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.

Purpose This study aims to develop a sustainable approach to textile colouration, which is imperative for a clean environment. Black pigment is a commonly used pigment in textiles, sourced from fossil fuels, contributing to significant health and environmental concerns.Design/methodology/approach An attempt has been made to utilise cotton and okra biochar derived from their respective waste materials, as a black pigment for textile printing. It was synthesised at four pyrolysis temperatures, e.g. 700 degrees C, 800 degrees C, 900 degrees C, 1,000 degrees C and subsequently printed onto the 100% cotton fabric through screen-printing technique. The printing results were compared with the fabrics printed using conventional black pigment for textile, conventional carbon black, cotton and okra biochar from waste. Colour properties of printed fabrics like shade depth (K/S), colour fastness properties, as well as other physical properties were investigated along with FTIR and SEM analysis.Findings The colour properties of biochar printed fabric were improved by increasing pyrolysis temperature of biochar synthesis, and maximum colour properties were exhibited by the printed fabrics when cotton and okra biochar were synthesised at 900 degrees C. Biochar printed fabrics exhibited shade depth values ranging from 7.14 to 3.68, while the conventional pigment printed fabric exhibited shade depth in the range of 3.81-3.49, and conventional carbon black exhibited shade depth in the range of 3.71-3.39. Abrasion resistance and air permeability properties of the biochar printed fabrics were also comparable to conventional black pigment.Originality/value The black pigment is obtained from post-consumer textile and plant waste, and it was successfully applied onto textile fabric with superior shade depth and performance properties. Thus, providing various socio-economic and environmental benefits.

PurposeConventional and certain toxic finishes for polyester fabric are more effective than bio-based finishes. The purpose of this study it to improve the performance of the eco-friendly and bio-based finishes through innovative methods.Design/methodology/approachThis research presents novel ozone-based textile finishing processes for polyester fabric using different finishes. Resins (Dimethylol dihydroxy ethylene urea and citric acid), water repellent finishes (Phobotax and stearic acid), flame retardant finishes (Pyrovatax and diammonium hydrogen phosphate) and softener finishes (Silicone, polyethylene and fatty acid) are used. Consequently, this research evaluates the performance of bio-based finishes through three novel ozone treatment processes. These processes are pre-ozonation, in situ ozonation and post-ozonation. Unexposed samples are considered benchmark samples. Ozone is used in a controlled manner at low dosage.FindingsThe effectiveness of the novel ozone-based finishing processes for polyester fabric has been confirmed with relevant finish performance tests. FTIR analysis, air permeability, absorbency and wicking results also support enhanced performance of ozone-exposed polyester fabric as compared to the benchmark.Originality/valueTypically, ozone is reported as a color or finish destroyer, such as in wastewater treatment or bleaching, at much higher dosages. This research reported the ozone-based bio-finishing and conventional finishing processes for the four most-used finishes under controlled ozone dosages.

This research investigates the conventional application of bio-based and synthetic finishes on digitally printed textiles. While digital inkjet printing was used for coloration, all finishes were applied using traditional padding techniques. The performance of the bio-based finishes including fatty acid (softener), citric acid (crease resistance), diammonium hydrogen phosphate (flame retardant), and stearic acid (oil and water repellent) were compared with synthetic analogues such as silicon softeners, DMDHEU resin, phosphorus-based flame retardants, and fluorinated oil and water repellents. Fabrics studied were cotton, polyester, polyester-cotton blend, and silk. The bio-based finishes provided competitive functional performance on digitally printed textiles. This work highlights a sustainable alternative in textile finishing without compromising key performance parameters.

