Applied Chemical Engineering

Abstract Waste-to-energy (WtE) technologies are increasingly important for sustainable waste management and circular economy practices, as they enable recovery of energy from municipal, agricultural, and industrial wastes while reducing landfill use and associated emissions. Despite this relevance, existing research on machine learning (ML) applications in WtE systems remains fragmented, with most studies addressing individual processes, specific algorithms, or isolated performance metrics, and lacking an integrated perspective across the full value chain. The objective of this work is to provide a comprehensive review of machine learning applications in WtE systems, covering resource evaluation, conversion efficiency, and environmental effects within a unified framework. The study is based on a systematic analysis of recent peer-reviewed literature reporting experimental validation or applied modeling in incineration, gasification, pyrolysis, and anaerobic digestion processes. The review indicates that machine learning models successfully capture the nonlinear and time-varying behavior of WtE systems, allowing accurate prediction of waste generation and composition, heating value, biogas yield, process efficiency, and pollutant emissions. Tree-based ensembles and neural networks show strong performance in feedstock assessment and conversion modeling, while data-driven soft sensors and surrogate models support real-time emission prediction and life-cycle impact evaluation. These findings demonstrate that machine learning offers practical benefits for improving operational stability, energy recovery, and environmental compliance in WtE plants, while also highlighting persistent challenges related to data quality, model transferability, and interpretability that should guide future research and deployment.

Abstract CC-Ag NPs  (Silver nanoparticles) were synthesized from the leaf extract of Cymbopogon citratus . The biosynthesized silver nanoparticles were characterized by different analytical techniques. A characteristic absorption peak at 432 nm was observed which confirmed the surface plasmon resonance in silver nanoparticles (AgNPs). FTIR analysis showed the presence of bioactive compounds that take part in reduction and stabilization processes. The results of XRD analysis showed a crystalline phase formation only corresponding to silver nanoparticles. AFM and SEM analyses revealed that most of the particles are spherical with an average particle size of about 40 nm. The biosynthesized silver nanoparticles showed strong antibacterial effects against different pathogenic bacteria that covered both Gram-positive and Gram-negative strains. These included Proteus mirabilis , Bacillus subtilis , Staphylococcus aureus , Escherichia coli , Klebsiella pneumoniae , Vibrio cholerae , Vibrio parahaemolyticus  and Salmonella enteritidis . Among all these tested organisms, the maximum zone of inhibition was observed against Vibrio parahaemolyticus  with a diameter of 34.67mm. The concentration of the synthesized AgNPs solution used in these experiments was 1 mM.

Abstract Biomass resource mappings are essential tasks for sustainable energy planning, since it offers information on the potential supply, geographical availability of resources, plant sitting, transportation opportunities and roadmap towards long term renewable energy concepts that have policy relevance. Its relevance is increasing as countries are embracing low carbon economy’s roadmaps which demand for reliable spatial quantitative estimations of forest and waste residues – based biomass potentials. Despite the significant headway, there are still some loopholes in applying remote sensing and machine learning techniques. These limitations comprise scarcity of good quality field data for model calibration, poor integration of socio–economic drivers and difficulties in representing fine–scale spatial variability that hinder accurate estimate of yields at different spatial levels. This paper surveys recent machine learning methods for AGB estimation, discusses their methodological limitations, and proposes future research avenues toward scalable and robust forest biomass mapping. A combination of satellite observations, GIS–based layers and ground inventory data sets are included in the analysis as well as a variety of regression, tree based, kernel based, neural network, deep learning and hybrid modelling approaches over various land coverage areas. According to the previous works, evidence is gathered from the surveyed studies that ensemble and deep learning approaches can enhance prediction performance on multi–source data; GIS–machine learning integration contributes to better site selection and logistics analysis. The results also demonstrate the potential for a combined framework that exploits transfer learning approaches and digital twin methodologies to reduce prediction uncertainty, especially in low–data areas. Such information could help support rational decision–making activities for policymakers, planners and industry actors that consider the role of bioenergy in national energy security, climate change mitigation strategies, resource sustainability and long–term renewable energy planning.

