Search Results (277)

Waste electrical and electronic equipment (WEEE) is one of the fastest-growing waste streams globally. Disposable e-cigarettes are among the products that have gained popularity in recent years. Their complex construction and embedded lithium-ion batteries (LIBs) present environmental, safety, and resource recovery challenges. Despite growing research interest, integrated analyses linking material composition with user disposal behavior remain limited. This study is the first to incorporate device-level mass balance, material contamination assessment, battery residual charge measurements, and user behavior to evaluate the waste management challenges of disposable e-cigarettes. A mass balance of twelve types of devices on the Polish market was performed. Plastics dominated in five devices, while non-ferrous metals prevailed in the others, depending on casing design. Materials contaminated with e-liquid residues accounted for 4.4–10.7% of device mass. Battery voltage measurements revealed that 25.6% of recovered LIBs retained a residual charge (greater than 2.5 V), posing a direct fire hazard during waste handling and treatment. Moreover, it was estimated that 7 to 12 tons of lithium are introduced annually into the Polish market via disposable e-cigarettes, highlighting substantial resource potential. Survey results showed that 46% of users disposed of devices in mixed municipal waste, revealing a knowledge–practice gap largely independent of gender or education. Integrating technical and social findings demonstrates that improper handling is a systemic issue. The findings support the relevance of eco-design requirements, such as modular casings for battery removal, alongside the enforcement of Extended Producer Responsibility (EPR) schemes. Current product fees (0.01–0.03 EUR/unit) remain insufficient to establish an effective collection infrastructure, highlighting a key systemic barrier. Full article

An inorganic–organic hybrid nano-adsorbent was prepared by chemical immobilisation of an organic Schiff base Cu (II) ion receptor, DHB ((E)-N-(1-(2-hydroxy-6-methyl-4-oxo-4H-pyran-3-yl) ethylidene) benzohydrazide), a selective dehydroacetic acid-based chemosensor, onto a mesoporous silica support. In order to prepare the sorbent, the silylating agent was anchored onto the silica. During this procedure, 3-Chloropropyl trimethoxy silane (CPTS) was attached to the surface, increasing hydrophobicity. By immobilising DHB onto the CPTS platform, the silica surface was activated, and as a result the coordination chemistry of the Schiff base generated a hybrid adsorbent with the capability to rapidly sequestrate Cu (II) ions from wastewater, as an answer to combat growing Waste Electrical and Electronic Equipment (WEEE) contamination in water supplies, in the wake of a prolonged consumerism mentality and boom in cryptocurrency mining. The produced hybrid materials were characterised by FTIR, proximate and ultimate analysis, nitrogen physisorption, PXRD, SEM, and TEM. The parameters influencing the removal efficiency of the sorbent, including pH, initial metal ion concentration, contact time, and adsorbent dosage, were optimised to achieve enhanced removal efficiency. Under optimal conditions (pH 7.0, adsorbent dosage 3 mg, contact time of 70 min, and 25 °C), Cu (II) ions were quantitatively sequestered from the sample solution; 93.1% of Cu (II) was removed under these conditions. The adsorption was found to follow pseudo-second-order kinetics, and Langmuir model fitting affirmed the monolayer adsorption. Full article

The escalating volume of waste electrical and electronic equipment (WEEE) poses a significant global environmental challenge. The disassembly of printed circuit boards (PCBs), a critical step for resource recovery, remains inefficient due to limitations in the adaptability and dexterity of existing automated systems. This paper proposes an intelligent disassembly system for PCB components that integrates a multimodal large language model (MLLM) with a multi-agent framework. The MLLM serves as the system’s cognitive core, enabling high-level visual-language understanding and task planning by converting images into semantic descriptions and generating disassembly strategies. A state-of-the-art object detection algorithm (YOLOv13) is incorporated to provide fine-grained component localization. This high-level intelligence is seamlessly connected to low-level execution through a multi-agent framework that orchestrates collaborative dual robotic arms. One arm controls a heater for precise solder melting, while the other performs fine “probing-grasping” actions guided by real-time force feedback. Experiments were conducted on 30 decommissioned smart electricity meter PCBs, evaluating the system on recognition rate, capture rate, melting rate, and time consumption for seven component types. Results demonstrate that the system achieved a 100% melting rate across all components and high recognition rates (90–100%), validating its strengths in perception and thermal control. However, the capture rate varied significantly, highlighting the grasping of small, low-profile components as the primary bottleneck. This research presents a significant step towards autonomous, non-destructive e-waste recycling by effectively combining high-level cognitive intelligence with low-level robotic control, while also clearly identifying key areas for future improvement. Full article