Conventional acid pad dyeing of silk fabric produces substantial effluent attributable to its high liquor ratio. Sustainable methodologies are under investigation within the textile manufacturing sector to enhance material and product processing efficiency. Foam technology has recently emerged as a viable alternative for textile pre-treatment, coloration, and finishing. Nevertheless, this economical approach encounters challenges in foam generation, stability, performance, and optimization for dyeing diverse fabrics. The present study examines foam formulations incorporating three primary acid dyestuffs and their application in foam dyeing of silk fabric. Color strength (K/S value), fastness properties and tensile strength of foam-dyed and pad-dyed fabrics were evaluated. Findings indicate that optimized acid foam dyeing recipes, employing sodium laureth sulphate (SLES), sodium dodecyl sulphate (SDS) and sodium phosphate (SP) and sodium-tripolyphosphate (STPP) as stabilizers at pH 4.5, were applied using a foam coating machine. The foam-dyed silk fabrics exhibit color strength of dyed and fastness comparable to pad-dyed equivalents, with substantial reductions in water, energy, and chemical consumption, alongside minimized wastewater pollutants-facilitating zero discharge of hazardous chemicals (ZDHC) compliance at pilot scale. This methodology equates pad dyeing performance across 12 parameters and supports sustainable coloration of cotton (reactive/vat), polyester (disperse), and silk (acid) substrates.

Wastewater from textile dyes remains a significant environmental concern. The demand for sustainable, efficient, and environmentally friendly treatment technologies has accelerated the development of advanced filtration systems. Among these, polymeric membranes are the most effective approach due to their ease of fabrication, cost-effectiveness, and excellent selectivity for Methylene Blue (MB) remediation. In this study, PTh/ZnO-based PVDF membranes were fabricated using the phase inversion technique. The prepared membranes (PM-1 to PM-5) were characterized by Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), tensile testing, and contact angle measurement. The PM-5 membrane achieved 85% porosity owing to nanocomposite-induced interconnected pore formation. Moreover, the contact angle decreased from PM1-PM5, indicating enhanced hydrophilicity driven by hydroxylated ZnO and surface roughness. Membrane performance was evaluated through photocatalytic degradation of a representative dye pollutant, exhibiting efficiencies of 91%, 95%, and 97% for PM-3, PM-4, and PM-5, respectively. In comparison, pristine PVDF exhibited limited photocatalytic activity, achieving only 32% MB degradation. Density functional theory (DFT) calculations further revealed enhanced electronic interactions and charge transport within the PTh/ZnO-PVDF composite, supporting its improved photocatalytic activity. These findings highlight PTh/ZnO-PVDF membranes, offering a viable route toward clean water technologies and practical applications in dye-intensive industries.

This paper introduces a sustainable circular-economy plan of transforming waste cotton fabric into useful carbon materials through controlled pyrolysis and assesses their application as epoxy composite strengthening agent. Scanning electron microscopy and Raman spectroscopy confirmed that the semi-graphitic carbon had retained the fibrous morphology and porous surfaces of the cotton precursor. Composites with 1 wt% and 3 wt% of cotton-based carbon were compared to the composites with the commercial multi-walled carbon nanotubes (MWCNTs). Cotton-based carbon composites with 3 wt% loading exhibited about 90% greater ultimate tensile strength, 150% greater tensile toughness and a moderate increase in Young’s modulus, compared to MWCNT composites. Tribological tests showed the presence of lower friction as a result of the fibrous morphology and consistent carbon-rich transfer layers. The electrical conductivity testing further indicated that carbon composites made of waste cotton came close to that of the MWCNT-based composites. These results indicate that carbon based on cotton is a low-cost, eco-friendly additive for achiving mechanically tough, electrically active, and tribologically improved polymer composites.

The extraction of critical raw materials (CRMs) from waste printed circuit boards (WPCBs) represents a crucial challenge for the economy, particularly in the electronics sector. To improve the concentration of individual CRMs and facilitate their extraction from WPCBs, we propose an innovative approach based on machine learning and computer vision to guide mechatronics system in selectively disassembling various types of electronic components from WPCBs. Our approach addresses the need for a robust and automated solution to identify and localize valuable components that contain CRMs. Specifically, we use YOLOv5 and YOLOv8 models, which are the state of the art convolutional neural networks (CNN) for detection and localization tasks. Our pipeline, called EC-Det, starts from collecting a custom image set called V-PCB, and obtains a large collection of 10,829 high-quality images of WPCBs. These images are used to train the YOLO family variants which show excellent accuracy. Furthermore, we consider not only accuracy but also memory usage, training time, and inference time for our CNN models, evaluating their suitability for resource-constrained on the edge systems. Our results show that integration of machine vision system provides a highly effective and reproducible solution for guiding automated mechatronics system, thereby enhancing the efficiency of the CRMs extraction process.

