Search Results (15,436)

Waste management presents a major environmental and public health challenge, creating an urgent need for strategies that convert discarded materials into higher-value products. Waste-derived nanoparticles (WDNPs) have gained increasing attention because they integrate waste valorization with the production of functional nanomaterials for environmental, biomedical, agricultural, packaging, sensing, catalytic and energy-related applications. This review critically evaluates WDNP synthesis from five major waste streams, including agricultural residues, animal-derived waste, plastic waste, electronic waste and industrial by-products. Across these categories, precursor composition strongly influences nanoparticle size, morphology, surface chemistry, stability and functional performance, making feedstock selection and processing conditions central to reproducible production. Evidence from recent studies indicates that WDNPs have broad functional potential across environmental remediation, biomedical delivery, antimicrobial systems, sustainable packaging, agriculture, energy storage and catalysis. However, translation beyond laboratory-scale studies remains limited by feedstock variability, limited reproducibility, complex purification requirements, potential toxicity, insufficient standardization and limited pilot-scale validation. By comparing synthesis approaches, application outcomes and translational barriers across waste categories, this review provides a critical overview of the opportunities and limitations of WDNPs and identifies the key requirements for their responsible development within a circular-economy framework. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

The rapid reshoring of semiconductor manufacturing in the United States has introduced large-scale, energy-intensive industrial facilities into metropolitan regions increasingly exposed to climate-related infrastructure pressures. While existing research on industrial development often emphasizes agglomeration-driven economic spillovers, less attention has been given to how the early operational period of such facilities corresponds with surrounding commercial activity, particularly in peri-urban and greenfield suburban contexts. This study examines the spatiotemporal dynamics of localized commercial vitality surrounding semiconductor fabrication facilities in Phoenix, Arizona, and Austin, Texas. High-frequency point-of-interest (POI) mobility data are used to measure localized commercial activity, while regional electricity load records provide contextual information on metropolitan-scale demand conditions. Using a comparative Difference-in-Differences (DiD) framework combined with distance-band analysis and sectoral-temporal stratification, the study evaluates activity patterns between 2020 and 2025. The results indicate that the early operational period of the Phoenix facility is associated with a sustained relative divergence in mobility-derived commercial activity compared with the Austin benchmark trajectory. Spatial analysis identifies a clear distance-dependent gradient, with the largest relative reductions concentrated in intermediate suburban zones rather than immediately adjacent to the facility. Sectoral and temporal analyses further show larger reductions in dining and nighttime activity than in routine retail and daytime activity. Overall, the findings suggest that the early operational period of large industrial mega-projects may be associated with differentiated commercial activity trajectories across surrounding suburban environments. More broadly, the study demonstrates how high-frequency mobility data can be used to examine spatiotemporal variation in commercial vitality around major industrial developments. Full article

Flywheel energy storage systems (FESSs) offer millisecond-level response speed, making them highly suitable for providing system inertia/frequency support in emergency grid scenarios. However, the FESSs often have limited energy capacity due to their high capacity cost, which necessitates a comprehensive consideration between remaining stored energy and sustained support capability. Thus, this paper proposes a virtual synchronous generator (VSG) control strategy based on a multi-time-step model predictive control (MPC) that considering flywheel’s state of charge (SOC), which provides both emergency frequency support and autonomous flywheel energy recovery within a single integrated framework. First, a multi-time-step MPC with the objective function aiming for both fast frequency response and smooth power output is introduced to compensate the reference power generated by the VSG strategy. Second, an SOC-adaptive frequency weight function is designed and incorporated into the objective function to balance the frequency deviation and the inertia/frequency support duration. Furthermore, an SOC self-recovery strategy is developed, allowing the flywheel to autonomously adjust its SOC to the desired range when the FESS is not participating in frequency regulation. Finally, the proposed strategy is verified through comprehensive simulations on various scenarios, demonstrating that it can efficiently and rapidly meet the frequency regulation demands when the SOC is sufficient, as well as achieve the balances between the frequency regulation performance and the support continuity when the SOC is insufficient. Full article

