Search Results (1,363)

In contrast to packaging, non-packaging plastics remain largely untargeted by EU regulations, despite their comprising over 60% of primary plastics in the EU 27 + 3 in 2022. This results in lower separate collection and recycling rates as well as fewer studies analysing recycling-relevant characteristics in non-packaging plastic waste (NPW), which are relevant to ensure the circularity and sustainable management of all plastics. This study presents a detailed characterisation of NPW found in mixed municipal solid waste and lightweight packaging waste on polymer and product levels, using the case study of Vienna, Austria. Results show that 4100 t/yr of polymers targeted for recycling, especially polypropylene, are currently discarded and lost in mixed MSW. A large share of NPW, however, exhibits recycling-hindering traits like multi-polymer objects or black colouring. While products made of high-quality food contact material were assessed to be ideal for separate collection to ensure closed-loop recycling, consideration should be given to collecting the majority of NPW via recycling centres to prevent contamination of target polymers with currently non-targeted other polymers. Design for recycling guidelines should also be introduced for non-packaging plastics, targeting separability, colouring and small-scale products. By doing so, a more sustainable management of NPW can be achieved. Full article

Engineering education in developing countries faces critical challenges that hinder progress toward achieving the United Nations Sustainable Development Goals (SDGs). In the Democratic Republic of Congo (DRC), students entering engineering programs often exhibit significant apprehension toward foundational sciences, creating barriers to developing the technical competencies required for sustainable development. This paper introduces the AI-Integrated Practical Learning in Engineering (AIPLE) Framework, an innovative pedagogical model that synergizes Problem-Based Learning (PBL), hands-on experimentation, and strategic Artificial Intelligence (AI) integration to transform engineering education for sustainability. The AIPLE framework employs a five-stage cyclical process designed to address student apprehension while fostering sustainable engineering mindsets essential for achieving SDGs 4 (Quality Education), 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), and 11 (Sustainable Cities and Communities). This study, grounded in qualitative surveys of engineering instructors at Université Loyola du Congo (ULC), demonstrates how the framework addresses pedagogical limitations while building technical competency and sustainability consciousness. The research reveals that traditional didactic methods inadequately prepare students for complex sustainability challenges, while the AIPLE framework’s integration of AI-assisted learning, practical problem-solving, and sustainability-focused projects offers a scalable solution for engineering education transformation in resource-constrained environments. Our findings indicate strong instructor support for PBL methodologies and cautious optimism regarding AI integration, with emphasis on addressing infrastructure and ethical considerations. The AIPLE framework contributes to sustainable development by preparing engineers who are technically competent and committed to creating environmentally responsible, socially inclusive, and economically viable solutions for developing countries. Full article

Biomass-derived hard carbon (BHC) has attracted considerable attention as a sustainable and cost-effective anode material for sodium-ion batteries (SIBs), owing to its natural abundance, environmental friendliness, and promising electrochemical performance. This review provides a detailed overview of recent progress in the synthesis, structural design, and performance optimization of BHC materials. It encompasses key fabrication routes, such as high-temperature pyrolysis, hydrothermal pretreatment, chemical and physical activation, heteroatom doping, and templating techniques, that have been employed to control pore architecture, defect density, and interlayer spacing. Among these strategies, activation-assisted pyrolysis and heteroatom doping have shown the most significant improvements in sodium (Na) storage capacity and long-term cycling stability. The review further explores the correlations between microstructure and electrochemical behavior, outlines the main challenges limiting large-scale application, and proposes future research directions toward scalable production and integration of BHC anodes in practical SIB systems. Overall, these advancements highlight the strong potential of BHC as a next-generation anode for grid-level and renewable energy storage technologies. Full article

