Search Results (444)

Green synthesis is an eco-friendly, simple, and cost-effective process for the synthesis of metal nanoparticles from plant extracts that are rich in bioactive compounds. In the current study, the antioxidant potential and total soluble polyphenol content (TPC) of different parts of Ligusticum mutellina (L.) Crantz were evaluated using DPPH (2,2-diphenyl-1-picrylhydrazyl) and FRAP (ferric reducing antioxidant power) assays, and the results indicated that the seed extract was the most active plant part. HPLC analysis indicated the presence of phenolic compounds such as gallic acid, protocatechuic acid, and catechin, which may contribute to the reduction and stabilization of AgNPs. Silver nanoparticles (AgNPs) were synthesized from the aqueous seed extract of L. mutellina. The formation of nanoparticles was confirmed by UV–Vis spectroscopy, FT-IR analysis, powder X-ray diffraction (PXRD), and transmission electron microscopy (TEM). The UV–Vis spectrum indicated a surface plasmon resonance peak at around 411 nm, and PXRD analysis indicated an average crystallite size of around 13 nm. TEM analysis revealed predominantly spherical nanoparticles with an average size of 25.36 ± 10.76 nm. The synthesized AgNPs exhibited strong antibacterial activity against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. Overall, the results demonstrate that L. mutellina seed extract represents an effective natural source of reducing and stabilizing agents for green nanoparticle synthesis and highlight the potential of the obtained AgNPs as environmentally friendly antimicrobial materials. Full article

Aiming to improve cooling and dehumidification performance in air conditioning systems and to meet the trend toward environmentally friendly refrigerants, this study proposes a coupled system that combines a CO₂ transcritical refrigeration cycle (CTRC) with a liquid desiccant dehumidification cycle. The system takes advantage of high-grade waste heat from the exothermic side of the CTRC to drive the regenerating process of the liquid desiccant dehumidification. A cooling evaporator is adopted to cool indoor air, while another evaporator (i.e., Evaporator II) is utilized to cool the concentrated solution, improving dehumidification capacity and enabling independent control of sensible and latent heat loads. Through thermodynamic modeling and the exergy analysis model, a mathematical model of the system is developed to examine how key parameters (such discharge pressure and the CO₂ mass flow rate ratio in Evaporator II (λ)) affect performance and to analyze exergy loss features. Results show that the system’s coefficient of performance (COP) and dehumidification coefficient of performance (COP_deh) initially rise and then fall with increasing CTRC discharge pressure, achieving an optimal pressure of around 10,500 kPa (COP up to 4.32) under a specific working condition, surpassing those of standalone CTRC systems. Properly increasing λ enhances dehumidification capacity and energy efficiency, with a low specific dehumidification energy (SDE) of 0.2033 kWh/kg, indicating high economic efficiency. Most exergy losses occur in the CO₂-solution heat exchanger and dehumidifier (over 60% of total losses). The system’s maximum exergy efficiency reaches 12.4%, leaving room for further improvements. This coupled system offers an efficient, eco-friendly way for air conditioning in high-humidity environments, combining cooling and dehumidification with the potential for energy recovery. Full article

The aim of this study is to determine teachers’ views on integrating sustainable artificial intelligence use into classroom teaching processes. The study was conducted using a qualitative research approach and adopted a case study design. The study group consisted of 38 teachers who were selected using maximum diversity sampling, who currently use AI, and who participated in a 4-week structured “Sustainable AI Training Program.” To ensure methodological triangulation, data were collected through semi-structured interviews, researcher diaries, and participant diaries and analyzed using inductive thematic content analysis. According to the analysis results, some findings reveal that teachers considered filtering AI tools through a pedagogical filter centered around the question “Is it really necessary?” rather than using them directly and intensively. Furthermore, digital minimalism was adopted in classroom practices, along with the use of a single, optimized prompt instead of trial-and-error queries, the practice of archiving and reusing generated content, and a shift toward low-tech alternatives. It was determined that teachers would adopt digital minimalism in classroom practices, aiming to serve as role models for sustainable use by bringing the hidden environmental costs of technology into the learning process and fostering eco-digital citizenship awareness among students. Consequently, AI integration has evolved from a technical decision into a pedagogical redesign process encompassing ethical and ecological dimensions. Full article