The present study investigates the possibility of selective iron reduction from the Keregetas iron–manganese ore deposit (Kazakhstan) using hydrogen, followed by the separation of iron- and manganese-containing phases. The relevance of the research is associated with the need to develop environmentally sustainable processing technologies for low-grade iron–manganese ores under the conditions of metallurgical industry decarbonization. Experimental studies were carried out at temperatures of 800–900 °C in a high-purity hydrogen atmosphere, followed by magnetic separation and liquid-phase separation of the reduction products. The phase and chemical compositions of the samples were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS). It was established that during the reduction process, iron oxides were predominantly transformed into the metallic state with the formation of α-Fe, whereas manganese oxides were mainly reduced to MnO and Mn₃O₄. Magnetic separation demonstrated limited selectivity due to the simultaneous transfer of iron-containing and manganese-containing phases into the magnetic fraction. At the same time, liquid-phase separation of the pre-reduced material at 1650 °C ensured effective separation of metallic and slag phases, with manganese concentrated in the slag and minimal losses in the metallic product. A technological flowsheet for the processing of iron–manganese ores is proposed, including hydrogen reduction, magnetic separation, and subsequent high-temperature phase separation. The obtained results demonstrate the prospects of hydrogen metallurgy for the development of low-carbon technologies for the integrated processing of iron–manganese raw materials. Full article

Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ET_a) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R² = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ET_a variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture. Full article

In recent years, there has been growing awareness of the potential harmful effects that analytical methods can have on human health and the environment. Green analytical chemistry (GAC) integrates sustainability into chemical analysis by emphasizing a reduction in waste, energy consumption, and hazardous reagents while maintaining analytical performance. This review summarizes the most recent developments in atomic spectrometry techniques used for analyzing trace metals in various types of samples. Key advances include green metrics, sampling methods, direct analysis, and instrument miniaturization. Since direct sample analysis via spectrometric methods is rarely feasible, recent developments in sample pretreatment, which align with the 12 principles of GAC, are also discussed. Passive sampling can serve as a valuable approach for conducting analyses with reduced sample pretreatment steps and overall costs, thereby addressing these concerns. Current green assessment metrics and their application in atomic spectrometry are also reviewed. This article aims to provide researchers with detailed information to improve the determination of trace metals in accordance with GAC principles. Full article

Polyethylene terephthalate (PET) is one of the most widely used plastics for single-use applications, with annual global production exceeding 80 Mt. Enzymatic degradation of PET has emerged as a promising and sustainable alternative to conventional recycling methods, enabling the hydrolysis of PET into its constituent monomers. While amorphous PET can be efficiently degraded by polyester hydrolases identified from environmental sources, crystalline PET remains highly recalcitrant to enzymatic attack and constitutes a major bottleneck for the industrial implementation of enzymatic PET recycling. Although physicochemical pretreatments can increase PET amorphicity, these approaches often require substantial energy input, thereby compromising the overall sustainability of the process. Consequently, the development of enzymes capable of directly degrading crystalline PET has long been sought; however, currently engineered enzymes exhibit insufficient catalytic activity toward highly crystalline PET owing to multiple factors, including limited substrate surface accessibility, highly ordered polymer morphology, incompatible binding-pocket geometries, restricted chain mobility, and unfavorable conformational energetics at the polymer–enzyme interface. This review aims to evaluate the factors limiting the enzymatic degradation of crystalline PET and to assess current strategies for overcoming low degradation rates. Specifically, it examines advances in substrate modification as well as enzyme- and process-engineering approaches designed to improve the depolymerization of crystalline PET. The advantages and limitations of these strategies are critically compared and discussed, highlighting the remaining challenges and future directions toward efficient and scalable biocatalytic PET recycling. Full article