Abstract The increasing complexity of bio-energy systems is a reason for the need of advanced analytical methods to enhance resource utilization, process stability and environmental performance. AI methods are popular in this domain, but many papers neglect concerns around data quality, interpretability, scalability and the low generalization potential of models toward plants and feedstocks different from those they were trained on. This paper intends to offer a systematic review on AI techniques available for biomass resource assessment, conversion-process optimization, and supply-chain planning and emission management. The review is structured adopting a rigorous review approach focusing on model, data set, optimization framework and hybrid method developed in the scope of bio-energy value chain. Highlights – The key findings are that AI improves the prediction of biomass availability and biogas/syngas yields, of feedstock properties and emission behavior; surrogate and hybrid models result in expedited simulation time and facilitate real-time decision making. The review also highlights an emerging trend with digital twins, remote sensing with application of machine learning, and federated learning in multi-plant optimization. These findings also have important implications for researchers, engineers and policy-makers who aim to develop robust low emissions bio-energy systems that are economically feasible.

Abstract This study presents a novel, one-pot synthetic strategy for preparing triazole and oxadiazoline derivatives directly from naproxen. This approach aligns with the principles of green chemistry, aiming to enhance synthetic efficiency by minimizing reaction steps and reducing waste. By eliminating the need for multiple isolation and purification stages, this method offers a sustainable alternative to conventional multi-step procedures. The synthesized compounds underwent structural confirmation using a suite of spectroscopic techniques, specifically Fourier-Transform Infrared  spectroscopy, Proton Nuclear Magnetic Resonance  spectroscopy, and Carbon-13 Nuclear Magnetic Resonance spectroscopy and CNMR dept 135 and CNMR dept 90. Further analysis supported their potential as anti-inflammatory agents through molecular docking studies. These studies demonstrated strong binding affinities of the compounds to the cyclooxygenase-2 (COX-2) enzyme, suggesting a favorable mechanism of action for anti-inflammatory activity. Additionally, their acute toxicity was assessed by determining the LD50 values, providing preliminary data on their safety profile. Collectively, the new derivatives exhibited promising multi-target activity. The synthesized compounds exhibit potent broad-spectrum antimicrobial effects, demonstrating significant efficacy against both “Gram-positive bacteria, Staphylococcus aureus  and Staphylococcus epidermidis” , and “Gram-negative bacteria, including Klebsiella  species and Escherichia coli . Furthermore, they show promising antifungal activity against the pathogenic yeast Candida albicans” . This research demonstrates that a sustainable, one-pot synthesis can efficiently generate new compounds with valuable biological properties. Sustained-release naproxen derivatives show significant potential for future development in medicinal chemistry. This work highlights the constructive collaboration between green chemistry principles and the discovery of novel therapeutic agents.

Abstract The increased new and remerging infectious diseases, need to discovery and urgent of new microbial compounds having diverse chemical structures and novel mechanism have also been. The study focused on determining the antibacterial properties of  pomegranate peels (Punica granatum), Cordia myxa fruits, and Citrullus colosynthis fruits, against pathogenic bacteria E.coli, Pseudomonas aeruginosa as Gram-negative and Staphylococcus aureus as Gram-negative. Also the antioxidant as free radical scavenge to the plants   The plant parts were collected, shade dried and powdered and subjected to extraction by methanol maceration as solvents. The extracts were used to determine the antibacterial activity by agar well diffusion method and MIC assay. The results showed that the plant extracts inhibited the growth of both Gram-positive and negative bacteria, Gram-negative bacteria were more sensitive. The inhibition zone (in millimeters) was greater for Gram-negative bacteria (17 mm) than for Gram-positive bacteria (0 mm) with Cordia myxa . Punica  peel and Citrullus colocynthis  fruits extracts exhibited high biological activity against all types of bacteria at concentrations of 0.1, 0.5, and 1 mg/mL, respectively. Cordia myxa  had the highest percentage of free radical scavenging, followed by Punica  and then Citrullus colocynthis.  On the basis of the Data obtained it is clear that plant extracts can be used as effective herbal cure against human pathogens.

