Search Results (1,586)

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

Biomass combustion, as a key technology for achieving a low-carbon transformation of the energy system, faces multiple challenges in its efficient and clean utilization, including the high heterogeneity of fuels, the complex multi-scale coupling of the combustion process, and the attainment of ultra-low emissions. Traditional research methods have significant disconnections between microscopic mechanism understanding, macroscopic performance prediction of reactors, and end-of-pipe pollution control, which restricts the improvement of system performance. This review presents recent advances in advanced numerical simulation, pollutant control strategies, and bioenergy with carbon capture and storage (BECCS) pathways targeting ultra-low emissions in biomass combustion. This work synthesizes progress across three interconnected domains. First, methodologies are examined for integrating detailed chemical kinetics, particle-scale models, and reactor-scale simulations to develop high-fidelity predictive tools. Second, low-nitrogen combustion and synergistic pollutant control strategies for primary furnace types (e.g., grate, fluidized bed) are evaluated, alongside process optimization from fuel pretreatment to flue gas purification. Third, the potential for integrated design of biomass energy systems with carbon capture is assessed, emphasizing that system efficiency hinges on holistic “fuel-combustion-capture” chain optimization rather than isolated unit improvements. Future research directions are highlighted, including the development of physics-informed AI modeling paradigms, deeper co-design of multiple processes, and the establishment of robust life-cycle assessment frameworks. This review aims to provide a structured reference to inform both fundamental research and the practical development of next-generation clean biomass combustion technologies. Full article

Sustainable nanotechnologies derived from renewable resources are increasingly being positioned at the interface of green chemistry, advanced drug delivery, and translational pharmaceutics. Over the past decade, lignocellulosic nanomaterials, chitin/chitosan platforms, polysaccharide-based nanogels and nano-enabled hydrogels, lignin- and polyphenol-derived nanostructures, and bio-based lipid nanocarriers have been engineered through progressively eco-efficient routes, including solvent-minimized self-assembly, nanoprecipitation, spray drying, hot-melt extrusion, and microfluidic-assisted fabrication. This work provides a structured evidence map of nano-enabled drug delivery and therapeutic platforms derived from renewable biological resources. Specifically, we aim to (i) identify and classify nanoplatform classes and renewable feedstocks; (ii) summarize reported pharmaceutical critical quality attributes (CQAs) and performance and safety endpoints; and (iii) appraise how “renewability” and “green” claims are evidenced (feedstock origin vs. process sustainability) and how frequently translational readiness factors (scalability, quality control, regulatory alignment) are addressed. We critically compare renewable and conventional nanomaterial platforms across key translational dimensions, including carbon footprint, batch consistency, biodegradability, functional tunability, safety/persistence, and scale-up maturity. Finally, we delineate a practical translational pathway—from biomass sourcing and fractionation to nanoformulation, characterization/stability, and GMP scale-up—highlighting cross-cutting enablers such as lifecycle assessment, EHS/toxicology risk assessment, quality-by-design, and regulatory alignment. Collectively, the evidence supports renewable nanomaterials as viable, scalable candidates for next-generation therapeutics, provided that variability control, standardized characterization, and safety-by-design principles are embedded early in development. Full article

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

Native Staphylococcus aureus protein A exhibits strong affinity to the Fc and VH regions of human IgG1, IgG2, and IgG4, making it a valuable tool for monoclonal antibody (mAb) purification. However, its low stability under conditions such as increased alkaline concentrations during cleaning-in-place (CIP), protease exposure, thermal stress, and shear forces limits its usability for large-scale industrial applications. Recombinant Protein A (rProtein A) can be modified to improve key properties, including alkaline stability. In this study, we present targeted modifications to the C domain of native Protein A, evaluating multimeric variants for structural and functional improvements. The selected variant demonstrated extremely high stability after 60 h incubation at 0.5 M NaOH by maintaining more than >90% initial dynamic binding capacity (DBC) and up to 80% DBC after 40 h in 1.0 M NaOH. However, the most impressive result obtained was the stability of the ligand in 1.5 M NaOH, retaining 80% DBC after 22 h and 60% DBC after 40 h. To the best of our knowledge, this is the first time that such high alkaline stability is reported for a rProtein A. To assess its application in monoclonal antibody purification, the optimized rProtein A ligand was immobilized on agarose resin and tested in chromatography processes. The resulting chromatography resin functionalized with the CmZmb ligand (now commercialized by Sunresin, China under the name of rProtein A Seplife Suno) exhibited a high dynamic binding capacity of 70 mg/mL, minimal ligand leaching under operational conditions (~15 ppm), and extended lifecycle performance (88% DBC retained after 120 purification cycles with 0.5 M NaOH CIP), making it well-suited for industrial-scale applications. Full article