The growing demand for lithium, driven by its key role in rechargeable batteries and its use in electric vehicles, highlights the need for sustainable and environmentally friendly recovery strategies. Conventional methods, such as pyrometallurgy and hydrometallurgy, are effective but costly and harmful as they emit toxic compounds. Biohydrometallurgy has emerged as a promising alternative, as it uses microorganisms and their metabolites to solubilize metals under milder conditions. Biohydrometallurgy has emerged as a promising alternative, as it relies on microorganisms and their metabolites to solubilize metals under mild operating conditions. Nevertheless, challenges related to process efficiency and selectivity remain, particularly for lithium recovery. In this context, recent advances in metal-binding peptides have attracted increasing attention due to their inherent selectivity and the possibility of rational design and heterologous expression in well-established microbial hosts such as Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae. This review critically analyzes current biotechnological strategies and explores the integration of microbial bioleaching with peptide-based approaches as a complementary and environmentally friendly framework for the selective recovery of lithium and other metals from spent batteries and waste electrical and electronic equipment. Overall, this review provides an integrative conceptual framework that highlights the potential of combining microbial processes with metal-binding peptides to guide the development of more selective and sustainable biotechnological strategies for lithium recovery from secondary sources. Full article

The rapid growth of Waste Electrical and Electronic Equipment (WEEE) in the European Union highlights the need for a rigorous understanding of its long-term dynamics and the role of innovation in shaping its trajectory. This study investigates how innovation influences the dynamics of WEEE generation in the European Union. We develop an innovation-adjusted mathematical model of e-waste as a stock flow system and prove the existence and global stability of a unique positive equilibrium. The model analytically generates an environmental Kuznets-type turning point and shows that innovation reduces waste accumulation by accelerating effective depreciation. To link the theoretical results with empirical patterns, we embed the model in a STIRPAT panel specification using annual data for 27 EU member states from 2013 to 2023, where EU Eco-innovation Index (EEI) serves as a composite index which directly captures policy-driven green technology and circular economy activities, aligning precisely with our theoretical framework. We also extend the quasi-demeaning transformation to panels with correlated shocks and establish its consistency under a factor structured error process. The empirical estimates confirm a positive effect of income on WEEE at lower development levels and a negative coefficient on its squared term, consistent with an inverted U pattern, while innovation is associated with lower waste intensity. These findings demonstrate how mathematical modeling can strengthen the interpretation of macro panel evidence on circularity and provide a basis for future optimization of innovation driven sustainability transitions. Full article

The rapid growth in manufacturing and use of electrical and electronic equipment has led to unprecedented volumes of poorly managed e-waste, posing serious ecological risks. Although data on individual chemical substances in e-waste are available, evidence of ecotoxicity from actual e-waste materials remains scattered. This review consolidates organism-level ecotoxicity data on real e-waste samples (mixed fractions, fragments, leachates) and samples collected near e-waste facilities (soil, sediments, dust, water) across aquatic and terrestrial environments. It critically examines how methodological approaches influence reported outcomes and outlines research priorities. In aquatic environments, toxic responses vary with increased amounts of toxicants (dissolved metals, particles from dismantling operations) that mobilise to surface waters, while hydrophobic organic compounds cause sublethal behavioural and genotoxic effects. The few studies on terrestrial environments show impaired invertebrate growth and reproduction, along with changes in soil and “plastisphere” microbiota. However, tested concentrations, material complexity, and incomplete reporting of exposure chemistry, among other factors, limit the environmental relevance and comparability of the data. Uniformised procedures, combined with thorough chemical characterisation, environmentally realistic conditions, and cross-system bioassays (including different exposure routes and cumulative assessments), may provide mechanistic insights into e-waste toxicity, supporting evidence-based risk management strategies while contributing towards the development and validation of robust new approach methodologies (NAMs). Full article

Brominated Flame Retardants (BFRs), widely used in Electrical and Electronic Equipment (EEE), pose severe health and environmental risks and complicate recycling at the end-of-life stage, calling for innovative, sustainable detection and sorting solutions. In this context, new strategies that are efficient, reliable, sustainable, and cost-effective are required. This study investigates Short-Wave Infrared (SWIR) spectroscopy for detecting brominated plastics and quantifying bromine (Br) and antimony (Sb) content in Cathode-Ray Tube (CRT) e-waste. X-Ray Fluorescence (XRF) provided reference measurements, while Support Vector Machine (SVM) models were trained on reflectance spectra acquired with a portable spectroradiometer. The SVM–Discriminant Analysis models achieved near-perfect classification, with 100% accuracy in distinguishing samples above and below the regulatory thresholds for Br (2000 mg/kg) and Sb (8354 mg/kg). SVM regression yielded excellent quantitative predictions, with R²_P = 0.996 and RMSEP = 2671 mg/kg for Br, and R²_P = 0.999 and RMSEP = 1056 mg/kg for Sb. These performances confirm the robustness of SWIR spectroscopy for rapid, non-destructive monitoring of hazardous plastics, even in highly heterogeneous waste streams. The integration of SWIR spectroscopy with machine learning supports selective recycling and safer resource recovery, directly contributing to United Nations Sustainable Development Goals on Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), and Responsible Consumption and Production (SDG 12). Full article