Dioecious plants often exhibit sex-specific physiological strategies that influence their response to environmental change. However, it is not well understood whether such dimorphism extends to the developmental trajectory of the photosynthetic apparatus during natural senescence. In this study, we compared the seasonal development and decline of photosystem II (PSII) function in naturally grown male and female Ginkgo biloba using non-destructive fast chlorophyll a fluorescence induction kinetics (OJIP) and JIP-test analysis. Sun-exposed, healthy leaves were sampled at approximately 15-day intervals from 18 July to 26 November 2024 [day of year, (DOY 188–332)]. The study monitored chlorophyll content and OJIP-derived parameters, and evaluated sex differences statistically (p < 0.05). Chlorophyll content began to decline after DOY 268 in both sexes, but decreased earlier and more rapidly in males. By DOY 332, male chlorophyll content fell to 1.37% of its level at DOY 268, whereas females retained 9.55%. OJIP fluorescence transient analysis revealed that ΔW_oj shifted from negative to positive values after DOY 268 in male plants, accompanied by a sustained increase in the relative variable fluorescence at the J step (V_j). This pattern indicates an earlier and more pronounced acceptor-side limitation of PSII in male plants, associated with accelerated accumulation of QA⁻ and restricted electron transfer from QA⁻ to QB and the plastoquinone (PQ) pool. In addition, male plants showed a clearer donor-side limitation, with a pronounced ΔW_ok response, suggesting reduced stability of the oxygen-evolving complex (OEC). In contrast, females maintained higher cross-section-based energy fluxes (TR₀/CS₀, ET₀/CS₀) and PSI-end acceptor reduction capacity (RE₀/CS₀), and exhibited a slower decline in integrated performance indices (PI abs, PI total, DF abs). Principal component analysis further suggested that male senescence trajectories were more tightly associated with changes in electron-transport efficiency, whereas females exhibited a more gradual adjustment in energy-flux allocation. Collectively, these results reveal pronounced sexual dimorphism in the PSII–PSI functional decline pathway during natural senescence in G. biloba and provide a physiological basis for understanding sex-specific variation in photosynthetic decline in this species, with potential relevance to broader studies of dioecious plants. Full article

Thorium (Th) is increasingly considered a promising fertile material for sustainable nuclear energy—which is not fissile itself, but convertible to fissile ²³³U—particularly as a by-product of rare earth element (REE) processing. This study develops a parametric techno-economic assessment (TEA) framework synthesizing published data from China, Russia, the USA, India, and Europe to establish the methodological foundation for evaluating thorium recovery economics from monazite ores and REE tailings under Central Asian conditions. Monazite typically contains 4–12% ThO₂, while tailings contain 0.1–3%, making secondary resources attractive for future recovery strategies. Particular attention is given to integration with uranium tailings and the application of advanced materials such as nanocomposite sorbents and carbon-based electrodes. Reported production costs of ThO₂ range from 50 to 500 USD/kg depending on process scale, feedstock quality, and co-production of REEs. The reviewed studies consistently show that coupling thorium recovery with REE processing improves economic feasibility. Modern approaches, including hybrid technologies and electrosorption systems, may reduce operational costs and improve process efficiency. Despite challenges related to capital investment, market uncertainty, and radioactive waste management, thorium continues to attract growing interest as a potential component of future nuclear fuel cycles and advanced reactor systems, including small modular reactors. To the best of the authors’ knowledge, this is the first parametric TEA framework structured around Central Asian conditions, combining literature-derived regional data, scenario-based process economics, and Monte Carlo sensitivity analysis within a single discounted cash flow structure. Full article

In the context of SDG 7 and SDG 13 of the 2030 Sustainable Development Agenda, a new performance indicator has started to gain momentum in scientific research: renewable energy productivity. Understanding the drivers and the challenges of green energy productivity could help add on to the classical focus of renewable energy research on infrastructure, technical and economic feasibility, and environmental and social impacts, by considering the performance indicators in this field more. Only very few studies have explored the influencing factors of renewable energy productivity. Thus, this research aims to reveal the impact of social, economic, energy, and environmental variables on green energy productivity. The methodological approach involves bibliometric analyses of the literature on green energy productivity (GEP) and panel data regression models involving 16 independent variables. The main findings indicate positive effects of green taxes, female participation in the workforce, and highly educated people on GEP, pointing out the importance of green taxation, education, and gender equality in sustainable development. On the other hand, negative relationships of green energy productivity with economic growth, traditional energy variables, and air pollution were found for the European Union’s member states over 2007 and 2023. The results suggest that the analyzed European countries based their economic growth on traditional resources, with less importance given to renewable resources and green technologies, as the share of renewable resources of GDP was also negatively correlated. While private financial resources increase green energy productivity, questions about research and development investments, urbanization, and diversity index are still debatable. Full article

