Search Results (54,392)

The growing global consumption of concrete is driving up the demand for cement, which has a negative environmental impact due to intensive CO₂ emissions. This impact can be reduced by replacing cement with reactive mineral industrial waste, simultaneously addressing the issue of waste accumulation in landfills. However, to ensure the effective use of such materials, it is essential to comprehensively investigate their influence on concrete durability. This study analyzes glass processing waste (GPW) generated during glass grinding. The waste is removed using water, resulting in the formation of glass processing waste. In the experiment, CEM I 42.5 R cement, GPW, sand, crushed dolomite stone, concrete sludge (CS), chemical admixtures, and water were used. In the tests, cement was replaced with glass processing waste in amounts ranging from 5% to 30%, analyzing a total of seven different compositions. The properties of the sustainable concrete mixture were evaluated, and the mechanical–physical properties of the hardened concrete were determined. Resistance to alkali–silica reaction was tested according to the RILEM AAR-4 methodology, while the environmental impact of glass processing waste was assessed using Life Cycle Assessment (LCA). The results showed that glass processing waste increases the concrete’s resistance to alkali corrosion: as the amount of waste increased, a smaller change in the linear dimensions of the specimens was recorded, and the lowest mass loss was found in the composition where 20% of the cement was replaced by glass processing waste. The environmental impact assessment confirmed a direct correlation—as the amount of glass waste increases, CO₂ emissions decrease proportionally. To produce sustainable concrete, it is recommended to use up to 20% glass processing waste: this allows for the maximum reduction in environmental impact while maintaining mechanical properties and high resistance to alkali–silica reaction. Full article

The exposure of microplastics (MPs) to ultraviolet (UV) light in the environment can affect their flotation behavior and removal efficiency. This study investigated the effects of UVC irradiation on the physical and surface characteristics of polypropylene (PP), polyethylene (PE), acrylonitrile butadiene styrene (ABS), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC), and evaluated their removal using agglomeration–micro-flotation. MPs were irradiated with UVC for 7 days, and they were characterized using particle size distribution analysis, CIE L*a*b* color analysis, and contact angle measurements. Flotation experiments were conducted using kerosene as a hydrophobic bridging liquid. The results showed that UVC irradiation induced polymer-dependent changes, including fragmentation, apparent shape-related changes, and redistribution behavior, resulting in changes in particle size distribution. Surface discoloration and reduced contact angle were also observed after UV exposure, suggesting photooxidative surface modification and increased surface hydrophilicity. These surface modifications reduced flotation performance at low kerosene dosages, particularly for PET and PVC. However, increasing kerosene dosage improved removal efficiency by enhancing agglomeration and particle–bubble attachment. The results indicated that agglomeration–micro-flotation is a promising approach for removing UV-aged MPs and provided insights into the influence of UV-induced surface modifications on flotation behavior. Full article

Background/Objectives: Poly(lactic-co-glycolic acid) (PLGA) microparticles are widely used for sustained drug delivery, yet the release behavior reported in the literature remains difficult to predict across studies. It was hypothesized that this limitation reflects insufficient information content in commonly reported formulation variables rather than model inadequacy. Methods: A curated dataset of 321 PLGA microparticle formulations from 113 publications comprising 89 drugs and 4913 release observations was analyzed. Early time release was parameterized using Korsmeyer–Peppas descriptors (n, K), and burst release was quantified as the 24 h cumulative release. Machine learning models were evaluated using formulation-grouped cross-validation, applicability-domain analysis, and leave-one-study-out validation to assess cross-laboratory transportability. Results: Under formulation-grouped validation, predictability was limited (stacked ensemble: $R^2=0.156$ for n, $R^2=0.169$ for K, burst $R^2=0.100$). Leave-one-study-out validation yielded negative pooled $R^2$ values for all targets ($-0.061$, $-0.040$, and $-0.180$, respectively), indicating failure to generalize across laboratories. Applicability-domain filtering did not materially improve performance, supporting the interpretation that prediction is limited by missing or inconsistently reported variables rather than covariate extrapolation alone. Conclusions: These results reveal an information-limited regime in PLGA release prediction in which the literature covariates enable only weak formulation-level prediction under grouped validation and cannot support transferable models. Minimum reporting priorities are therefore proposed, including standardized characterization of polymer molecular weight, end-group chemistry, quantitative emulsification and solvent-removal parameters, and microstructural or porosity measurements, to enable reproducible formulation screening. Full article