Purpose This study aims to create a sustainable valorization pathway for textile waste through recycling of textile waste into useful carbon black (CB) fillers in high-performance epoxy composites. This technique not only successfully recycle various textile waste but also leads to additional energy rich bio-oil as well.Design/methodology/approach In this paper, researchers explore a sustainable valorization route through pyrolysis of most commonly used textile fabrics; cotton, polyester and polyester cotton (PC) fabrics to recover functional CB and high calorific bio-oil. The solid carbon residue was then applied as a filler in epoxy composites. Scanning electron microscope and Raman analyses revealed distinct CB morphologies and ID/IG ratios (0.82-0.88), confirming graphitic structure.Findings At 4% loading, cotton derived CB showed the best balance of properties with enhanced resilience (+46%), high elongation and 54% lower friction than neat resin, surpassing commercial carbon black. Polyester and PC CB provided higher breaking stress, while pyrolysis also produced energy rich bio-oils, highlighting dual benefits in composites and fuels.Originality/value This approach offers an eco-friendly novel solution for textile waste having different fiber content with additional advantage of energy recovery and advanced composite reinforcement within a circular economy framework.

Risk perception is crucial for making effective fisheries management strategies. However, this role of risk perception needs to be addressed, particularly in developing countries. Published literature documents such a scenario in the case of Pakistan, which results in a decreased economic contribution to the fisheries sector. Despite its importance, the role of risk perception in managing the fisheries sector is absent in online scientific studies. The present study strives to address this research void by analyzing survey-based data collected through snowball sampling between May 2022 and October 2024. Multivariate analysis, viz., Structure Equation Modeling (SEM), was done through Statistical Package for Social Sciences (SPSS) as well as Analysis of Moment Structures (AMOS). Cronbach’s alpha values for all constructs were above 0.6, with the highest being 0.962 for policies and regulations risk, confirming data reliability. Confirmatory Factor Analysis (CFA) indices, including Comparative Fit Index (CFI) (0.933) and Tucker-Lewis index (TLI) (0.916), indicated a good model fit, with acceptable construct reliability (CR) and Average Variance Extracted (AVE) values. SEM showed that economic risk (estimate = -0.425, p = 0.000), environmental risk (estimate = -0.251, p = 0.007), and consumption risk (estimate = -0.265, p = 0.000) negatively impacted performance, while policies and regulations risk (estimate = -0.113, p = 0.121) and infrastructure and logistics risk (estimate = -0.073, p = 0.411) were insignificant. Risk perception was a significant mediator of performance, with varying effects across Sindh and Balochistan. According to the survey participants, there is a dire need to increase levels of fisheries risk perception, which can be achieved through properly designed capacity-building and incentive-based management techniques. Furthermore, this study discusses the practical implications and limitations.

Semiconductor heterostructure photocatalysts are gaining attention for environmental applications due to their effectiveness in decomposing pollutants under solar light. In the present study, efficient Fe:ZnO and Cu:ZnO photocatalysts were synthesized by hydrothermal methodology. The structure, elemental composition, and morphology of prepared pristine and transition metal (Fe and Cu)-doped ZnO photocatalysts were evaluated using FTIR, XRD, and SEM–EDX, respectively. The results showed the hexagonal structure of pristine ZnO and doped ZnO heterostructures. The band gap energy (Eg) and photocatalytic performance against methylene blue (MB) were tested, revealing 94