The growing use of electrical and electronic appliances, coupled with shorter product lifespans, has accelerated the rise in waste electrical and electronic equipment (WEEE). Accurate forecasting is essential for addressing environmental challenges, conserving resources, and advancing the circular economy (CE). This research employs a Trigonometry-Based Discrete Grey Model (TBDGM(1,1)) that integrates the Jaya algorithm and Least Squares Estimation (LSE) for parameter estimation. By leveraging Jaya’s parameter-free robustness and LSE’s computational efficiency, the model enhances prediction accuracy for small-sample and nonlinear datasets. WEEE data from Washington State (WA) in the USA and Türkiye are utilized to validate the model, demonstrating cross-context adaptability. To evaluate performance, the model is benchmarked against five state-of-the-art discrete grey models. For the WA dataset, additional benchmarking against methods used in prior e-waste forecasting literature enables a dual-layer comparative analysis, which strengthens the validity and practical relevance of the approach. Across evaluations and multiple performance metrics, TBDGM(1,1) attains satisfactory and competitive prediction performance on the WA and Türkiye datasets relative to comparator models. Using TBDGM(1,1), Türkiye’s e-waste is forecast for 2021–2030, with the 2030 amount projected at approximately 489 kilotones. The findings provide valuable insights for policymakers and researchers, offering a standardized and reliable forecasting tool that supports CE-driven strategies in e-waste management. Full article

Waste Electrical and Electronic Equipment (WEEE) management is a critical global challenge. This study proposes a model for the WEEE Transportation Problem using advanced evolutionary algorithms such as the Genetic Algorithm (GA), the Offspring-Selected Genetic Algorithm (OSGA), the Evolution Strategy (ES), and the Offspring-Selected Evolution Strategy (OSES). These algorithms, which are part of the field of Artificial Intelligence (AI), are applied to optimise transportation routes, minimising time and costs, and promoting sustainability by reducing the carbon footprint. Test instances and solutions are presented to demonstrate the feasibility of the model and the effectiveness of the proposed algorithms. Rather than providing technical detail, the focus is placed on the novelty of applying these algorithms to the WEEE Transportation Problem in Mexico, particularly for minimising operational cost. While reductions in carbon emissions are discussed as a natural consequence of cost optimisation, a formal dual-objective formulation is beyond the present scope and is identified as a direction for future work. Full article

This study investigates the operational performance and optimization of a real greywater treatment system utilizing aluminum (Al)-based electrocoagulation (EC). The EC process was systematically evaluated and optimized through Response Surface Methodology (RSM) using the Box–Behnken Design (BBD), focusing on three critical parameters: pH, current density, and electrolysis time. Greywater samples collected from domestic sources were characterized by key physicochemical parameters including pH, COD, TSS, turbidity-ty, and electrical conductivity. The electrochemical treatment was conducted using a batch reactor equipped with Al electrodes in a monopolar configuration. Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD), and Fourier-Transform Infrared Spectroscopy (FTIR) were employed to characterize both the electrodes and the generated sludge. Results revealed a maximum COD removal efficiency of 86.34% under optimized conditions, with current density being the most influential factor, followed by its significant interaction with pH. The developed quadratic model exhibited high predictive accuracy (R² = 0.96) and revealed significant nonlinear and interaction effects among the parameters. Sludge characterization confirmed the presence of amorphous aluminum hydroxide and oxyhydroxide phases, indicating effective coagulant generation and pollutant capture. The treated greywater met physicochemical criteria for non-potable reuse, such as agricultural irrigation, supporting resource recovery objectives. These findings demonstrate that EC is a low-waste, chemically efficient, and scalable process for decentralized wastewater treatment, aligning with the goals of sustainable chemical engineering. Full article

In this research, thermoplastic waste (polyethylene and propylene) from waste electrical and electronic equipment (WEEE) was used to manufacture polymer composite materials that included talc, fly ash, and elastomers, with tailored electromagnetic interference shielding properties, for the potential use for electric car components. A distribution of inorganic components within the polymer structures without particle clustering were observed, illustrating an effective melt compounding process. The gradual replacement of talc with fly ash lowered both the fluidity index and the softening temperature values. The increase in fly ash content resulted in higher values of both permittivity and dielectric loss factor. The novelty was related to a significant increase in both dielectric characteristics at increased quantities of fly ash at higher temperatures, an aspect more relevant at higher frequencies where they approached a steady value. The permittivity values surpassed five, and the dielectric loss factor values exceeded 0.04, fulfilling the requirements for their application in electrical equipment. The recipes containing 10% fly ash may guarantee an electromagnetic shielding effectiveness of at least 99% within the frequency domain of 0.1–4 GHz. Composites with greater amounts of fly ash can conduct heat more efficiently, leading to improved diffusivity and thermal conductivity values, with significant thermal conductivity values surpassing 0.2 W/(m*K). Finally, it was concluded that the composites with 10% talc, 10% fly ash, and elastomer using recycled high-density polyethylene might be the best choice for electric vehicle parts, in line with all required standards for these uses. Full article