Abstract A turbine blade on an aircraft's jet engine is a component of the turbine section of turbojet engines. These blades extract energy from high-pressure gas flows and rising temperatures, making them subject to high-temperature gradients. Functionally graded materials are promising materials used in this research to improve blade performance in this challenging environment. This study analyzes the development, manufacturing, and characterization of multiple aluminium, nickel, and 316 steel alloys combined within multi-functionally graded materials that have been successfully fabricated using the powder metallurgical method. The three functionally graded material samples used in this study consist of five layers, starting with AL-NI (75% Ni to 25% Al) on one side and endi ng with Ni-AL and 316 steel (3.33% Ni to 33.33% Al to 33.33% steel) wt% on the other. After determining the mechanical characteristics of each layer of the functionally graded beam both before and after fatigue cracking, the natural frequency of the samples is calculated. As a result, it was found that the combination of 316L steel and particle concentration improved the mechanical properties of the Al-Ni alloy, making it a practical and lightweight alternative to steel structural elements. The 316L steel hybrid alloys showed favourable results in tensile tests and demonstrated stability in long-term fatigue tests. Additionally, the study found that fracture mechanics can accurately predict fatigue life, and that milled and blended Al-Ni-316L steel behaves similarly to a metal powder compact, with consolidation involving particle rearrangement and plastic deformation. The tensile tests carried out at 20°C demonstrated that Sample 2 had the best mechanical performance, with a yield strength of 679 MPa, ultimate tensile strength of 825 MPa, and elastic limit of 708 MPa, which are improvements of +12.6%, +11.7%, and +17.4% more than Sample 1, correspondingly. Fatigue-life experiments were carried out at a stress ratio R = –1, excitation frequencies of 25–27 Hz, and using loads ranging between 5.5–17 kN. The fatigue lives measured for a constant frequency corresponded to 55,000 cycles, while for random vibration patterns 2 and 3, they were 75,400 and 64,000 cycles, correspondingly. The longest fatigue life was demonstrated by Sample 2, showing a +37% improvement over Sample 1. The FGMs revealed stable first-mode natural frequencies around 25–27 Hz, while resonance was strongly affecting fatigue damage. The Al/Ni/Steel FGM compact specimen was also found to exhibit comparable yield and ultimate stress values to steel, thus enhancing its mechanical properties while reducing weight. Keywords:  Turbojet engines; Turbine blades; Functionally Graded Materials; Mechanical         Characteristics

Abstract Environmentally friendly methods are recommended for the petroleum refining industries, for treatment of the generated wastewater. Ammonia, sulfides, phenol, oil and grease, and petroleum hydrocarbons, and other chemical compounds are only a few of the many pollutants found in effluent from petroleum refineries. Major disadvantages of conventional treatment systems for refinery effluent include low effectiveness, high operating and capital costs, and susceptibility to low toxicity and biodegradability. Refinery wastewater has been treated using a variety of techniques, such as chemical, biological, physical, and hybrid approaches. Advanced oxidation processes (AOPs) have elaborated improved efficacy. Because of its multiple uses, affordability, and environmental friendliness, the Fenton process has emerged as a desirable substitute for other AOPs in the removal of contaminants from water. Therefore, the full comprehension of the Fenton process in petroleum refinery wastewater management is the main subject of this study by going over the major developments in the catalytic oxidation of nano-based Fenton materials for the treatment of petroleum refinery wastewater. Nano-based particles are widely used in Fenton catalysis because of their cheap cost and plentiful supply, particularly α-Fe2O3, as well as their high removal efficiency of chemical oxygen demand (COD) and concentrations of phenol, with an average elimination efficiency surpassing 90%. The current review discusses current developments in the manufacture and use of Fenton catalysts that are heterogeneous for the reduction of organic contaminants. Since the reaction between solid Fenton catalysts and H2O2 can produce extremely reactive hydroxyl radicals (HO%), heterogeneous Fenton reactions have drawn a lot of interest in eliminating stubborn organic pollutants. The aim of this study was to thoroughly examine the studies that have been conducted to improve heterogeneous Fenton reactivity for effective application in treating.  