Sustainable fertilizer technologies are essential to address nutrient losses, environmental pollution, and inefficiencies associated with conventional urea application. In this study, humic acid–functionalized starch (St–HA) gel coatings were developed and optimized via a Wurster fluidized-bed system to produce controlled-release urea granules, with an additional carnauba wax outer layer to further extend nutrient release duration. The coating formulation was synthesized through in situ crosslinking of tapioca starch with humic acid using N,N′-methylenebisacrylamide and potassium persulfate, yielding a cohesive film. A central composite rotatable design (CCRD) was employed to investigate the influence of atomizing air pressure, fluidizing air flow rate, fluidized-bed temperature, and spray rate on coating performance. Comprehensive characterization; including FTIR, XRD, rheological analysis, thermogravimetric studies, water retention, biodegradability, and surface abrasion, confirmed chemical crosslinking, structural stability, and mechanical robustness of the coatings. Nitrogen release analysis in both water and soil demonstrated a substantial extension of release longevity from less than 2 days (uncoated) to 18–20 days for St–HA-coated urea, and up to 28 days with the additional wax coating. Coated granules exhibited low abrasion (8–24%), high water-retention capacity, and 68% biodegradation in 60 days, ensuring environmental compatibility. The findings establish St–HA/wax hybrid coatings as a viable, eco-friendly strategy for controlled-release fertilizers, integrating renewable feedstocks with scalable industrial processing for precision nutrient management. Full article

Synthetic dyes discharged from the textile and dyeing industry present a significant environmental and health hazard due to their inherent toxicity, environmental persistence, and potential carcinogenicity. Microbial degradation has garnered significant interest as a cost-effective and eco-friendly strategy for dye wastewater treatment in recent years. The study systematically evaluated the decolorization performance, degradation pathways, and detoxification effects of three bacterial strains, including Rhodopseudomonas palustris gh32, Bacillus cereus HL7, and Bacillus safensis X64, on the dye indigo carmine (IC) and three azo dyes: reactive black 5 (RB5), direct black G (DBG), and direct blue 15 (DB15). The degradation mechanisms were elucidated through UV-Vis spectroscopy, UPLC-Orbitrap-HRMS analysis, and enzyme activity assays. Molecular docking simulations were employed to investigate the interactions between key redox enzymes (such as laccase, tyrosinase, and azoreductase) and the dye molecules. The results demonstrated that the strain-specific enzymatic systems effectively disrupted the dye structures. Significant detoxification effects were further confirmed through a series of bio toxicity assays involving Escherichia coli, Bacillus subtilis, plant seeds, and erythrocytes. The addition of Fe³⁺, sodium citrate, or yeast extract significantly enhanced both the decolorization efficiency and enzyme activity. This study provides an in-depth understanding of the bacterial dye degradation process at the mechanistic level, highlighting the potential of customized bacterial systems for eco-friendly dye wastewater treatment. It offers theoretical support for elucidating the mechanisms of bacterial dye degradation and advancing bioremediation technologies. Full article

To address the challenges of rapid water loss and insufficient long-term inhibition efficiency of conventional inhibitors in the high-temperature environments of deep goafs, a novel, environmentally friendly Deep Eutectic Inhibitor (DEI) was synthesized. This DEI utilizes citric acid (Ca) and proline (Pr) as the hydrogen bond donor and acceptor, respectively, with ascorbic acid (VC) and propyl gallate (PG) serving as antioxidants. A moisture retention evaluation model based on Fick’s law of diffusion was established to systematically investigate the liquid-domain stability of the DEI across a temperature range of 30 °C to 120 °C. The results demonstrate that the DEI exhibits superior moisture retention capabilities under high-temperature conditions, with the relative moisture retention peaking in the 80–110 °C range. Mechanistically, the formation of a robust hydrogen bond network effectively counteracts moisture evaporation driven by thermal kinetic energy. Furthermore, the DEI demonstrated significant inhibition effects on four coal samples with varying degrees of metamorphism. Tests on oxidative heat release characteristics revealed that DEI treatment delayed the initial oxidation temperature of the coal. Kinetic analysis further indicated that during the critical oxidation stage (200–300 °C), the apparent activation energy of the treated coal samples increased by 10.28–18.9 kJ/mol, effectively suppressing the spontaneous combustion process. This study contributes to the development of high-efficiency and eco-friendly fire prevention materials for coal mines. Full article

