Search Results (417)

The global shift toward carbon-neutral energy systems has renewed interest in biorefineries as integrated platforms for the sustainable production of fuels, chemicals, and materials. In this context, lignin, the second most abundant natural polymer and the only renewable source of aromatic carbon, has gained attention as a promising feedstock for high-value applications. Despite its high energy density and chemically complex structure, lignin is primarily used as a low-value fuel through combustion, a practice that fails to capitalize on its molecular potential and offers minimal energetic and economic benefits to the industry. Unlocking its value requires a fundamental shift toward energy-efficient valorization strategies that minimize external energy input while retaining carbon in marketable products. To enable a comprehensive evaluation of this shift, this perspective introduces a three-criterion framework—operating below 250 °C and 50 bar, achieving a fossil energy ratio above one across all process steps, and retaining more than 40% of lignin carbon in recoverable products—and applies it to critically evaluate four lignin valorization pathways: catalytic depolymerization, solvent-assisted fractionation, biological and electrochemical conversion, and material-based applications. Across all pathways, system-level integration, namely, separation, solvent recycling, and catalyst generation, constantly influences the overall energy balance and represents the field’s unresolved challenge. To address these barriers, this perspective discusses several future research directions spanning advanced catalyst design, biotechnology, computational tools, and process intensification, alongside the policy and economic measures needed to enable the commercial deployment of integrating lignin valorization with existing biorefinery operations. Collectively, these insights aim to elevate lignin from an underutilized by-product to a foundational resource for circular, low-carbon bioeconomy. Full article

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained. Full article

This study explores the potential of biomimetic reasoning to inform early-stage design thinking, with a focus on enhancing the consideration of material utilization and textile waste. While sustainability efforts within the field of textiles are often focused on recycling and end-of-life management strategies, it is important to recognize that a substantial proportion of final waste-related outcomes are determined during the conceptual design stage and the initial prototyping iterations. This study investigates the potential of organizational principles derived from natural systems to inform the definition of problems, the generation of ideas, and early conceptual prototyping. This is achieved by the introduction of ecological constraints and material life-cycle awareness in conjunction with user-centered requirements. To address the conceptual gap between biological forms and manufacturing, biomimicry is approached as a mode of systemic reasoning, utilizing topological skeletonization as a tool for logic extraction rather than formal imitation, with emphasis placed on continuity, modularity, and adaptive organization. This computational proof-of-concept employs a Particle Swarm Optimization (PSO) framework, utilizing biological venation as a topological guide to demonstrate how distinct organizational logics influence pattern configuration while incorporating manufacturing-inspired constraints (such as path continuity and density) as optimization penalties. The findings are exploratory in nature and are confined to the computational domain; while the study utilizes proxy indicators to simulate potential textile behaviors, it acknowledges the lack of direct experimental validation of physical fabrication as a current limitation. By framing waste as an outcome of upstream design choices, this paper contributes a methodological perspective. This perspective places biomimetic design thinking as a reflective tool within sustainable and regenerative design practice. It also supports earlier engagement with ecological considerations in textile development. Full article

Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red–Green–Blue (RGB) and red–green–blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing. Full article

The transition from a linear to a circular, resource-efficient economy is crucial in order to address the growing scarcity of resources, environmental degradation and the rapid increase in electronic waste and end-of-life products. Artificial Intelligence (AI) has emerged as a key enabling technology, capable of enhancing decision making, automation and optimization across Circular Economy (CE) pathways, including reuse, remanufacturing and recycling. This perspective paper presents a comprehensive and critical overview of AI’s role in supporting the transition to a circular, resource-efficient economy, introducing the Digital CE Architecture (DCEA-4) as a novel framework for integrating AI across the circular value chain. Recent advances in machine learning, deep learning and data-driven optimization are analyzed in the context of electronic waste and used battery management. This highlights how AI-based solutions can improve material recovery rates, reduce environmental impact and enhance system-level efficiency. Additionally, we examine major challenges concerning data availability, model generalization, industrial deployment, and explainability, together with relevant industrial case studies. Although AI offers substantial potential for optimizing circular resource systems, its environmental benefits must be balanced against the computational energy demands of large-scale AI models. This perspective discusses the potential rebound effects associated with AI deployment and emphasizes the importance of energy-efficient algorithms and sustainable digital infrastructures. By bringing together current developments and highlighting future opportunities, this paper aims to help researchers, practitioners and policymakers leverage AI to speed up the transition to sustainable, circular and resource-efficient systems. Full article

