Search Results (366)

Wax deposition, driven by the crystallization of long-chain n-alkanes, poses severe challenges across industries such as petroleum, oil and natural gas, food processing, and chemical manufacturing. This phenomenon compromises flow efficiency, increases energy demands, and necessitates costly maintenance interventions. Wax inhibitors, designed to mitigate these issues, operate by altering wax crystallization, aggregation, and adhesion over the pipelines. Classic wax inhibitors, comprising synthetic polymers and natural compounds, have been widely utilized due to their established efficiency and scalability. However, synthetic inhibitors face environmental concerns, while natural inhibitors exhibit reduced performance under extreme conditions. The advent of nano-based wax inhibitors has revolutionized wax management strategies. These advanced materials, including nanoparticles, nanoemulsions, and nanocomposites, leverage their high surface area and tunable interfacial properties to enhance efficiency, particularly in harsh environments. While offering superior performance, nano-based inhibitors are constrained by high production costs, scalability challenges, and potential environmental risks. In parallel, the development of “green” wax inhibitors derived from renewable resources such as vegetable oils addresses sustainability demands. These eco-friendly formulations introduce functionalities that reinforce inhibitory interactions with wax crystals, enabling effective deposition control while reducing reliance on synthetic components. This review provides a comprehensive analysis of the mechanisms, applications, and comparative performance of classic and nano-based wax inhibitors. It highlights the growing integration of sustainable and hybrid approaches that combine the reliability of classic inhibitors with the advanced capabilities of nano-based systems. Future directions emphasize the need for cost-effective, eco-friendly solutions through innovations in material science, computational modeling, and biotechnology. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals. Full article

This study employed multiple machine learning and hyperparameter optimization techniques to analyze and predict the mechanical properties of self-drilling screw connections in thin-walled steel–ply–bamboo shear walls, leveraging the renewable and eco-friendly nature of bamboo to enhance structural sustainability and reduce environmental impact. The dataset, which included 249 sets of measurement data, was derived from 51 disparate connection specimens fabricated with engineered bamboo—a renewable and low-carbon construction material. Utilizing factor analysis, a ranking table recording the comprehensive score of each connection specimen was established to select the optimal connection type. Eight machine learning models were employed to analyze and predict the mechanical performance of these connection specimens. Through comparison, the most efficient model was selected, and five hyperparameter optimization algorithms were implemented to further enhance its prediction accuracy. The analysis results revealed that the Random Forest (RF) model demonstrated superior classification performance, prediction accuracy, and generalization ability, achieving approximately 61% accuracy on the test set (the highest among all models). In hyperparameter optimization, the RF model processed through Bayesian Optimization (BO) further improved its predictive accuracy to about 67%, outperforming both its non-optimized version and models optimized using the other algorithms. Considering the mechanical performance of connections within TWS composite structures, applying the BO algorithm to the RF model significantly improved the predictive accuracy. This approach enables the identification of the most suitable specimen type based on newly provided mechanical performance parameter sets, providing a data-driven pathway for sustainable bamboo–steel composite structure design. Full article

This study focuses on a profound paradox in eco-hotel evaluations: why do consumer ratings for location, a static asset, exhibit dynamic fluctuations? To solve this puzzle, we construct a two-stage signal-processing theoretical framework that integrates Signaling Theory and the Elaboration Likelihood Model (ELM). This framework posits that the dynamic trajectory of a hotel’s green attributes (encompassing eco-facilities, sustainable practices, and ecological experiences) constitutes a high-fidelity market signal about its underlying quality. We utilized natural language processing techniques (Word2Vec and LSA) to conduct a longitudinal analysis of over 60,000 real consumer reviews from Booking.com between 2020 and 2023. This study confirms that continuous improvements in green attributes result in significant positive spillovers to location scores, while any degradation triggers strong negative spillovers. More critically, consumer environmental involvement (CEI) acts as an amplifier in this process, with high-involvement consumers reacting more intensely to both types of signals. The research further uncovers complex non-linear threshold characteristics in the spillover effect, subverting traditional linear management thinking. These findings not only provide a dynamic and psychologically deep theoretical explanation for sustainable consumption behavior but also offer forward-thinking practical implications for hoteliers on how to strategically manage dynamic signals to maximize brand value. Full article