Roller-compacted concrete (RCC) is a promising alternative to conventional pavement systems due to its structural capacity, rapid construction, and potential for sustainable performance. Nevertheless, its global adoption remains limited by the absence of standardized design protocols, variability in construction practices, and insufficient long-term performance assessments. This study provides a comprehensive and critical review of 125 peer-reviewed publications published between 1967 and 2025, proposing a multi-dimensional integration framework that connects material fundamentals, structural design principles, construction practices, in-service monitoring strategies, and documented applications within a unified analytical perspective. Unlike earlier reviews that addressed these aspects separately, this study explicitly articulates their interdependencies and identifies a fragmented global implementation of RCC monitoring practices, with limited integration of structural, functional, and instrumentation-based assessments across life-cycle stages. The findings consolidate a structured reference framework that supports more consistent, data-driven, and sustainability-oriented use of RCC pavements in contemporary infrastructure projects. Full article

The electrification of mobility increases the need for efficient local distribution and charging infrastructures. In this context, direct current (DC) architectures may reduce conversion stages, transmission losses, and material demand compared with alternating current (AC) systems. This study aims to quantify the environmental implications of AC- and DC-based grid and charging infrastructures for large-scale rollout in Germany. For this purpose, a dynamic life-cycle assessment (DLCA) is conducted for parking garages, parcel centers, and delivery bases over the period 2023–2045, covering the production and use phases with respect to global warming potential (GWP) and material demand. The results show that DC configurations achieve lower total GWP across all application contexts investigated. For parking garages, DC reduces total GWP by 9.3% compared with AC, while for parcel logistics facilities the reduction amounts to 5.7%. Copper is identified as the dominant material driver, and DC reduces copper demand by 17.1–58.7% depending on the application. A screening-based supply-risk assessment further indicates the elevated relevance of copper due to rising demand and Germany’s import dependence. Overall, the findings provide quantitative evidence that DC-based infrastructures can reduce both environmental impacts and copper demand in large-scale charging infrastructure deployment. Full article

This study addresses a critical gap in the operationalization of sustainability frameworks at the project level by developing and validating an empirically grounded measurement instrument for assessing Environmental, Social, and Governance (ESG) compliance in manufacturing industry projects. While the United Nations Sustainable Development Goals (SDGs) articulate sustainability aspirations at the national and global level, and ESG frameworks capture organizational-level sustainability performance, no validated instrument exists for measuring ESG integration at the project level where sustainability commitments are ultimately operationalized. Drawing on the theoretical foundations of sustainable project management, stakeholder theory, and the ESG governance literature, the authors developed a 30-item survey instrument capturing six conceptual dimensions of ESG-aligned project performance. Data were collected from 2231 project sponsors and decision-makers in North American goods manufacturing firms classified under NAICS codes 31–33, which collectively encompass the entire manufacturing sector in North America. Through a sequential analytical approach employing principal component analysis (PCA) for initial item reduction, exploratory factor analysis (EFA) for dimensionality assessment, and structural equation modelling (SEM) for confirmatory validation, a parsimonious two-factor model emerged with excellent fit indices (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035). The first factor captures ESG planning activities undertaken during project initiation and planning phases, while the second factor represents ESG monitoring and controlling functions during project execution. The reduction from six theoretical dimensions to two empirical factors reflects lifecycle governance theory, where planning-phase governance and execution-phase control emerge as functionally distinct but correlated constructs. The validated instrument offers practical utility for project managers, organizational sustainability officers, and policy-makers seeking standardized benchmarks for ESG compliance at the operational project level. The validated instrument and complete survey are shared for replication and testing across different industries and countries. Full article

Recycled concrete powder (RCP) utilization as an auxiliary cementitious material absorbs construction waste and promotes low-carbon transition in construction by replacing high-carbon materials. This study optimized RCP’s particle size and amorphous SiO₂ content through physical activation, systematically investigating its effects on alkali-activated cementitious materials (AACMs). The results demonstrated that 20% activated RCP enhanced compressive strength by 9% (34.2 MPa), only 12.7% lower than that of the reference samples. Hydration analysis revealed activated RCP delayed exothermic peaks but increased total heat via active particles. Life-cycle assessment showed substituting 20% ground granulated blast-furnace slag (GGBS)/fly ash (FA) with RCP reduced carbon emissions from 169.3 to 165.9 kg CO₂-e/ton (−2.1%). Although activation slightly raised emissions to 166.6 kg CO₂-e/ton, RCP’s carbon contribution remained at 9% versus GGBS’s 83% dominance. Crucially, the activation’s 0.7 kg CO₂-e/ton increase was offset by 4.7 kg CO₂-e/ton reductions from material substitution and waste recycling benefits, confirming its net carbon-neutral potential. Full article