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

The industrial dismantling and recycling of end-of-life photovoltaic (PV) modules require robust visual inspection under dust contamination, inter-class similarity, and constrained edge-computing conditions. This study proposes an end-to-end framework that detects key module components (junction box, backsheet label, aluminum frame, and shadow region) and estimates the aluminum frame gap height for dismantling control. The primary novelty is a dust erosion-aware detection and metrology framework that couples frequency-enhanced visual perception with shadow-guided geometric measurement, while lightweight deployment modules serve as supporting engineering components. Specifically, DWT/FFT-based enhancement with CLAHE is used to improve degraded features, and YOLOv8 is strengthened by GSConv and Coordinate Attention in the backbone and neck; transfer learning, INT8 quantization-aware training, and CMFH-based compact rechecking are further introduced for practical deployment. Experiments show that the proposed method improves mAP@0.5 by 5.08 percentage points over baseline YOLOv8 while increasing speed from 45 to 52 FPS. For geometric metrology, the method achieves 93.0% accuracy with a mean error of 0.45 mm. The results demonstrate an accurate, robust, and edge-deployable solution for the automated inspection and recycling of end-of-life PV modules under dusty conditions. Full article

The rapid growth of polyethylene terephthalate (PET) waste and the limitations of conventional recycling methods for mixed waste streams emphasize the need for chemical recycling routes that deliver high-value monomers in a sustainable, resource-efficient manner. This work explores N-heterocyclic carbenes (NHCs) as organocatalysts for the glycolysis of PET with ethylene glycol to bis(hydroxyethyl)terephthalate (BHET), aiming for milder conditions and higher activity. A systematic catalyst screening links steric and electronic properties (percent buried volume, Tolman electronic parameter) of the NHCs to performance in the glycolysis process, resulting in a catalyst system with high PET conversion (up to 97%) and BHET yield (up to 65%). Mechanistic investigations (experimental and computational) support an anionic activation pathway for glycolysis. To lower the reaction temperature, selective cosolvent systems were explored, albeit with some loss of catalytic activity. Cooperative catalysis combining NHCs with Lewis acids enhances activity, leading to a high conversion (up to 90%) while maintaining lower temperatures than state-of-the-art glycolysis methods. The process was successfully transferred to post-consumer waste streams to validate the practicality. Full article

In the transition toward the circular economy and high-rate manufacturing, thermoplastic composites (TPCs) are increasingly outperforming conventional thermosets due to their superior fracture toughness, recyclability, and rapid processing capabilities. Among available joining techniques, fusion bonding stands as the main mechanism for structural integration, as it bypasses the fundamental limitations of traditional assembly: the weight penalties and stress concentrations inherent in mechanical fastening, as well as the long cycle times and interfacial weaknesses often associated with adhesive bonding. This paper provides a comprehensive evaluation of welded TPC joints through a dual-methodological approach: a historical narrative review tracing the evolution of fusion bonding principles, and an in-depth literature review of 25 key articles published since 2015. The analysis focuses on the intersection of experimental characterization—quantifying interfacial strength and fracture energy—and numerical modeling techniques, such as Cohesive Zone Modeling (CZM) and progressive damage analysis. By categorizing recent advancements into specific thematic pillars, this study correlates process-induced phenomena with macro-scale mechanical performance and virtual predictive accuracy. The findings synthesize decades of foundational knowledge with cutting-edge research trends, highlighting the transition from empirical testing to computational design. This work serves as a roadmap for achieving standardized, high-performance thermoplastic assemblies in safety-critical applications. Full article

This paper investigates the fatigue and residual strength behavior of recycled carbon fiber reinforced plastics (rCFRPs) with different fiber architectures in an epoxy resin matrix: a unidirectional (UD) rCFRP and a non-crimp fabric (NCF) composite. Due to the research gap in fatigue testing of recycled carbon fiber-reinforced plastics with quasi-continuous fiber reinforcement, their fatigue properties are investigated in this article. The objective of the present study is to contribute to the broader goal of integrating recycled carbon fibers as quasi-continuous fiber reinforcement in structural applications by understanding their failure behavior. To determine suitable stress levels for fatigue testing, quasi-static tensile tests are conducted first. Subsequently, fatigue tests are performed with a stress ratio of 0.1. Damage evolution is documented by a continuous recording of the stiffness degradation. For the unidirectional material, an S-N_f curve is created based on three stress levels. The curve can be described with a logarithmic equation. Fatigue testing of the NCF laminate is performed at a single stress level. Subsequent residual strength tests using standard specimens show no clear correlation between the number of load cycles of pre-cycling and residual strength, but indicate a sudden-death behavior for both composites. For further investigation of the damage behavior, in situ residual strength tests are carried out using a combination of acoustic emission analysis and micro-computed tomography (µCT) imaging. This investigation is intended to illustrate crack initiation and propagation three-dimensionally after pre-cycling and during residual strength tests. The results demonstrate a significant influence of the microstructure on the failure behavior. Full article