The Three Gorges Reservoir Area in China presents a critical conflict between industrial development and ecological conservation. It functions as a key hub for water management, energy production, and shipping, while also serving as a vital zone for ecological and environmental protection. Focusing on Zigui County, this study developed a 16-indicator evaluation system integrating geological, ecological, and socioeconomic factors. It utilized the Analytic Hierarchy Process (AHP), coefficient of variation (CV), and the Real-Coded Accelerating Genetic Algorithm-Projection Pursuit (RAGA-PP) model for evaluation, the latter of which optimizes the projection direction and utilizes PP to transform high-dimensional data into a low-dimensional space, thereby obtaining the values of the projection indices. The findings indicate the following: (1) The RAGA-PP model outperforms conventional AHP-CV methods in assessing Zigui County’s eco-geological environment, showing superior accuracy (higher Moran’s I) and spatial consistency. (2) Hotspot analysis confirms these results, revealing distinct spatial patterns. (3) From 2000 to 2020, “bad” quality areas decreased from 17.31% to 12.33%, while “moderate” or “better” zones expanded. (4) This improvement reflects favorable natural conditions and reduced human impacts. These trends underscore the effectiveness of China’s ecological civilization policies, which have prioritized sustainable development through targeted environmental governance, afforestation initiatives, and stringent regulations on industrial activities. Full article

This study examines the environmental challenges posed by azo-dye pollutants and aluminum industrial waste. Aluminum oxide nanoparticles (P/Al₂O₃-NPs) were produced using a green method that utilized pharmaceutical packaging waste as an aluminum source and marine algae extract (Padina pavonica) as reducing and stabilizing agents and that was characterized by XRD, EDX, SEM, TEM, and zeta potential. Batch biosorption studies were performed to assess the effectiveness of P/Al₂O₃-NPs in removing CR dye from aqueous solutions. The results demonstrate that the particle sizes range from 58.63 to 86.70 nm and morphologies vary from spherical to elliptical. FTIR analysis revealed Al–O lattice vibrations at 988 and 570 cm⁻¹. The nanoparticles displayed a negative surface charge (−13 mV) and a pH_zpc of 4.8. Adsorption experiments optimized parameters for CR dye removal, achieving 97.81% efficiency under native pH (6.95), with a dye concentration of 30 mg/L, an adsorbent dosage of 0.1 g/L, and a contact time of 30 min. Thermodynamic studies confirmed that the process is exothermic and spontaneous. Kinetic data fit well with the pseudo-second-order model, while equilibrium data aligned with the Langmuir isotherm. The adsorption mechanism involved van der Waals forces, hydrogen bonding, and π–π interactions, as supported by the influence of pH, isotherm data, and FTIR spectra. Overall, the study demonstrates the potential of eco-friendly P/Al₂O₃-NPs to efficiently remove CR dye from aqueous solutions. Full article

In response to the rising demand for sustainable engineering materials and waste valorisation strategies, this study investigates the multi-objective optimisation of eco-friendly hybrid composites reinforced with natural fibres and industrial waste. Sixteen composite specimens were fabricated using compression moulding by varying sisal fibre from 0 to 45 wt.%, banana fibre from 0 to 45 wt.%, NaOH alkali treatment from 0 to 6%, and red mud filler from 1 to 4 wt.%. Mechanical properties were evaluated following ASTM standards D256 for impact strength, D790 for flexural strength, D638 for tensile strength, D5379 for shear strength, and E18 for hardness. The Taguchi method combined with grey relational analysis was employed to identify optimal processing conditions. The best mechanical performance, with an impact strength of 6.57 J, flexural strength of 72.58 MPa, and tensile strength of 65.52 MPa, was achieved with 30 to 45 wt.% sisal fibre, 15 wt.% banana fibre, 6% NaOH, and 3 to 4 wt.% red mud. ANOVA revealed that NaOH treatment had the most significant influence on mechanical properties, with high F values and p values close to 0.05. Grey relational analysis proved more effective for multi-objective optimisation, with the highest grey grade of 0.894 observed in the specimen containing 45 wt.% sisal fibre, 6% NaOH, and 2 wt.% red mud. These findings highlight the critical role of fibre treatment and hybrid reinforcement in enhancing performance. The optimised composites demonstrate strong potential for use in automotive interior panel applications, offering a sustainable alternative with balanced strength and reduced environmental impact. Full article