Considering the worldwide trend of embracing recycling for a greener future, this study transforms cotton-based textile waste into eco-friendly carbon black. It reports an innovative synthesis of carbon black from 100% cotton-based post-consumer textile waste by pyrolysis in an inert nitrogen environment at three temperatures. The synthesized carbon black was applied as a reinforcing material in epoxy resin composites in five different concentrations. Commercial carbon black N-660 was used as a benchmark material. The best results were exhibited by carbon black when synthesized at 950 degrees C. Morphology analysis confirmed the agglomerate formation of commercial carbon black and the dispersed nature of the prepared carbon black. Raman analysis confirmed defect reduction and increased graphitization of carbon black by increasing pyrolysis temperature. Also, the epoxy composites exhibited improved mechanical properties after the addition of the cotton-based carbon black. Overall, the post-consumer textile waste was successfully converted into valuable textile composites with enhanced properties.

Waste Electrical and Electronic Equipment (WEEE) represents a rapidly growing waste stream globally, posing significant environmental and health risks due to its hazardous components. Simultaneously, WEEE contains valuable resources, including Critical Raw Materials, making its effective valorization crucial for a circular economy. This paper explores the integration of Artificial Intelligence (AI) into WEEE management to enhance sustainability and resource recovery. We discuss current challenges in WEEE management, review various AI technologies applicable to different stages of the WEEE life cycle, and highlight their potential to optimize collection, improve sorting and disassembly, and facilitate material valorization. Special attention is given to AI-powered solutions for electronic component detection on Waste Printed Circuit Boards (WPCBs) to enable the recovery of high-density CRMs. We also address the challenges and future directions for AI integration in WEEE management, emphasizing the need for robust data infrastructure, interdisciplinary collaboration, and supportive policy frameworks to realize a truly sustainable WEEE management system.

In this study, textile foam dyeing technique, a low wet pick-up and minimal add-on technique, has been applied to the cotton fabric to evaluate performance properties in comparison to the traditional pad-dry-cure dyeing method. The vat foam dyeing process was developed for cotton fabric by using three foaming agents along with two foam stabilizers. The foaming agents, sodium lauryl ether sulphate and sodium lauryl sulphate were investigated in combination with foam stabilizers sodium sulphate and sodium-tri polyphosphate using leuco vat dye. The foam dye formulation was optimized for two colors of vat dye and applied on fabric using a knife on roller type foam coating machine. Various foam dye formulations were used to achieve the required shade depth of the color. The performance properties of the foam dyed fabric were evaluated for shade depth (K/S value), color fastness to washing and color fastness, tearing strength and air permeability in comparison with pad-dry-cure vat dyed cotton fabric. The best results for vat foam dyed cotton fabric were achieved with all foaming agents in blend with foam stabilizer sodium tri-polyphosphate. The dye form formulation presented excellent results with the foam agent sodium dodecyl sulphate with the addition of the foam stabilizer sodium tri-poly phosphate.

The global agri-food system (AFS) is increasingly vulnerable to a complex web of economic, environmental, and geopolitical disruptions. This review paper critically examines the economic vulnerabilities embedded within agri-food supply chain (AFSC), focusing particularly on smallholder farmers, export-oriented economies, and the broader risks associated with globalization. Drawing on recent crises such as the COVID-19 pandemic and the Russia–Ukraine conflict, the paper explores how systemic shocks disrupt production, distribution, and consumption, leading to increased food insecurity, especially in the Global South. Key issues include limited financial access, infrastructural deficits, digital exclusion, and food price volatility. The paper highlights a range of mitigation strategies, including policy reform, digital technology adoption (e.g., blockchain, internet of things), local food system strengthening, financial risk transfer instruments, and collaborative capacity building. Through global case studies and critical analysis, the paper identifies persistent research gaps—particularly regarding informal food systems and the contextual adaptability of technological innovations. It calls for interdisciplinary approaches and multi-stakeholder cooperation to foster resilient, inclusive, and sustainable AFSs capable of withstanding future shocks. Moreover, this paper advances key Sustainable Development Goals by protecting smallholder livelihoods (SDG 1 and 2), promoting digital agriculture and infrastructure (SDG 9), improving supply chain transparency (SDG 12), and addressing climate risks with adaptive strategies (SDG 13). It lays a foundation for resilient and sustainable AFSs through policy and innovation.