This study develops and assesses sustainable polyvinyl chloride (PVC) composites reinforced with agro-industrial waste fillers, integrating an ontology-based lifecycle assessment (LCA) framework to enhance sustainability evaluation. Agro-waste reinforcements, including rice husk ash (RHA), coir, bamboo fibre, and wood flour, were examined for their capacity to improve the mechanical and environmental performance of PVC and to advance circular economy objectives. Empirical data from UK PVC window manufacturing were integrated with Granta EduPack, Eco Design, Eco Audit, OpenLCA, and Protégé within a multi-layered semantic pipeline that links materials, processes, and environmental indicators. The agro-filler composites exhibited lower embodied energy and CO₂ emissions than glass fibre systems, with the PVC + 30% wood flour formulation achieving the highest efficiency. The ontology framework, comprising 25 classes, 7 object properties, 26 individuals, 16 data properties, and 218 axioms (generated automatically by Protégé’s metrics feature and verified with the Pellet reasoner), ensured semantic interoperability and consistent validation across datasets, enabling transparent and traceable sustainability analysis. Overall, coupling industrial data with digital LCA and ontology reasoning provides a reproducible pathway toward net zero-aligned, sustainable PVC composite manufacturing. Full article

Emerging pharmaceutical pollutants such as acetaminophen (ACE) pose health and environmental risks. Solar photocatalysis provides a sustainable and efficient treatment option. In this study, UiO-66-NH₂ metal–organic framework was immobilized on cotton fabrics to enable their application in both batch and continuous flow systems. Cotton, a biodegradable and low-cost support, was first functionalized by two strategies: hydroxylation (-OH) and carboxylation (-COOH), to promote MOF anchoring. Cotton fabric functionalization and MOF growth were confirmed by ATR and X-ray diffraction, while SEM and EDX analyses revealed that carboxylated fibers achieved higher MOF loading. Photocatalytic experiments under simulated solar irradiation demonstrated significantly higher degradation of acetaminophen when the carboxylated cotton fabric-based catalyst (F-COOH-UiO-66-NH₂) was used. Mott–Schottky analysis and band alignment revealed that, under the applied reaction conditions, hydroxyl radical generation was not favored due to the position of the valence band. Studies with scavengers identified the superoxide radical as the dominant oxidative agent responsible for the photodegradation process. In particular, the F-COOH-UiO-66-NH₂ system demonstrated its suitability for application in continuous flow systems, achieving acetaminophen conversion of up to 50% under simulated solar irradiation. This confirms its potential for scalable application in practical water treatment technologies. These results reinforce the feasibility of immobilizing MOF-based photocatalysts on functionalized textile waste, offering a dual-purpose solution that combines the removal of pharmaceutical pollutants with the valorization of waste materials. The synergistic integration of high photocatalytic efficiency, sunlight harvesting and recyclability of the materials underlines the eco-friendly and cost-effective nature of the proposed strategy. Full article

Plastics are highly persistent materials, and their environmental degradation can potentially exacerbate, rather than alleviate, pollution. The degradation of plastic materials releases toxic monomers and additives, such as bisphenol A (BPA), styrene, and dioxins, which are more reactive, harmful, and persistent than intact plastics. With half-lives ranging from weeks to decades, they bioaccumulate in food chains, disrupt ecosystems, and contribute to endocrine disruption and mutagenicity. Natural degradation pathways, like microbial metabolism and photodegradation, are slow and incomplete, often leaving toxic intermediates such as microplastics. Artificial strategies, including bioremediation and advanced oxidation processes (AOPs), show potential to address the problems of plastic pollution but face additional challenges like secondary pollution and scalability. Sustainable alternatives, including bioplastics and renewable non-plastic substitutes, present promising solutions. However, their widespread adoption is hindered by challenges such as high production costs and the need for specific conditions to facilitate degradation, necessitating further research and development. A combined approach of reducing plastic production, advancing recycling, and implementing effective remediation strategies is critical to mitigating plastic pollution’s long-term impacts on ecosystems, biodiversity, and human health. This review provides a critical analysis of the current understanding of plastic degradation processes and the toxic byproducts they generate. It highlights the paradox wherein increased degradability may exacerbate environmental hazards. Additionally, the review assesses innovative, eco-friendly alternatives designed to mitigate plastic pollution. Full article