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

The pursuit of sustained economic growth remains a fundamental objective for all nations, as it directly contributes to improving living standards and the overall quality of life for citizens. This research examines how human development, technological innovation and environmental taxation influence long-term economic performance across twenty-two European Union (EU) countries over the 1990 to 2022 period. Method of Moments Quantile Regression (MMQR) is employed for data analysis and the robustness check is achieved by employing the Pooled Mean Group (PMG) and Panel Corrected Standard Errors (PCSE) methods. Key findings reveal the importance of human development, research and development and investment by sector in raising the Gross Domestic Product (GDP) per capita. Moreover, the MMQR findings shows that environmental taxes exhibit positive relationships with GDP per capita in the lower and middle quantiles, while insignificant relationships prevail in the upper quantiles. Therefore, environmental taxes are subject to some upper limits on their influence on GDP per capita. Once the threshold is achieved, environmental taxes tend to harm production. The PCSE findings show that the relationship of environmental taxes and GDP per capita is a weak positive one, while the PMG results shows that these factors are negatively related. Renewable energy is observed to be negatively related with GDP per capita as supported by the MMQR, PMG and PCSE results. These findings offer valuable policy implications, reinforcing the importance of aligning economic strategies with the Sustainable Development Goals (SDGs) to foster inclusive and environmentally sustainable growth within the European context. Full article

The thermomechanical deformation behavior of high-entropy alloys (HEAs) is governed by complex interactions among strain, strain rate, and deformation temperature, necessitating robust constitutive models for accurate flow stress prediction and process optimization. In this study, a novel Zerilli–Armstrong (NZA) constitutive model was developed to characterize the hot deformation behavior of FeCoCrNi HEA. The proposed NZA model incorporates enhanced descriptions of strain hardening and deformation-temperature coupling to improve prediction accuracy. The predictability of the proposed NZA model was systematically evaluated and compared with that of the original Zerilli–Armstrong (ZA) and modified Zerilli–Armstrong (MZA) models using key statistical indicators, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). The findings demonstrate that the NZA model exhibits superior predictive performance, achieving an excellent correlation coefficient (R) of 0.997, a low AARE of 4.22%, and an RMSE of 5.82 MPa. These results confirm the reliability and effectiveness of the proposed constitutive framework in accurately describing the thermomechanical flow behavior of FeCoCrNi HEA over a wide range of deformation conditions. The proposed NZA model provides a robust framework for optimizing hot-forming processes and improving the manufacturing performance of HEA-based components while promoting sustainable manufacturing through reduced material consumption, enhanced energy efficiency, and support for SDGs 9 and 12. Full article

Coccocypselum lanceolatum is a tropical, perennial, creeping, herbaceous C3 plant species that is found in deeply shaded humid forests. This species has potential for medicinal and culinary uses. Knowledge about this species and other herbaceous Rubiaceae is confined to phytocoenological and morpho-anatomical studies. Here, it was hypothesized that (1) leaf gas exchange dynamics over a one-year period in C. lanceolatum are related to light conditions, phenology and environmental seasonal changes; (2) photosynthetic performance is focused on enhanced carbon gains through a high leaf net assimilation rate (A_net) relative to light availability, a low dark respiration rate (R_d) and a light compensation point (LCP); and (3) these parameters will vary over leaf age. The photosynthetic photon flux density (PPFD), characterizing the growth and development of C. lanceolatum, was reduced to 4–11% of incoming light in the open area, while the red-to-far-red light ratio (R:FR) was reduced from 1.15 to mean diurnal values of 0.45–0.81, depending on forest canopy dynamics. Leaf gas exchange parameters [A_net, stomatal conductance (g_s), leaf transpiration (E), and intrinsic water use efficiency (iWUE)] were observed over a one-year period. A_net, g_s, and E were correlated with energy factors (PPFD and air temperature) during vegetative growth, while only iWUE showed a correlation with leaf gas exchange parameters during blooming and fruiting, indicating that seasonality and phenology were additional drivers of leaf gas exchange. As a deep-shade forest species, C. lanceolatum displayed low iWUE (3–21 μmol m⁻² s⁻¹) and was adapted to maximize carbon gain and prioritize high g_s rather than water economy. The extremely low LCP (4.2 μmol m⁻² s⁻¹), low R_d (0.2 to 0.43 μmol m⁻² s⁻¹), maximum net photosynthesis (A_max, 5 μmol m⁻² s⁻¹), and apparent quantum efficiency of CO₂ assimilation (Φ of 0.04 µmol µmol⁻¹) were adaptational traits of this species for low light. Finally, the A_net, g_s, E, iWUE, gross photosynthesis under light saturation, R_d, LCP, and light saturation point values were different when comparing young and adult leaves. The ecophysiological responses over a one-year period shown here could assist in the success of C. lanceolatum as a sustainable soil-cover plant in shaded areas. Full article