Direct thermal decomposition of rare-earth chlorides into rare-earth oxides (REOs) in a single step presents a short-process, wastewater-free, and environmentally friendly alternative to the conventional precipitation–calcination method, which produces large amounts of saline wastewater. While earlier reviews have primarily focused on summarizing reaction conditions and thermodynamic parameters, they have seldom discussed the critical variations in pyrolysis behavior across different rare-earth elements. This review highlights a novel classification of rare-earth chlorides into fixed-valence and variable-valence groups, revealing how their respective oxidation states govern thermodynamic stability, reaction pathways, and chlorine release behavior. Furthermore, a systematic comparison is provided on the effects of additives, temperature, and gas partial pressure on product purity, particle size, and microstructure, with particular attention to the mechanisms underlying oxychloride intermediate formation. Beyond fundamental reaction principles, this work uniquely evaluates the design and performance of existing pyrolysis reactors, outlining both opportunities and challenges in scaling up direct rare-earth chloride (RECl_x) pyrolysis for industrial REO production. By integrating mechanistic insights with reactor engineering considerations, this review offers advancements over previous descriptive summaries and proposes a strategic pathway toward sustainable rare-earth processing. Full article

Steel structures that support machines and industrial process installations should ideally be flexible, adaptable, and easily reconfigurable. However, in current practice, new profiles are frequently used and discarded whenever layout modifications are required, leading to considerable material waste, increased costs, and environmental burdens. Such practices conflict with the principles of the circular economy, in which reusability is preferable to recycling. This paper presents a life cycle sustainability assessment (life cycle cost, LCC, and life cycle assessment, LCA) applied to six structural typologies: (a) welded IPE profiles, (b) bolted IPE profiles, (c) welded tubular profiles, (d) bolted tubular profiles, (e) clamped IPE profiles with demountable joints, and (f) flanged tubular profiles with demountable joints. The assessment integrates structural calculations with an updatable database of costs, operation times, and service lives, providing a systematic framework for evaluating both economic and environmental performance in medium-load industrial structures (0.5–9.8 kN/m²). Application to nine representative case studies demonstrated that demountable clamped and flanged joints become economically competitive after three life cycles, and after only two life cycles under high-load conditions (9.8 kN/m²). The findings indicate relative cost savings of up to 75% in optimized configurations and carbon-footprint reductions of approximately 50% after three cycles. These results provide quantitative evidence of the long-term advantages of demountable and reconfigurable steel structures. Their capacity for repeated reuse without loss of performance supports sustainable design strategies, reduces environmental impacts, and advances circular economy principles, making them an attractive option for modern industrial facilities subject to frequent modifications. Full article

The growth of microorganisms and lower plants on building walls may respond the central principle of the biophilic design: sustained engagement with nature. As such, bioreceptive concrete has great potential to increase the biodiversity in our cities. In addition, by actively participating in the carbon and nitrogen cycles, biologically active, bioreceptive concrete has the potential to reduce the building’s environmental impact considerably. In the present study, we analyze the biological growth on concrete and critically review the current research approaches in the bioreceptivity evaluation. The uncontrolled and unaesthetic growth of fungal colonies, poor long-term survivability of the laboratory-developed biofilms, and a lack of field applications were identified among the major factors that hinder the practical application of bioreceptive concrete in the building envelope. Our ongoing field tests have shown that concrete’s controlled and aesthetically pleasant greening may be achieved in several years. We argue that such nature-integrated solutions would emphasize the beauty of the aging buildings while offering clear, practical benefits. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

China and Italy, both ancient civilizations, have numerous traditional villages that bear witness to history and support the transmission of cultural heritage. However, these villages face challenges such as homogenized development, population outflow, and disruptions in cultural continuity. While both Chinese and Italian traditional villages have received considerable scholarly attention, their comparative study remains relatively limited, leaving the transferability of respective solutions across different legal, heritage and planning contexts to be fully explored. This study aims to adapt and transfer Italy’s contiguous protection, integrated operation, national park designation, and community partnership policies to China in order to establish a comprehensive mechanism for preservation and revitalization of traditional villages. A cross-case study of Cinque Terre (Italy) and Jiande (China), incorporating on-site mapping, governance analysis, and interviews, reveals that Italy’s integrated community-based approach markedly outperforms China’s fragmented state-led model in sustaining population, culture and tourism quality. These findings provide a globally replicable paradigm for traditional village preservation. Full article