This study presents the fabrication and optimization of eco-efficient epoxy composites reinforced with ground natural stone fillers, namely pebble, sandstone, and marble, at loadings of up to 15.6 wt.%. Low content of a bio-based modifier, modified castor oil (MCO ≈ 0.5 wt.%), is incorporated to improve filler dispersion, processing behavior, and matrix–filler interfacial compatibility. The composites are designed to enhance mechanical, thermal, and dielectric performance using low-cost, abundant, and environmentally sustainable constituents. An experimental optimization approach is employed to evaluate and optimize bulk density, Shore D hardness, thermal conductivity, dielectric constant, and tensile strength. The results demonstrate that pebble-reinforced composites exhibit the highest tensile strength (≈30 MPa) and surface hardness (≈82 Shore D), which are attributed to the angular morphology and high intrinsic rigidity of pebble particles. Marble-filled systems show superior thermal stability, with residual mass increasing from approximately 2.5 wt.% for neat epoxy to over 11 wt.% at 550 °C, owing to the thermally stable calcium carbonate phase. In contrast, sandstone-reinforced composites exhibit the lowest dielectric constant (≈3.2), indicating enhanced electrical insulation capability. Fourier–transform infrared spectroscopy (FTIR) results confirm that the epoxy network structure is preserved upon filler incorporation, while MCO promotes improved interfacial interactions through physical interactions. Thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) reveal enhanced thermal resistance, reduced microvoid formation, and improved filler–matrix adhesion at optimal filler contents of approximately 3.5 wt.%. Full article

The transition to environmentally sustainable healthcare is gaining urgency, yet public discourse shaping this shift remains underexamined. This study employs natural language processing (NLP) to analyze 15,976 English-language tweets (2006–2024) related to sustainable healthcare. Using Latent Dirichlet Allocation (LDA), eight dominant topics were identified, including eco-friendly access, net-zero implementation, climate impact, emissions, cost and waste, education, infrastructure, and green technologies. Sentiment analysis (VADER) of 9433 tweets showed 59.1% positive, 31.1% neutral, and 9.8% negative sentiment, with AI and technology topics receiving the highest positivity (73.5%) and climate-related topics the most negativity. Thematic analysis of 800 tweets revealed six cross-cutting themes, including healthcare’s environmental responsibility, co-benefits for health, urgency of climate action, and optimism in technological solutions. These findings offer a nuanced understanding of public perceptions, informing targeted strategies and communication for healthcare sustainability. The study also demonstrates the value of mixed-method NLP in examining enablers and barriers to health system transformation. Full article

To address the information-processing burden faced by consumers in green consumption markets due to complex carbon footprint labels, opaque certification standards, and vague descriptions of environmental benefits, this study proposes a generative artificial intelligence (GenAI)-based precision recommendation mechanism for green products. The mechanism aims to enhance cognitive fluency and promote low-carbon purchase decisions. An experimental system, termed Eco-GenRec, is developed by integrating large language models (LLMs), multimodal generation, and retrieval-augmented generation (RAG) techniques to enable personalized presentation of green product information. Based on inferred user cognitive styles, the system transforms product information into chart-based representations for analytical users or emotionally framed scenario narratives for intuitive users. This study is conducted on a web-based simulated shopping platform and employs a fully randomized design. A total of 1000 participants are randomly assigned to either a standardized information display group (control group) or an Eco-GenRec-generated display group (experimental group). Participants are drawn from diverse socioeconomic backgrounds and cover a wide age range. The sample exhibits substantial demographic diversity, which enhances the representativeness of the findings. Cognitive fluency and low-carbon purchase conversion rates are measured as the primary outcomes. The results show that the Eco-GenRec group achieves a significantly higher cognitive fluency score (M = 5.68, SD = 0.89) than the control group (M = 4.60, SD = 1.01). This represents an increase of 23.4% (t = 18.34, p < 0.001, effect size d = 1.17). In addition, the low-carbon purchase conversion rate in the experimental group (36.3%) is significantly higher than that in the control group (17.6%). The absolute increase of 18.7% is statistically significant (χ² = 70.28, p < 0.001, effect size Cramér’s V = 0.265). Under conditions of high cognitive-style matching, the conversion rate improvement reaches 27.2%. Mechanism analysis shows that cognitive fluency mediates the relationship between GenAI-based recommendations and purchase intention. By transforming abstract environmental parameters into intuitive and easily interpretable content, artificial intelligence reduces information-processing burden and activates positive affect and trust among consumers. Overall, this study empirically validates the effectiveness of GenAI in green product recommendation. It provides a practical pathway for addressing the “comprehension barrier” in green consumption and extends the theoretical boundaries of research on cognitive fluency and low-carbon decision-making. Full article