Micro-dwarf tomato cultivars are increasingly considered for urban and controlled-environment agriculture due to their compact architecture and suitability for high-density planting. In this study, we evaluated the effects of different leaf removal intensities on leaf-level physiological performance, fruit yield, and fruit quality in three micro-dwarf tomato cultivars (Mohamed, Hahms Gelbe Topftomate, and Red Robin) grown under contrasting seasonal light conditions. Plants were subjected to low (15%), moderate (30%), or severe (90%) leaf removal, and leaf-level gas exchange was measured across canopy layers, along with yield and fruit quality assessments. Severe leaf removal (90%) increased carbon assimilation, transpiration, and stomatal conductance in middle and lower canopy leaves by up to approximately twofold compared with control plants, indicating improved light availability at the leaf level. However, these physiological enhancements did not consistently translate into higher yield, reflecting reduced whole-plant source capacity under excessive leaf removal. Low to moderate leaf removal (15–30%) generally increased or maintained yield and fruit number, whereas severe leaf removal reduced yield in Hahms Gelbe and Red Robin, particularly under low seasonal radiation. Fruit quality was largely unaffected by leaf removal, except for total soluble solids, which declined by approximately 12% under severe leaf removal across cultivars, consistent with sugar dilution under source limitation. Overall, these results demonstrate that optimal leaf removal in micro-dwarf tomatoes requires balancing improved canopy light distribution with maintenance of sufficient leaf area for carbon assimilation. For the tested compact canopies, LR15–30% represented a generally safe, practical range, whereas LR90% posed a substantial risk of source limitation, particularly at lower radiation; the exact threshold, however, remained cultivar- and light-dependent. Full article

Phishing detection models often report strong benchmark performance, yet their reliability under realistic deployment conditions remains uncertain. This study examines this problem by investigating three failure modes of cross-dataset phishing email detection: corpus generalization failure, asymmetric prevalence-shift failure, and artifact-driven spurious learning. Using six public email corpora, CEAS_08, Enron, Ling, Nazario, Nigerian Fraud, and SpamAssassin, the study evaluates Term Frequency (TF) and Inverse Document Frequency (IDF)-based Logistic Regression and Linear Support Vector Classifier (SVC) models across pooled baseline testing, single-corpus cross-dataset transfer, leave-one-corpus-out pooled training, prevalence-shift simulation, training prevalence manipulation, dataset-identification analysis, top-feature inspection, artifact-removal ablation, and targeted feature-sensitivity masking. The findings show that single-corpus models are unstable under cross-dataset transfer, with F1-scores varying substantially across source–target combinations. In contrast, leave-one-corpus-out pooled training improves robustness, with Logistic Regression achieving sustained F1-scores between 0.8201 and 0.8994, and Linear SVC achieving F1-scores between 0.7607 and 0.8910 across unseen corpora. Prevalence-shift experiments reveal that failure is asymmetric and threshold-dependent. High-prevalence-trained models maintain high recall under fixed thresholds but suffer sharp recall degradation when operational alert-budget constraints are imposed. Conversely, low-prevalence-trained models become overly conservative in high-threat environments, producing high precision but substantially lower recall and poorer calibration. Artifact analyses further show that source corpus identity is highly learnable, with dataset-identification accuracy reaching 0.9722 for Logistic Regression and 0.9806 for Linear SVC. Top-feature and masking analyses indicate that models rely partly on corpus markers, date tokens, URL/domain terms, headers, and other artifact-like features rather than only general phishing indicators. The study contributes a deployment-aware and adversary-aware evaluation framework for phishing detection. It shows that benchmark accuracy alone is insufficient for assessing real-world robustness and that reliable phishing detection requires cross-corpus validation, prevalence-aware thresholding, and systematic testing for artifact-driven spurious learning. Full article