The circular economy (CE) encourages sustainability by shifting towards business models that prolong resource use. The quantity of waste generated by electric and electronic equipment (WEEE or e-waste) is rapidly rising and is one of the fastest-growing waste streams. Our paper explores the awareness, benefits, barriers, incentives, and implementation of CE in the electric and electronic equipment (EEE) industry in Slovenia and Croatia. In the theoretical section, we review the existing literature on CE, the EEE industry in both countries, and their policy frameworks for developing CE. Based on primary research through a survey and inferential statistical analysis in the empirical section, we investigate the implementation of CE in the EEE industry in both countries. This paper identifies which CE practices companies in the EEE industry implement, how company size affects CE implementation, how companies evaluate institutional support to CE, and their plans regarding CE. The results highlight some differences between the two countries. Drawing on the empirical findings, we determine key challenges and opportunities for increasing CE adoption in this sector and formulate some European suggestions for policymakers, industry practitioners, and researchers. Full article

In the context of the Carbon Generalized System of Preferences (CGSP), this paper develops a three-tier reverse supply chain model comprising the government, recyclers, and consumers. Differential game analysis is employed to investigate the evolutionary dynamics of consumers’ perceived recycling value and to examine how government recycling efforts and recyclers’ point rewards levels influence this perception. Furthermore, the study explores the dynamic trajectory of consumers’ perceived recycling value across three decision-making models—collaborative-driven, government-driven, and market-driven—and evaluates its impact on supply chain efficiency. The research shows that (1) enhanced recycling efforts by both the government and recyclers significantly improve consumers’ perceived recycling value, thereby promoting the recycling of waste electrical and electronic equipment, with the most pronounced improvement observed under the collaborative-driven strategy; (2) in the government-driven model, the government’s subsidy rate affects recyclers’ decisions regarding point-based incentives but does not influence the government’s own recycling effort; (3) the evolutionary trajectories of consumers’ perceived recycling value and system efficiency differ among the models, with the highest levels achieved under the collaborative-driven model, followed by the government-driven model, and the lowest under the market-driven model. This study fully accounts for the dynamic nature of consumers’ perceived recycling value, offering theoretical and practical guidelines for effective WEEE recycling. Full article

This study investigates the use of waste glass powder derived from fluorescent lamps as a partial replacement for cement in mortar production, aiming to valorize this Waste from Electrical and Electronic Equipment (WEEE) and enhance sustainability in the construction sector. Mortars were formulated by substituting 25% of cement by volume with glass powders from fluorescent lamp glass and green bottle glass. The experimental program evaluated mechanical strength, durability parameters and ecotoxicological performance. Results revealed that clean fluorescent lamp mortars showed the most promising mechanical behavior, exceeding the reference in long-term compressive (54.8 MPa) and flexural strength (10.0 MPa). All glass mortars exhibited significantly reduced chloride diffusion coefficients (85–89%) and increased electrical resistivity (almost 4 times higher), indicating improved durability. Leaching tests confirmed that the incorporation of fluorescent lamp waste did not lead to hazardous levels of heavy metals in the cured mortars, suggesting effective encapsulation. By addressing both technical (mechanical and durability) and ecotoxic performance, this research contributes in an original and relevant way to the development of more sustainable building materials. Full article

Plastics make up a significant proportion of the stream of the European Waste of Electric and Electronic Equipment (WEEE), yet the use of recycled plastic materials is very low in new manufactured products. A description of the WEEE waste stream in Europe is given, with a focus on the plastic materials commonly found in WEEE that include four principal polymers: polypropylene (PP), polycarbonate (PC), acrylonitrile-butadiene-styrene (ABS) and polystyrene (PS). Furthermore, the legislative aspects related to WEEE and plastics recycling in Europe are complex, and numerous norms have been dictated by the European Commission. These norms are crucial to the sector of polymer recycling and production in Europe. Moreover, an overview of the entire treatment chain is presented. More specifically, each step of a typical recycling chain is introduced, with a focus on the sorting of plastics and the separation of polymers. Lastly, the influence of contaminants in the plastic fraction is discussed, both in terms of polymer particles and unwanted additives. By showing the impact of the purity rate on the mechanical properties of recycled plastics, the consequences of inadequate end-of-life treatment for WEEE-plastics is highlighted, hence linking the quality of recycled plastics to the separation step and the re-compounding of recycled granulates. Full article