Abstract We present a computational investigation of Gefitinib, a clinically approved EGFR inhibitor, and Quercetin, a bioactive flavonoid with anticancer potential, through an integrated density functional theory (DFT), time-dependent DFT (TD-DFT), and molecular docking framework. Geometry optimization at the B3LYP/6-31G(d,p) level yielded electronic and global reactivity descriptors, providing insights into molecular stability and interaction potential. Quercetin exhibited higher electronic stability, with a HOMO–LUMO gap of 5.22 eV compared to 4.24 eV for Gefitinib. TD-DFT simulations predicted key absorption bands at ~369 nm for Gefitinib and ~310 nm for Quercetin, correlating with their distinct electronic transitions. Docking studies against the EGFR tyrosine kinase domain (PDB: 4HJO) revealed stronger predicted binding for Quercetin (−8.9 kcal/mol) than Gefitinib (−5.5 kcal/mol), supported by hydrogen bonding and π–π stacking interactions. These findings highlight how computational chemistry techniques, widely applied in materials design and process optimization, can also provide multiscale insights into drug–target interactions. By integrating electronic structure analysis with molecular docking, this study demonstrates a transferable approach relevant to applied chemical engineering, bridging quantum chemistry with molecular interaction studies in pharmaceutical and materials science contexts.

Abstract Bio-energy systems are frequently promoted as low-carbon substitutes for fossil fuels and are closely related to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Actual environmental performance, however, is contingent on supply-chain design, choice of feedstock, set-up of the technology and local operating conditions. The life-cycle assessment (LCA) is currently the predominant scientific instrument for assessing these impacts in a comprehensive approach linked to SDG 12 Responsible Consumption and Production. Traditional LCA, however, has to deal with a variety of challenges including data scarcity, spatial and temporal variations, and the necessity to analyze several scenarios depending on changing circumstances. In recent years, some of these limitations can be mitigated by using artificial intelligence (AI) and machine learning (ML) techniques. This approach facilitates inventory data extrapolation, gap filling and estimation of the nonlinear function between process variables and environmental parameters, all leading to more dynamic and data rich assessment which reflects SDG 9 (Industry, Innovation and Infrastructure). This review consolidates a summary of up-to-date studies on AI-enabled LCA approaches for bio-energy systems and pollution assessment methods that interface directly with sustainability evaluation. The paper describes a general description of key aspects related to bio-energy supply chains in LCAs regarding impact categories, including greenhouse gas (GHG), air pollutants, land use and water use with relevance to SDG 6-Clean Water and Sanitation and SDG 15-Life on Land. It subsequently provides an overview of AI and ML applications covering the full bio-energy life cycle, including aspects related to biomass resource assessment and feedstock production as well conversion, upgrading, refining, distribution to end use. Particular focus is given to works that integrate ML with LCA metrics for the prediction of environmental performance or for the optimization of process conditions through sustainability-based indicators. The review also presents AI-facilitated pollution monitoring applications, such as deep learning techniques for emissions detection using remote sensing data, carbon emissions prediction, and air quality monitoring through ML for betterment of SDG 11 (Sustainable Development Goals: Sustainable cities and communities). Major challenges including model interpretability, system boundary consistency, uncertainty propagation and hybrid modelling requirements have been identified. Next, AI focusing on digital twin, scenario analysis supported by AI and interactive LCA tool for informed decision making.

Abstract The performance and longevity of polymer filaments in Fused Deposition Modeling Additive Manufacturing (FDM-AM) are highly dependent on their chemical and thermal stability, which can be significantly enhanced by the incorporation of functional additives. This study explores the role of stabilizers, plasticizers, nanofillers, and antioxidant agents in improving the structural integrity and printability of common polymers such as polylactic acid (PLA), polyethylene terephthalate glycol (PETG), and acrylonitrile butadiene styrene (ABS). Analytical chemistry techniques, including Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and gel permeation chromatography (GPC), are reviewed and applied as critical tools to evaluate molecular interactions, degradation kinetics, and additive dispersion within the polymer matrix. Results highlight that additives not only suppress thermo-oxidative degradation and moisture sensitivity but also influence glass transition behavior, crystallinity, and filament rheology, ultimately leading to improved dimensional accuracy and mechanical performance in printed parts. This work provides a comprehensive framework for correlating chemical stability with processing reliability in FDM-AM, offering insights for the development of next-generation, high-performance, and durable polymer filaments tailored for sustainable and industrial applications.