Multifunctional membranes capable of simultaneously separating oil–water emulsions and removing organic dyes from complex aqueous systems have garnered considerable attention in recent years. However, the facile fabrication of high-performance membranes that integrate both separation and adsorption functions remains a significant challenge. Herein, we report the fabrication of a polyacrylonitrile/polyvinyl alcohol–chitosan (PAN/PVA-CS) bilayer membrane with asymmetric wettability via electrospinning. The micro/nanostructures and surface wettability of the as-prepared membranes were precisely tailored by modulating the chitosan (CS) concentration. The resultant PAN/PVA-CS membrane exhibited an overall separation efficiency exceeding 97.5% for mechanically emulsified samples. Notably, the PVA-CS layer demonstrated superhydrophilicity and excellent underwater oleophobicity, enabling the gravity-driven simultaneous separation of oil-in-water emulsions and adsorption of water-soluble Congo red dye without requiring external pressure. The maximum adsorption capacity for Congo red reached 61.3 mg g⁻¹, surpassing that of numerous reported membrane-based and adsorbent materials. Concurrently, the hydrophobic PAN layer in the bilayer structure enabled the separation of water-in-oil emulsions. Overall, this work provides a promising strategy for the rational design of asymmetrically wettable multifunctional membranes with great potential for practical application in the purification of complex industrial wastewater containing both emulsified oils and soluble organic dyes. Full article

Advanced oxidation processes (AOPs) are widely applied to degrade recalcitrant organic contaminants in municipal effluents, industrial wastewaters, and water-reuse streams. Their deployment, however, remains constrained by matrix scavenging, high energy or reagent demand, catalyst/electrode ageing, and the possible formation of toxic transformation products. Artificial intelligence and machine learning (AI/ML) have been proposed as tools for prediction, optimization, catalyst discovery, mechanism inference, and process control, but high accuracy on curated laboratory datasets is often confused with actionable knowledge for real treatment systems. This narrative review evaluates AI/ML-enabled AOPs through an evidence-to-deployment framework built on three principles: real wastewater is treated as the primary inference domain; mechanistic claims are graded according to convergent evidence; and AI/ML contributions are linked to explicit decisions rather than to model accuracy alone. We argue that progress depends less on black-box complexity than on standardized reporting, benchmark matrices, curated datasets, uncertainty-aware validation, and pilot-scale demonstrations that satisfy contaminant removal, energy efficiency, byproduct safety, and operational constraints simultaneously. A six-gate decision framework and a targeted research agenda are proposed to guide future studies toward deployment-grade evidence. Full article

The coordinated enhancement of wood production and carbon sequestration in plantations is increasingly important for sustainable forest management and climate-change mitigation. However, quantitative evidence on how forest management can jointly improve these two functions remains limited. Based on 847 Pinus massoniana plantation plots (yielding 1184 consecutive-period observations) from China’s 7th–9th National Forest Inventories (covering 2004–2018), we quantified the degree of coordination between wood productivity and tree-layer carbon sequestration rate. Linear mixed-effects models, the piecewise structural equation model, and XGBoost-SHAP analyses were subsequently applied to identify the major drivers and threshold ranges of key stand factors. The results showed that mean carbon sequestration rate and wood productivity were 1.16 Mg/ha/yr and 4.17 m³/ha/yr, respectively. Among the examined management categories, plots with standing-volume harvest intensity < 0.15 (i.e., removing less than 15% of stand volume) showed the highest tree-layer carbon sequestration rate and wood productivity. Overall, wood productivity and tree-layer carbon sequestration rate showed broadly consistent responses across the examined management conditions, suggesting a generally high degree of coordination between the two functions. Stand structural attributes were the primary determinants of the degree of coordination, whereas management factors tended to strengthen this coordination both directly and indirectly through modifications of stand structure. Within the sampled range, a higher degree of coordination was associated with stand DBH values of 7.5–10.6 cm, stand density below 1071 trees/ha, stand age exceeding 29 years, and standing-volume harvest intensity approaching 0.12. These findings provide a quantitative basis for balancing timber production and tree-layer carbon sequestration, and offer practical implications for adaptive management of subtropical plantations under climate-mitigation and timber-supply objectives. Full article