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems. Full article

Driven by the growing demand for sustainable resource utilization, the recovery of valuable constituents from spent lithium-ion batteries (LIBs) has attracted considerable attention, whereas conventional recycling processes remain energy-intensive, inefficient, and environmentally detrimental. Herein, an efficient and environmentally benign separation strategy integrating dielectrophoresis (DEP) and magnetophoresis (MAP) is proposed for isolating the primary components of “black mass” from spent LIBs, i.e., lithium iron phosphate (LFP) and graphite microparticles. A coupled electric–magnetic–fluid dynamic model is established to predict particle motion behavior, and a custom-designed microparticle separator is developed for continuous LFP–graphite separation. Numerical simulations are performed to analyze microparticle trajectories under mutual effects of DEP and MAP and to evaluate the feasibility of binary separation. Structural optimization revealed that the optimal separator configuration comprised an electrode spacing of 2 mm and a ferromagnetic body length of 5 mm with 3 mm spacing. Additionally, a numerical study also found that an auxiliary flow velocity ratio of 3 resulted in the best particle focusing effect. Furthermore, the effects of key operational parameters, including electric and magnetic field strengths and flow velocity, on particle migration were systematically investigated. The findings revealed that these factors significantly enhanced the lateral migration disparity between LFP and graphite within the separation channel, thereby enabling complete separation of LFP particles with high purity and recovery under optimized conditions. Overall, this study provides a theoretical foundation for the development of high-performance and environmentally sustainable LIBs recovery technologies. Full article

Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater environment. Of the three main practicable UWC technologies (acoustic, optical, and radiofrequency), acoustic methods are best for far-reaching links, while optical is best for high-bandwidth communication. Recently, utilizing reconfigurable intelligent surfaces (RISs) has become a hot topic in terrestrial applications, underscoring significant benefits for extending coverage, providing connectivity to blind spots, wireless power transmission, and more. However, the potential for further research works in underwater RIS is vast. Here, for the first time, we conduct an extensive survey of state-of-the-art of RIS and metasurfaces with a focus on underwater applications. Within a holistic perspective, this survey systematically evaluates acoustic, optical, and hybrid RIS, showing that environment-aware channel switching and joint communication architectures could deliver holistic gains over single-domain RIS in the distance–bandwidth trade-off, congestion mitigation, security, and energy efficiency. Additional focus is placed on the current challenges from research and realization perspectives. We discuss recent advances and suggest design considerations for coupling hybrid RIS with optical energy and piezoelectric acoustic energy harvesting, which along with distributed relaying, could realize self-sustainable underwater networks that are highly reliable, long-range, and high throughput. The most impactful future directions seem to be in applying RIS for enhancing underwater links in inhomogeneous environments and overcoming time-varying effects, realizing RIS hardware suitable for the underwater conditions, and achieving simultaneous transmission and reflection (STAR-RIS), and, particularly, in optical links—integrating the latest developments in metasurfaces. Full article

School buildings in hot-humid climates encounter considerable difficulties in balancing energy use and thermal comfort due to this environment, necessitating optimized design strategies to reduce energy consumption while enhancing occupant comfort. This study presents sustainable design strategies for educational structures in hot-humid regions, aiming to optimize energy efficiency and thermal comfort for environmental preservation and occupant welfare. The present work introduces a multi-objective optimization framework for window design in school buildings situated in hot-humid climates, targeting a balance between Energy Use Intensity (EUI) and Thermal Comfort Time Ratio (TCTR). Exploring multi-objective optimization through NSGA-II genetic algorithms, the study conducts Sobol sensitivity analysis for parameter assessment and applies Gaussian Process Regression (GPR) for effective model validation, identifying optimal window configurations that reduce energy consumption while enhancing thermal comfort. It finds that the Window-to-Wall Ratio (WWR) and Solar Heat Gain Coefficient (SHGC) are the most significant factors, with WWR and SHGC accounting for 28.1% and 23.7% of the variance in EUI and TCTR, respectively. The results reveal a non-linear trade-off between the objectives, with the Balanced Solution offering a practical compromise: a 6.7% decrease in energy use and a 14.3% enhancement in thermal comfort. The study examined various ranges of window parameters, including WWR (0.1–0.50), SC (0.20–0.80), K (1.0–2.5 W·m⁻²·K⁻¹), SHGC (0.1–0.4), Shading width (0.3–2.0 m), and Shading angle (0°–90°). The recommended compromise, known as the Balanced Solution, suggests optimal values as follows: WWR = 0.40, SC = 0.30, SHGC = 0.40, K = 1.2 W·m⁻²·K⁻¹, Shading width = 1.22 m, and Shading angle = 28°. The GPR model exhibited high predictive precision, with R² values of 0.91 for EUI and 0.95 for TCTR, underscoring the framework’s effectiveness. This research offers actionable insights for designing energy-efficient and comfortable school buildings in hot-humid climates, enriching sustainable architectural design knowledge. Full article

