pathway for full-component recovery of valuable metals from spent LIBs. Full article

Returning spent mushroom substrate (SMS) to the field is an effective way to dispose of it. However, given the substantial nutrient consumption associated with Volvariella volvacea SMS, their effects on soil properties and crop performance warrant further investigation. By analyzing the effects of three different application rates of SMS on soil nutrients and lettuce (Lactuca sativa L.) quality, the results showed that the group with 1.5 kg m⁻² SMS addition improved the total nitrogen (+21.2%), and organic content (+27.9%) in soil, and it demonstrated particularly outstanding performance in enhancing the survival rate (+21.9%), average weight (+71.7%), chlorophyll content (+45.6%), and total phenolic content (+25.2%) of lettuce. By comparing the soil microbial communities in the control group, the SMS (1.5 kg m⁻²) treatment group, and the organic fertilizer treatment group, it was found that they were mainly composed of Group S1, S2, and S3 microorganisms, respectively. The microbial community evenness in the treatment groups was greater than that in the control group. Furthermore, the results also revealed that the microbial conversion efficiency of nitrogen and phosphorus in the SMS treatment group was higher than the control group, which promoted nutrient cycling and improved the quality of lettuce. Our analysis provides an environmentally friendly way for Volvariella volvacea SMS disposal. Full article

This study investigates the combined effect of incorporating recycled concrete aggregates (RCAs) and glass fibers (GFs) on the properties of geopolymer concrete. The precursor binder consisted of a blend of ground granulated blast furnace slag and fly ash. Furthermore, two types of GFs (i.e., short and long) were incorporated, either individually or in hybrid combinations, to enhance the performance of the concrete. Experimental results revealed that replacing natural aggregates (NAs) with RCAs in geopolymer concrete production had no tangible impact on workability but resulted in a slight reduction in the density, ultrasonic pulse velocity, and bulk resistivity. Similarly, the compressive strength and modulus of elasticity decreased by up to 18 and 57%, respectively. Meanwhile, the addition of GFs, particularly in hybrid configurations, effectively mitigated these reductions. Among the hybrid mixtures, a short-to-long fiber ratio (A:B) of 1:3 yielded the most significant improvements of the physical, mechanical, and durability properties, with increases of up to 16%, 91%, and 61%, respectively. Several correlation equations were established to describe the relationships between the physical, mechanical, and durability properties of GF-reinforced geopolymer concrete and were compared with existing codified models. The outcomes provide critical insights into the synergistic roles of RCA and GFs in tailoring high-performance, eco-efficient concrete systems. This research supports the advancement of sustainable concrete production and promotes the broader structural adoption of geopolymer technologies. Full article