The increasing power density of modern electronics demands more efficient thermal management. Heat sinks with sinusoidal fins remain understudied, and the combined effect of perforations and variable fin spacing on transient performance has not been systematically quantified. This numerical study, conducted using ANSYS Fluent 2025 R2, analyzes three sinusoidal fin configurations under forced convection (3–5 m/s): solid fins (Case A), perforated fins (Case B), and perforated fins with alternating spacing of 2 mm and 4.5 mm (Case C). The base was maintained at 60 °C during a 20 s transient period. A mesh with an average skewness of less than 0.25 ensured numerical convergence. Case B showed remarkable uniformity in the base temperature (variations < 1 °C), in contrast to Case A (variations of up to 14.17 °C), due to a thermal boundary layer restart effect induced by the perforations. Case C reached the highest heat dissipation temperatures (up to 54.64 °C at 3 m/s), representing a 47.2% increase compared to Case A, indicating more effective heat extraction with this type of separate fin. The critical transient window occurs within the first 5 s (>85% of the total temperature rise). A vertical temperature gradient of 1.19 °C/mm was observed near the base. Although the perforations reduced the heat transfer area by 5.94%, the induced turbulence compensated for this loss. Sinusoidal fins with perforations and variable spacing significantly improve convective heat removal. Full article

Photocatalytic degradation of organic pollutants has emerged as a promising approach for wastewater treatment due to its environmental friendliness and high efficiency under mild conditions. This study focuses on evaluating materials for the decolorization of methylene blue (MB) and methyl orange (MO), which are commonly used cationic and anionic dyes, respectively, known for their persistence and toxicity in aquatic environments. The research investigates the synthesis of a Mott–Schottky junction at the interface of two materials using MXene as a dopant. We synthesized three MXene-containing Porous Organic Polymers (POP-2MX, POP-6MX, and POP-10MX), incorporating 2%, 6%, and 10% MXene, respectively. UV–Vis spectroscopy tests revealed that all polymers exhibited high degradation efficiency; however, POP-6MX demonstrated the best overall activity. Under illumination of a 500 W Xenon lamp (λ > 420 nm) with a catalyst loading of 1 mg/mL, POP-6MX achieved complete adsorption-corrected degradation of MB and MO within 10 and 45 min, respectively. This research also investigated the influence of pH on photocatalytic performance under homogeneous aqueous conditions, revealing that neutral pH provides the optimal environment for degradation activity. The photocatalytic mechanism follows a reactive oxygen species (ROS)-dominated pathway, primarily driven by superoxide radicals (•O₂⁻) and hydroxyl radicals generated through photochemical reactions. These results demonstrate the potential of POP-1/MXene composites as efficient and recyclable photocatalysts for sustainable dye wastewater treatment applications. Full article