Artificial intelligence (AI)-enabled unmanned aerial vehicles (UAVs) are rapidly emerging as transformative technologies for sustainable healthcare logistics, particularly in remote and infrastructure-constrained regions. Despite growing implementation, the environmental, social, and governance (ESG) implications of these systems remain insufficiently synthesized in the literature. This study conducts a PRISMA-ScR-guided Systematic Review of 37 peer-reviewed studies selected from 333 records across six major scientific databases (2015–2026). The analysis reveals a sharp acceleration of research after 2021, with over 80% of publications produced between 2021 and 2024, indicating increasing global interest in AI-supported autonomous medical logistics. Evidence demonstrates that AI-enabled drones can substantially reduce delivery times; expand access to blood, vaccines, and essential medicines; and enhance emergency response capacity in rural and disaster-affected environments. From a sustainability perspective, AI-driven route optimization and autonomous navigation may reduce transport-related emissions, supporting climate-responsive healthcare supply chains. However, large-scale deployment remains constrained by regulatory fragmentation, cybersecurity risks, operational limitations, and challenges with social acceptance. This review proposes an ESG-oriented framework linking technological innovation, ethical governance, and equitable healthcare access while identifying key research gaps in lifecycle sustainability assessment, cost-effectiveness modeling, and real-world implementation aligned with the Sustainable Development Goals (SDGs). Full article

The integration of microalgal cultivation with wastewater streams offers a promising pathway to enhance resource efficiency within circular bioeconomy frameworks. However, the suitability of clarified aquaponics sedimentation effluent for producing carbohydrate-rich microalgal biomass remains insufficiently evaluated, particularly with respect to nutrient recovery and bioethanol-relevant feedstock potential. In this study, clarified aquaponics sedimentation effluent was assessed as a cultivation medium for Chlorella sp. under controlled laboratory conditions. Biomass productivity, nutrient removal performance, and carbohydrate accumulation were systematically evaluated and compared with conventional synthetic medium. Chlorella sp. cultivated in clarified aquaponic effluent achieved a maximum biomass concentration of approximately 2.05 g L⁻¹, exceeding that obtained in Bold’s Basal Medium. Carbohydrate content exceeded 40% of dry weight, indicating suitability for fermentable sugar production. Nitrate and phosphate removal efficiencies greater than 95% were achieved, with mass balance analysis confirming biological assimilation as the primary removal mechanism (~87.4%). This confirms the dual functionality of the system. The effective nutrient assimilation and confirmed the dual functionality of the system as both a biomass production and nutrient recovery process. Comparable performance under diluted and undiluted effluent conditions further indicated that freshwater dilution is not required following clarification. Light saturation was observed at 180–190 μmol m⁻² s⁻¹, providing guidance for energy-efficient operation. These findings demonstrate that clarified aquaponics effluent can serve as an effective alternative growth medium for producing carbohydrate-rich Chlorella sp. biomass while enabling nutrient recovery. The estimated bioethanol potential is theoretical, based on stoichiometric conversion assumptions, and experimental fermentation was not conducted. This work provides quantitative evidence supporting the integration of microalgae into aquaponic systems and establishes a foundation for future pilot-scale, techno-economic, and life-cycle assessments. Full article

Background: Protein A resins are indispensable for monoclonal antibody (mAb) production, yet their condition and performance are traditionally assessed using indirect or qualitative methods. In this study, the multi-attribute method (MAM), previously applied to therapeutic protein characterization, is systematically adapted for the first time as a unified liquid chromatography–mass spectrometry (LC-MS) platform for Protein A resin analysis. Method: Four Cytiva Protein A resins, MabSelect™, MabSelect SuRe™, MabSelect SuRe™ LX, and MabSelect™ PrismA, were evaluated by MAM for resin identity, Protein A ligand integrity, fouling by impurities, and cleaning performance. Results: MAM enables resin-specific peptide fingerprinting and quantitative monitoring of Protein A ligand post-translational modifications (PTMs), including deamidation, isomerization, and fragmentation induced by repeated clean-in-place (CIP) cycles. Comparative analysis of virgin and used resins revealed ligand degradation and fouling despite engineered alkaline stability, with MabSelect™ showing the greatest susceptibility. Importantly, residual monoclonal antibodies (mAbs) and host cell proteins (HCPs) were directly detected and quantified from the resin matrix, providing a molecular-level assessment of resin cleaning effectiveness not achievable with conventional approaches. Conclusions: This work establishes MAM as a novel, sensitive, and comprehensive strategy for Protein A resin lifecycle management, delivering actionable insight for resin selection, cleaning optimization, and downstream process development. Full article