The global shipping industry faces escalating sustainability risks arising from geopolitical disruptions, operational instability, and tightening environmental regulations. These risks often first emerge in qualitative market narratives, limiting the effectiveness of conventional backward-looking indicators. This study proposes a sustainability-oriented natural language processing (NLP) framework for the early detection of sustainability-critical stress in global shipping. Using 155 weekly expert-curated shipping market reports published between 2022 and 2025, the framework integrates topic modeling and domain-tuned sentiment analysis to extract sustainability-relevant signals from unstructured text. Critical-to-Quality (CTQ) factors are reconceptualized as sustainability-critical performance dimensions encompassing economic sustainability (freight rate stability), operational sustainability (schedule reliability, lead time, vessel utilization, and equipment availability), and environmental sustainability (eco-efficiency). Topic–sentiment interactions are quantified using network analysis and ElasticNet-based estimation to construct composite CTQScores, which capture the intensity and persistence of sustainability stress. Empirical validation using observed market performance indicators demonstrates that the CTQScores exhibit strong directional accuracy and systematically precede market adjustments, supporting their role as early warning indicators rather than predictive forecasts. The framework is operationalized as a Sustainability Risk Radar, enabling proactive monitoring of economic, operational, and environmental risks. The findings demonstrate how NLP-based analytics can support ESG-aligned sustainability risk monitoring and resilience-oriented decision-making in global shipping systems. Full article

The manufacturing systems face growing demands due to the instability of the market, the demanding sustainability policies, and the high rate of old equipment, but traditional planning structures are mostly fixed and deterministic, leading to the inefficiency of joint optimization of operational stability and environmental sustainability in unpredictable situations. This research proposed and empirically tested an artificial-intelligence-based adaptive planning platform, which combines a physics-based Digital Twin (DT) and a Pareto-conditioned Multi-Objective Proximal Policy Optimization (MO-PPO) algorithm to be able to co-optimize reliability and sustainability indicators in real-time. The platform reinvents manufacturing planning as a Constrained Multi-Objective Markov Decision Process (CMDP), optimizing an Overall Equipment Effectiveness (OEE) and energy carbon intensity as well as material waste, and strongly adhering to operational restrictions. The study utilizes a four-layer cyber–physical architecture, which includes an edge-based data acquisition layer, a high-fidelity stochastic simulation engine that is calibrated via Bayesian inference, a graph attention network-based state-encoding layer, and a closed-loop execution loop that runs with 60 s long planning cycles. In this study, a statistically significant enhancement was shown in 10,000 stochastic simulation experiments and a 12-week industrial pilot deployment: 96.8% schedule performance, 84.7% OEE, 16.5% cut in specific energy usage (2.38 kWh/kg), 17.1% reduction in material-waste rate (6.8%), and 21.4% enhancement in carbon effectiveness, outperforming all baseline strategies (p = 0.001). The analysis showed that there was a surprising synergistic correlation between waste minimization and OEE enhancement (r = −0.73), and 34.1% of overall OEE improvement could be explained by sustainability strategies. This study provides a robust framework for adaptive, resilient, and eco-friendly manufacturing processes in line with Industry 5.0 ideologies. Full article

Polymer matrix composites derived from organic waste represent a viable solution for enhancing environmental sustainability. This study investigates the development and characterization of eco-friendly composite filaments using polylactic acid (PLA) reinforced with orange peel particulates (OPPs), evaluating their potential for fused filament fabrication (FFF). PLA/OPP composites were fabricated with varying reinforcement concentrations (2.5–20 wt%) and different particle sizes. The materials were characterized through mechanical testing, thermal analysis (DSC), and FTIR spectroscopy, while functional performance was evaluated via DPPH and ABTS antioxidant assays. The experimental results indicated that a specific low OPP concentration (2.5 wt%) maintained the tensile strength of the neat matrix while significantly improving ductility by 16.67%, thereby enhancing the processability for fused deposition modeling (FDM). Conversely, reinforcement levels exceeding 10 wt% led to a decline in mechanical properties due to fiber agglomeration and matrix saturation. Thermal analysis revealed that higher OPP content influences the crystallization kinetics, while FTIR spectra confirmed good interfacial compatibility through hydrogen bonding. Notably, the incorporation of OPP imparted significant antioxidant activity to the composites, which increased proportionally with filler content. In conclusion, this study demonstrates that low-content PLA/OPP composites successfully balance mechanical performance with functional bioactivity, providing a sustainable material suitable for active packaging and 3D printing applications. Full article