The engineering, resource, and financial constraints in space and spacecraft so far have not allowed the incorporation of biological components into a closed-loop bioregenerative life support system (BLSS), despite decades of research. The expected increase in deep-space exploration and planetary bases with limited access to Earth-based resources necessitates the development of self-sustaining hybrid BLSS technology. The created physicochemical systems, together with photosynthetic organisms and bacteria, aim to revitalize the air, produce food, and recycle nutrients and water in mutually beneficial mini-ecosystems. While plants are best in the function of food production and bacteria in waste recycling, the incorporation of microalgae would add immense benefits in optimizing the life support system (LSS) and increasing the degree of closure. Microalgal photobioreactors (PBRs) could perform wastewater treatment (WWT), removing the nitrogen (N) and phosphorus (P) in the human-derived wastewater (WW), and couple it with converting carbon dioxide (CO₂) from the cabin to oxygen (O₂) and food production. As microalgal WWT on Earth is an emerging field with engineering hurdles, power, mass, volume, microgravity fluid dynamics, and other constraints have also prevented their operations in space. However, in space vehicles, there is no need for large upscaling of a laboratory prototype system, and the WW effluent is easier to predict, facilitating microalgal extraplanetary use in comparison to Earth treatment plants. These factors, combined with the qualities of microalgae such as surface-to-volume efficiency, fast growth rate, high yield, and tolerability to WW, etc., have led to many preliminary testbeds, prototypes, and ground demonstrations from space agencies, space centers, and academia, which show promising results. Microalgal participation in space WWT is beyond current operational practice; however, PBRs are on the space agenda, and the scientific community is elaborating the technologies that would allow their successful implementation. Full article

Galectin-3 (Gal-3) is a β-galactoside-binding lectin implicated in metabolic inflammation, cardiovascular and renal dysfunction, neurodegenerative disorders, and obesity-related pathologies. Although Gal-3 is recognized as a clinically relevant biomarker, the mechanisms controlling its tissue expression and circulating abundance remain poorly defined. O-GlcNAcase (Oga; encoded by Mgea5), the enzyme that removes O-linked β-N-acetylglucosamine (O-GlcNAc) from proteins, regulates nutrient-sensitive signaling and transcriptional processes that overlap with Gal-3 associated disease pathways. To investigate the relationship between metabolic status and Gal-3 expression, male mice were fed a high-fat diet (HFD) for eight weeks to induce obesity. HFD-fed mice exhibited significant increases in body weight and fasting and fed blood glucose levels compared with lean controls, confirming metabolic dysregulation. ELISA revealed approximately threefold higher serum and plasma Gal-3 concentrations in obese mice, indicating enhanced Gal-3 production in diet-induced obesity. To determine whether Oga regulates Gal-3 expression, Oga wild-type (WT), heterozygous (HET), and knockout (KO) mice were analyzed. Circulating Gal-3 protein levels were significantly reduced in Oga KO mice, with intermediate levels in Oga HET animals. RT-qPCR revealed genotype-dependent modulation of Gal-3 (Lgals3) mRNA expression across multiple tissues, demonstrating tissue-specific regulation by Oga. These findings establish Oga as a critical regulator of Gal-3 expression and systemic abundance. The data reveal a mechanistic link between O-GlcNAc signaling enzyme Oga, and lectin-mediated metabolic inflammation, suggesting that Oga activity influences Gal-3 homeostasis and may affect its interpretation as a biomarker in metabolic disease. Full article

This study explores deep eutectic solvents (DESs) as potential alternatives to dimethylformamide (DMF) for extractive desulfurization of gas oil and marine fuel oil. A series of DESs, based on choline chloride (ChCl) and tetrabutylammonium bromide (TBAB), were evaluated using straight-run gas oil (SRGO) and compared to DMF. The influence of an oxidation pre-treatment and water content was also investigated, and the most efficient systems were further applied to marine fuel oil. Firstly, oxidation was found to be essential to enhance sulfur removal by improving the extractability of sulfone compounds. The results show that DES formulation strongly affects desulfurization performance, with TBAB-based DESs achieving efficiencies comparable to DMF (~80% desulfurization rate) and outperforming ChCl-based systems. In ChCl-based DESs, a moderate water content (~10 wt%) improved performance, whereas higher amounts disrupted the hydrogen-bond network, as evidenced by Fourier-transform infrared spectroscopy, leading to a decreased efficiency. When applied to marine fuel oil, similar trends were observed, although lower desulfurization levels were obtained due to the complexity of the feed, and a solvent-to-fuel ratio of 5:1 was required for proper phase separation. Fourier transform mass spectrometry analyses highlighted the persistence of refractory sulfur species, emphasizing the need for tailored solvent design. Overall, DESs represent promising and adaptable alternatives for desulfurization processes. Full article