Front-of-pack labels (FOPLs) have been identified as a potential key tool to enable consumers to make healthier and more sustainable food choices. The simplification of complex nutritional, environmental, and processing data into clear and immediate formats is an essential function of FOPLs, which facilitates a more efficient connection between detailed product information and real-world purchasing decisions. This review critically evaluates the three main categories of FOPL—nutritional (e.g., Nutri-Score), environmental (e.g., Eco-Score) and processing-based (e.g., NOVA)—and examines emerging efforts to weave these dimensions into unified labelling frameworks. A bibliometric analysis of 1803 publications from Scopus, Web of Science, and Google Scholar was conducted, using VOS viewer to identify co-occurrence networks and thematic clusters. A narrative synthesis of label design methods, regulatory steps and consumer impact research followed this. Despite the considerable maturation of individual FOPLs, their combined application remains ad hoc. Establishing harmonized, multidimensional criteria is therefore essential to ensure consistent labelling that informs consumers and promotes public health and sustainability goals. Full article

Designing sustainable farming systems in mountainous regions is particularly challenging because of complex economic, social, and environmental factors. Production models prioritizing sustainability and environmental protection require integrated assessment methodologies that can address multiple criteria and incorporate diverse stakeholders’ perspectives while ensuring accuracy and applicability. Life cycle assessment (LCA) and multi-actor multi-criteria analysis (MAMCA) are two complementary approaches that support “eco-design” strategies aimed at identifying the most sustainable options, including on-farm transformation processes. This study presents an integrated application of LCA and MAMCA to four supply chains: rye bread, barley beer, cow cheese, and goat cheese. The results show that cereal-based systems have lower environmental impacts than livestock systems do, although beer’s required packaging significantly increases its footprint. The rye bread chain emerged as the most sustainable and widely preferred option, except under high-climatic risk scenarios. In contrast, livestock-based systems were generally less favorable because of greater impacts and risks but gained preference when production security became a priority. Both approaches underline the need for a deep understanding of production performance. Future assessments in mountain contexts should integrate logistical aspects and cooperative models to enhance the resilience and sustainability of short food supply chains. Full article

This study investigates the adsorption performance of biochar synthesized under varying pyrolysis and CO₂ activation conditions for the simultaneous removal of nitrogen monoxide (NO) and sulfur dioxide (SO₂), with an additional focus on its environmental impacts via life cycle assessment (LCA). Biochar was produced from Hinoki cypress using a two-stage process comprising initial pyrolysis followed by CO₂ activation, and its physicochemical properties were evaluated through pore structure analysis. Adsorption experiments were conducted under both single- and combined-gas conditions to assess the synergistic or competitive behaviors of NO and SO₂ adsorption. The results indicated that activation conditions significantly influenced the surface area and pore volume of biochar, leading to enhanced gas adsorption capacities. A trade-off between biochar yield and pollutant removal efficiency was observed, suggesting an optimal activation temperature balancing these two factors. Furthermore, the LCA approach, employing IPCC 2021 GWP 100 metrics, quantified the environmental impacts of biochar production under different thermal conditions. The findings revealed that although higher activation temperatures improved adsorption efficiency, they also resulted in increased energy consumption and associated greenhouse gas emissions. These outcomes demonstrate the necessity of optimizing activation parameters not only for functional performance but also for environmental sustainability. This work provides insight into designing efficient biochar-based gas treatment systems and supports their potential application as eco-friendly alternatives in industrial emission control strategies. Full article

The rapid urbanization of contemporary cities represents one of the most complex challenges of the 21st century, with profound implications for the environmental, social, and economic sustainability of territories. In this context, urban regeneration emerges as a strategic approach to territorial transformation. The complexity of urban dynamics requires the adoption of innovative paradigms and systemic approaches capable of guiding decision-making processes toward eco-sustainable and resilient solutions. This research develops advanced decision support tools for urban regeneration, using the city of Potenza (Italy) as a case study. The main objective is to identify key indicators to evaluate the effectiveness of urban regeneration interventions in advance (ex-ante). The methodology develops a composite economic-financial risk index capable of providing an accurate picture of existing conditions while adapting to the territorial specificities of the analyzed area. This index, which uses the Analytic Hierarchy Process (AHP) technique to integrate elementary economic-financial indicators in order to assess the sustainability level of urban redevelopment projects, is able to synthesize complex economic variables into a single parameter of immediate comprehension, strategically guiding investments toward a sustainable urban development model. The analysis of results highlights a peculiar territorial configuration: semi-central areas present the greatest criticalities, while there is a progressive decrease in risk both toward the central core and toward peripheral and extra-urban areas. The study represents a significant methodological contribution to future urban regeneration initiatives at the local level, promoting an integrated vision of sustainable urban development for the benefit of current and future generations. Full article