Air pollution risk significantly impacts social and economic systems. Given the critical role of the pension system in socioeconomic stability, it is crucial to explore the impact of air pollution on pension contributions. Utilizing panel data from eight Chinese provinces between 2014 and 2024, this study quantifies the impact of Particulate Matter (PM_2.5) on pension contributions and explores its nonlinear and lagged effects through a fuzzy regression discontinuity design (FRDD) coupled with double machine learning (DML) techniques. Through the application of the FRDD, we found that pension contributions are significantly reduced when the PM_2.5 concentration exceeds the standard annual threshold of 35 $\mu g/m^3$, and the effects differ between the Urban Employees Basic Pension Insurance (UEBPI) and the Urban and Rural Residents’ Pension Scheme (URRPS). Further, the DML approach validated these findings and suggested that a complex hysteresis response mechanism exists in relation to air pollution. Additionally, it indicated that when PM_2.5 concentrations do not exceed the threshold, this similarly has a negative effect on pension contributions. These findings emphasize the need for policymakers and pension fund managers to integrate environmental considerations into pension sustainability strategies to increase resilience to ongoing environmental risks. Full article

This study presents a novel multi-objective optimization (MOO) model for the design of an off-grid hybrid renewable energy system (HRES) to support sustainable agriculture and rural development in Sub-Saharan Africa (SSA). Based upon a case study selected in Linia (Chad), three system architectures are compared under different levels of the reliability requirements (LPSP = 1%, 5%, and 10%). A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to optimize the Levelized Cost of Energy (LCOE), CO₂ emissions mitigation, and social impact, referring to the Human Development Index (HDI) enhancement and the job creation (JC) opportunity, using the MATLAB R2024b environment. The calculation results show that among the three configuration schemes, the PV–Wind–Battery configuration obtains the optimal techno–economic–environmental coordination, with the lowest LCOE (0.0948 $/kWh) and the largest CO₂ emission reduction (9.58 × 10⁸ kg), and the Wind–Battery system gets the most social benefit. The method developed provides users with a decision-support method for renewable energy systems (RES) integration into rural agricultural settings, taking into consideration financial cost, environmental sustainability, and community development. This information is important for policymakers and practitioners advocating for decentralized, socially inclusive clean energy access initiatives in underserved regions. Full article

Understanding the interactions between carbon quantum dots (CDs) and promising food preservatives (FPs), like pyrogallol (PG), benzoic acid (BA), and gallic acid (GA), is highly relevant. This knowledge is crucial for designing CD-based sensors capable of determining the safe levels of these molecules in food and beverages. Additionally, such sensors could be exploited in the development of sustainable, intelligent packaging that controls food shelf life. Based on those considerations, in this study, we post-functionalized blue-emitting CDs, prepared according to a synthetic approach previously developed, with the FP molecules PG, BA, and GA to obtain CD-(FP) systems. UV-vis absorption and FTIR spectroscopy confirmed the presence of the FP molecules on the CD surface. The appearance of a new vibrational band at 1196 cm⁻¹ in the FTIR spectra of all CD-(FP) systems suggested that the three FP molecules interact with the CD surface via electronic interactions between the aromatic and delocalized electron systems. Further electrochemical analyses of the CD-(PG) and CD-(GA) systems show that the interactions between PG and GA benzene rings and CDs prevent their oxidation to the corresponding quinone forms. Full article