Climate change poses a growing threat to global health, yet healthcare systems contribute substantially to environmental harm through energy use, waste, and greenhouse gas (GHG) emissions. Among hospital departments, Intensive Care Units (ICUs) are among the most resource- and energy-intensive, generating disproportionately high greenhouse gas (GHG) emissions. The aim of this systematic review is to synthesize the literature on the environmental footprint of ICUs and to develop evidence-based strategies for creating sustainable ‘Green ICUs’ in accordance with the PRISMA 2020 guidelines. Peer-reviewed studies published between 2012 and October 2025 were identified through searches of major biomedical databases. Eligible studies examined the impacts of climate change on human health and infectious diseases, the ecological footprint of medical imaging and personal protective equipment, and sustainability interventions relevant to adult intensive care units. The environmental footprint of ICUs ranges from 88 to 178 kg CO₂-equivalents per patient per day. High electricity consumption, especially from heating, ventilation, and air-conditioning (HVAC) systems, along with single-use medical supplies and diagnostic imaging, drives this impact. Life-cycle assessments consistently demonstrate that reusable textiles, optimized energy systems, and rationalized diagnostic practices significantly reduce emissions and waste. Educational and behavioral interventions were effective in reducing unnecessary consumable use while maintaining patient safety. A “Green ICU” model integrating energy efficiency, sustainable procurement, waste reduction, and staff education can substantially reduce environmental harm without compromising quality of care. Full article

The integration of Artificial Intelligence (AI) into the design, manufacturing, and lifecycle management of Composite Overwrapped Pressure Vessels (COPVs) is transforming the pathway toward autonomous and adaptive composite systems. This paper presents a comprehensive review and roadmap for AI-enabled COPVs development, bridging materials design, process optimisation, and predictive maintenance. The study synthesises over a decade of research on data-driven composite manufacturing, combining technology intelligence, PESTEL-SWOT environmental assessment, and cross-sectoral analysis of industrial and academic advances. A unified workflow is proposed to illustrate AI integration across the COPVs lifecycle, highlighting data feedback loops for continuous optimisation through digital twins and intelligent process control. Structural Health Monitoring (SHM) plays a central role in this ecosystem by providing real-time high-fidelity data on damage evolution and environmental interactions in COPVs. Through embedded sensing technologies such as fibre optic sensors and acoustic emission systems, SHM enhances digital twin fidelity, supports AI-based anomaly detection, and strengthens model validation in safety-critical hydrogen storage applications. Critical challenges are identified, including limited hydrogen-exposure datasets, lack of real-time adaptability, explainability in safety-critical design, and sustainability of AI-intensive workflows. These challenges highlight the need for tighter SHM-AI integration to enable reliable condition assessment and prognostics under multi-physics loading conditions. Based on these findings, the paper outlines actionable research directions to enable reliable, transparent, and sustainable AI adoption in composite manufacturing under the Industry 4.0 and hydrogen-economy paradigms. Full article

Arch–gravity dams feature both arch action and large concrete volume, yet targeted research on temperature control and crack prevention for this type remains insufficient. To address this, a Two-Parameter Decision Chart Method for predicting allowable placing temperature, an Analytical–Numerical Hybrid Estimation Method for estimating cooling durations, and the Comprehensive Cracking Risk Index (CCRI) for assessing lifecycle concrete safety are proposed, forming a complete design methodology. A case study on a proposed project using full-process simulation quantitatively evaluates the contribution of various measures in mitigating thermal stress across dam zones. Results show that without measures, the CCRI values for interior and surface concrete reach 68.9% and 38.1%, respectively. After implementing combined optimization measures targeting the control of maximum temperature, final temperature before grouting, and internal–external temperature difference throughout the entire process, both CCRI values are reduced to zero. Contribution analysis reveals distinct zonal effectiveness: for interior concrete, low-temperature placement with first-stage cooling contributes most (59.9%); for surface concrete, second- and third-stage cooling dominates (72.7%). Therefore, in practical engineering applications for temperature control and crack prevention in arch–gravity dams, a combination of measures centered on controlling the maximum temperature, optimizing the cooling process, and enhancing surface insulation should be adopted based on the characteristics of interior and surface zones, thereby improving cracking safety. Full article