Nanoparticles (NPs) exhibit unique physical and chemical properties that defy classical mechanics, owing to their quantum nature. These properties are dictated by size, shape, and structure, rendering NPs indispensable across diverse applications, including catalysis, medical imaging, drug delivery, and energy research. Advanced computational tools have become indispensable in unraveling the intricacies of nanomaterial behavior, driving significant progress in theoretical and computational research. Among these tools, the density functional theory (DFT) has emerged as a powerful method for predicting material properties. In this review study, we delve into key aspects of DFT simulations applied to nanomaterials, including Optimal Geometries, Band Gap and Electronic Properties, Density of States (DOS), Natural Bond Orbitals (NBO), and spectroscopic features (Infrared, Raman Spectra, and UV–Visible Spectra). Despite its successes, DFT faces limitations, particularly concerning semiconductor materials. Researchers strive to enhance its accuracy while maintaining computational efficiency. Balancing generically accurate functionals for specific applications remains an ongoing challenge. As nanomaterial continues to play a significant part in a variety of industries, the progress of DFT is of great interest and exploration. This review discusses DFT-based computational techniques employed for modeling nanomaterials. The calculations are generally done by utilizing generalized gradient approximation (GGA) functionals such as PBE (Perdew–Burke–Ernzerhof), and where necessary, hybrid functionals like B3LYP to enhance band gap accuracy. All calculations are performed using the standard quantum chemistry packages such as VASP, Gaussian, or Quantum ESPRESSO. This combination of methods offers a complete theoretical basis for the study of nanomaterial properties.

This study examines the effects of Free Trade Agreements (FTAs) on Pakistan’s seafood exports, specifically analyzing the China-Pakistan Free Trade Agreement (CP-FTA) and its stages, CP-FTAI and CP-FTAII. Using the gravity model (GM) of trade, it empirically analyzes the dynamics of seafood trade between Pakistan and its neighboring countries, aiming to provide insights into improving trade balance and export performance. The study employs three econometric approaches—Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Pseudo Poisson Maximum Likelihood (PPML) to ensure the robustness and reliability of the findings. The results reveal that Pakistan’s seafood exports are significantly influenced by FTAs, particularly with Afghanistan, Sri Lanka, and Malaysia, while CPFTA-I shows a positive impact. However, CPFTA-II exhibit negative and insignificant relationship, indicating that external shocks, like the COVID-19 pandemic, have impacted trade dynamics. The results of this study are significantly relevant to multiple United Nations Sustainable Development Goals (SDGs). Notably, they contribute to SDG 8, which focuses on Decent Work and Economic Growth, by promoting trade-driven economic advancement. Furthermore, they support SDG 9, which pertains to Industry, Innovation, and Infrastructure, by facilitating infrastructure development through the China-Pakistan Economic Corridor (CPEC). Additionally, they align with SDG 12, which emphasizes Responsible Consumption and Production by advocating sustainable trade practices. The study recommends enhancing FTAs, improving infrastructure, and expanding trade with neighboring markets to optimize Pakistan’s seafood export potential. Policymakers should focus on strengthening trade agreements, streamlining logistics, and integrating variables like security and exchange rates to develop more resilient and sustainable trade strategies, contributing to important SDGs.

The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs.

Poly(lactic acid) (PLA) is a renewable, sustainable, and versatile eco-friendly matrix that originated from natural sources. It has reasonable mechanical attributes; however, it expresses limited functional properties. This study is meant to address the functional limitations of the PLA matrix by incorporating cupric oxide (CuO) nanoparticles (NPs) through glutaraldehyde cross-linking using a solution-casting approach. Incorporating CuO NPs (1–3