Chlorpyrifos, a widely used organophosphate pesticide, poses significant environmental risks due to its persistence and the formation of toxic transformation products. Despite extensive research on iron-based Fenton systems, the application of manganese oxides, particularly α-Mn₂O₃, in chlorpyrifos degradation remains insufficiently explored. In this study, we investigated the catalytic performance of α-Mn₂O₃ in Fenton and visible-light-driven photo-Fenton processes for the degradation of chlorpyrifos in aqueous systems. Chlorpyrifos oxon was identified as a transient intermediate, detected at trace levels, supporting an oxidative degradation pathway. Kinetic analysis revealed pseudo-first-order behavior, with comparable rate constants for Fenton reactions at different catalyst loadings (0.0033 min⁻¹ for 5 mg and 0.0028 ± 0.0006 min⁻¹ for 10 mg), indicating that the process is not limited by catalyst concentration under the investigated conditions. In contrast, the photo-Fenton system exhibited a higher rate constant (0.0042 min⁻¹) and significantly improved degradation efficiency, highlighting the role of visible-light activation. The highest removal rates of chlorpyrifos were 86.24% for Fenton experiments and 96.05% for the photo-Fenton experiment, respectively. The enhanced performance is attributed to the photocatalytic properties of α-Mn₂O₃, including its narrow bandgap and the facilitation of Mn³⁺/Mn²⁺ redox cycling, which promotes reactive oxygen species generation. These findings demonstrate that α-Mn₂O₃ is a promising non-iron catalyst for advanced oxidation processes and provide new insights into manganese-mediated Fenton-like mechanisms for the removal of organophosphate contaminants. Full article

This study investigates the feasibility of utilizing locally sourced Nicotiana tabacum biomass from Adıyaman, Türkiye, as a cost-effective biosorbent for the removal of copper and chemical oxygen demand (COD) from industrial wastewater originating from the Adıyaman Organized Industrial Zone. Batch adsorption experiments were conducted to systematically investigate the influence of solution pH, contact time, and initial metal concentration on adsorption performance. The untreated wastewater exhibited elevated pollution levels, with mean chemical oxygen demand and copper concentrations of 925 ± 391 mg/L and 2.54 ± 0.97 mg/L, respectively. Four tobacco-derived biosorbents (Çelikhan, Ova, Bulam, and Çağlan) were evaluated under optimized experimental conditions (pH ≈ 8.3, 60 min contact time, and a biosorbent dosage of 2.2 g/L). The Çelikhan biosorbent exhibited the highest copper removal efficiency (approximately 83%), whereas chemical oxygen demand removal ranged between 28% and 34%. The adsorption kinetics were well described by the pseudo-second-order model, with coefficients of determination ranging from 0.987 to 1.000. Isotherm analysis further indicated favorable adsorption behavior, with a maximum Langmuir adsorption capacity of 1.867 mg/g. Fourier transform infrared (FT-IR) spectroscopy confirmed the involvement of hydroxyl, carbonyl, and ester functional groups in metal binding. These findings highlight tobacco biomass as a sustainable and cost-effective biosorbent for industrial wastewater treatment. This study presents the first comprehensive evaluation of locally sourced Adıyaman tobacco biomass as a biosorbent for the removal of copper and organic pollutants from real industrial wastewater, integrating kinetic, isotherm, and FT-IR analyses to elucidate the underlying adsorption mechanisms. Full article