This article presents a comparative analysis of eco-innovation across the Czech Republic, Slovakia, and Poland. Eco-innovation, defined as the development and application of new products and processes that contribute to environmental sustainability, is essential for addressing global environmental challenges. The study examines several dimensions of eco-innovation, including eco-innovation outputs, eco-innovation activities, resource efficiency, and socio-economic outcomes. Through a detailed assessment of these dimensions, the research highlights the performance and progress of each country in implementing eco-innovations. The findings reveal significant differences in eco-innovation outputs and activities, with implications for resource efficiency and socio-economic benefits. Ultimately, the article ranks the three countries based on their overall eco-innovation performance, providing insights into their respective strengths and areas for improvement. This comparative analysis contributes to a deeper understanding of eco-innovation dynamics in Central Europe and offers policy recommendations to enhance environmental sustainability in the region. Full article

This study examines the effect of internal dynamic capabilities i.e., digital leadership, environmental awareness, and organizational learning, on sustainable supply chain performance as studied in the logistics sector. It builds on the Dynamic Capabilities Theory by combining notions of green innovation and sustainability and fills the growing gap in the existing literature. Despite the fact that these domains have been extensively studied independently, there has been limited research examining how internal capabilities contribute to green supply chain innovation (GSCI) that in turn results in sustainability outcomes, especially in the case of emerging markets. Seven hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of data collected from 312 logistics and supply chain professionals in Jordan. This study shows that each of the three capabilities has a major effect on GSCI and therefore sustainable performance. Linking the most influential predictor of sustainability outcome to sustainable supply chain performance, as indicated by the strongest effect (β = 0.825, p < 0.001) between GSCI and sustainable supply chain performance, and followed by significant coefficients between the sustainable information processing (β = 0.261, p < 0.01), and information capabilities (β = 0.297, p < 0.001), indicates that the theory is more suited to GSCI. In particular, digital leadership had the largest impact on the green innovation (β = 0.481, p < 0.001), indicating that the role of digitally driven leadership is to facilitate eco-innovation. In addition, this intermediate factor, GSCI, serves as a variable that mediates relationships between the capabilities and the sustainability outcomes. As the results here suggest, leveraging internal capabilities is a very tangible channel for green innovation that has important ramifications for practitioners and policymakers facing resource constraints. Full article

The formulation of eco-friendly coatings with protective properties against corrosion and/or mechanical degradation requires the selection of appropriate bio-based binders and functional additives. Although the concentration of additives remains limited, the replacement of fossil-based additives with bio-based additives may deliver an important contribution to improving the carbon footprint of a coating, in parallel with their influences on coating performance, lifetime, and processing. However, the role of bio-based additives in life-cycle analysis (LCA) is often neglected and minorly considered in current literature. Reasons for this include the complexity of the full system, together with a lack of data, methodological inconveniences, and appropriate design of realistic scenarios. Within this work, an approach of simplified LCA is followed by ab initio cradle-to-gate analysis of coating formulations focusing on the replacement of specific fossil additives (e.g., carbon black, silicates, and calcium carbonate) with bio-based additives (e.g., biochar, bio-based wax, recovered calcium carbonate, and nanocellulose). The different environmental impact parameters (human health, eco-toxicity, resource scarcity, and carbon footprint) for bio-based additives and coating formulations are calculated from eco-cost analysis (Idemat 2024 v2.2 database), indicating a 15 to 30% gain in carbon footprint for coatings with bio-based additives. In a particular case study for improving coating performance by substituting cellulosic additives into nanocellulose from different sources, the reduction in environmental impact parameters is positively associated with their high performance at low concentration. The need for intermediate processing of bio-based additives is a main parameter contributing to their environmental impact, but environmental benefits are abundantly compensated by their carbon storage credit and performance improvement. Full article