This study examines the impact of recovered steel fibers (WTSFs) and waste tire aggregates of varying sizes—fine (FWTR), small coarse (SCWTR), and large coarse (LCWTR)—on the compressive strength of concrete subjected to elevated temperatures. Forty mixes were formulated utilizing four distinct WTR replacement ratios (0%, 5%, 10%, 20%) and four WTSF doses (0%, 0.5%, 1%, 2%), and evaluated at temperatures of 24 °C, 100 °C, 200 °C, and 300 °C. The findings indicate that elevated temperatures consistently diminish compressive strength, although the reference concrete saw around 18% loss at 300 °C, with WTR-containing mixes demonstrating losses ranging from 25% to 45%, contingent upon rubber size and dose. The type of WTR was critical—LCWTR mixes exhibited superior residual strength retention due to enhanced particle–matrix interlocking, whereas FWTR mixtures saw the most significant decline. The inclusion of WTSF increased strength by 2–10% at 0.5–1.0% fiber content through crack bridging, but excessive fiber addition (2.0%) decreased workability and caused clustering, leading to up to 40% strength loss. The ideal combination was 5LCWTR–1WTSF, which sustained 36.97 MPa at 24 °C and 29.65 MPa at 300 °C, indicating superior performance across all temperature ranges. Predictive modeling utilizing machine learning techniques (SVR, KRR, 1D-CNN, and DRL) corroborated the experimental results, with the CNN attaining the maximum generalization accuracy (R² = 0.9374) and the KRR exhibiting the most consistent performance (R² = 0.9305). The models indicated that WTR and temperature were the primary variables diminishing strength, although modest WTSF ratios enhanced overall thermal resilience. SHAP and ALE analysis further validated that WTR content exhibited the most significant negative feature contribution (~−6 MPa), succeeded by temperature, although modest fiber inclusion demonstrated a positive SHAP effect (+2–4 MPa), corroborating the experimentally observed non-linear reinforcement threshold. The combined experimental–computational framework demonstrates that the combination of coarse rubber aggregates (5–10%) with appropriate WTSF content (0.5–1.0%) improves sustainability and high-temperature durability. The integration of physical testing and interpretable AI modeling creates a hybrid approach that can anticipate and enhance thermo-mechanical performance in sustainable concrete systems. Full article

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor. Full article

The growing volume of waste electrical and electronic equipment presents both an environmental challenge and an opportunity for recovering critical raw materials embedded in discarded products. While recycling technologies are advancing, effective recovery remains strongly constrained by upstream collection systems, particularly in urban contexts subject to uncertainty, capacity limits, and regulatory constraints. This paper examines WEEE collection as a key lever for supporting sustainable critical-metal recovery in Europe. Methodologically, the study combines a Scopus-based bibliometric mapping and an institutional analysis of EU collection arrangements with the development of a robust multi-period mixed-integer linear programming model. After analysing organisational and regulatory arrangements in Poland and Portugal as illustrative cases, the paper introduces the Robust Multi-Period WEEE Allocation and Rare Metal Accumulation Problem (MP-WARMAP). The model integrates uncertain WEEE availability, intertemporal logistics planning, threshold-based rare-metal accumulation with endogenous sale timing, and a binding transport-related emission cap. Computational experiments show that robustness against inflow uncertainty can be achieved at a relatively low economic cost, that emission regulation exhibits a feasibility-threshold effect, and that capacity constraints may dominate price signals in determining recovery timing. The results highlight the importance of collection-system design and operational feasibility for improving the recovery of critical materials from urban WEEE streams. Full article

An industry 5.0 revolution is characterized by advanced automation and human-centric design resulting in an unprecedented growth in the electronics sector. This advancement comes at the cost of a surge in electronic waste (E-waste) generation. In the past, many researchers have reported on E-waste recycling and management; however, the efficient collection of domestic E-waste still remains a critical challenge. This research paper presents a novel approach to domestic E-waste management by developing a smart E-Bin equipped with an Electronic Waste Detection and Bin-Level Control System (EDBLCS), IoT setup, and a YOLOv11-powered (EW YOLO) computer vision system. This innovative solution selectively collects only E-waste, ensuring accurate identification and preventing contamination with other waste streams, with the mAP@0.50 score increased to 0.90074 by Epoch 50, while mAP@0.50–0.95 reached 0.73899 using YOLOv11. The primary contribution of this work is the integration of YOLOv11-based real-time detection with an IoT-enabled smart E-Bin framework to enable selective, edge-oriented domestic E-waste segregation. Full article

Automated waste classification is essential for improving recycling efficiency and supporting sustainable waste management systems. However, conventional convolutional neural network (CNN) approaches primarily focus on localized feature extraction, which may limit their ability to capture complex spatial relationships in heterogeneous waste materials. This study proposes DeepWasteSort-SI-SSO, a Vision Transformer (ViT)-based framework enhanced with a Self-Improved Sparrow Search Optimization (SI-SSO) strategy for hyperparameter tuning. The optimization process focuses on key training parameters, including learning rate, batch size, and dropout rate, to improve convergence stability and reduce the risk of suboptimal local minima. The framework was evaluated on a balanced four-class waste image dataset (paper, wood, food, and leaves; N = 4000) using a five-fold cross-validation protocol. Experimental results achieved an average accuracy of 95.5% (±0.007), a macro-averaged AUC-ROC of 0.975, and a Cohen’s Kappa coefficient of 0.938, indicating strong agreement between predicted and true labels. Comparative experiments against ResNet-50 and a baseline ViT configuration suggest that SI-SSO optimization improves performance stability with only a modest increase in computational cost. These findings highlight the potential of optimized Transformer-based approaches for automated waste image classification under controlled evaluation conditions. Full article