Abstract The rapid growth of bio-energy production is closely aligned with global sustainability agendas, particularly the Sustainable Development Goals (SDGs) related to Affordable and Clean Energy (SDG 7) , Industry, Innovation and Infrastructure (SDG 9) , and Climate Action (SDG 13) . Effective pollution monitoring across the bio-energy production chain is essential to ensure that renewable energy expansion does not lead to unintended environmental burdens. Current research largely treats artificial intelligence (AI) applications in environmental monitoring and bio-energy systems as separate domains, creating a research gap in integrated, process-wide frameworks that connect emission sources, sensor networks, data pipelines, and AI models across all production stages. The objective of this study is to critically review AI-based pollution monitoring approaches for bio-energy systems and to assess their capability to support sustainable and responsible energy production in line with SDG targets. The methodology involves a structured synthesis of recent literature on sensing technologies, data acquisition and preprocessing, machine learning and deep learning models, and hybrid physics-informed approaches applied from biomass handling to biofuel refining. The key findings show that AI-enabled monitoring improves real-time emission estimation, early detection of abnormal events, and short-term forecasting, supporting cleaner production pathways. At the same time, challenges related to sensor drift, data scarcity, model transferability, and interpretability limit large-scale adoption. The implications of this review highlight the need for open benchmark datasets, robust calibration strategies, and explainable AI models to strengthen regulatory trust, promote sustainable industrial practices, and contribute directly to achieving SDG-linked environmental and energy objectives.

Abstract Hydroxyapatite (HAp) is a bioactive calcium phosphate ceramic, which is the major inorganic constituent of natural teeth and bone. The current research paper involves the production of hydroxyapatite using chicken bone waste through a sustainable process and its pre- and post-processing with the use of microwave post-treatment. The originality of the work is the synthesis of a biogenic source of calcium with the irradiation of microwaves in order to adjust the structural and electrochemical characteristics of HAp. HAP1 was prepared as a sample by grinding and then by calcification, and then HAP2 was prepared by treating HAP1 with microwave. X-ray diffraction (XRD) ensured that the two samples are crystalline, and the microwave treatment caused slight changes in the peak and the size of crystallites. FESEM and TEM observations showed that HAP2 had smaller and more homogenous particles with lower agglomeration than HAP1. EDS identified the Ca, P, and O as the significant elements with minor traces of Mg and Na being biogenic in nature. Electrochemical characterization showed better ionic mobility, charge-transfer behavior, and capacitance of HAP1, but the treatment of microwave treatment raised the internal resistance and lowered ionic conductivity. In general, the research illustrates that microwave processing can increase the level of morphological homogeneity of biogenic HAp, but it has a comparable negative effect on the electrochemical activity.

Abstract Offshore wind power, as a key clean energy in the energy transition, has significant advantages in resource abundance and stability. However, current offshore wind turbine foundation designs face high carbon emissions, and the variability of offshore power generation easily increases the operational costs of the power system. In the design of basic structures, the chemical properties of materials have a significant impact on carbon emissions, costs and performance. However, current research lacks in-depth exploration of common directions in applied chemistry such as materials, reactions and chemical processes. This study proposes a low-carbon design of offshore wind turbine foundations and an optimization model of multi-energy complementary new energy systems based on smart grid collaboration. The experimental results indicated that after optimization with the proposed algorithm, the carbon emissions reached 2087.2 tons. The average cost of the proposed model is 7531.67 dollars, and the power balance constraint during peak load periods is satisfied at a rate of 98.51%. The supply insufficiency during low load periods is only 0.72%, and both curtailment rate and unit output exceedance occurrences are improved. These findings suggest that the proposed model was capable of achieving an effective balance between economic efficiency and environmental performance of offshore wind turbine foundations and promote low-carbon and efficient development of offshore wind power and new energy systems

Abstract This study attempts to explore the determinants of Nepal's export performance with focus on the energy variable. The major aim of the study is to find the impact of rising use of renewable energy and growing energy import on export trade of landlocked country Nepal in the context of growing trade deficit after 1990 to till date. Auto Regressive Distributed Lag Model (ARDL) for the time series data from 1990 to 2021 reveal the positive contribution of renewable energy consumption, gross fixed capital formation and energy imports. However, exchange rate did not show the significant result. The findings further reveal the fact that Nepalese export industries are still highly vulnerable to energy shocks besides having high potential for the generation of renewable energy. Thus, increasing the speed of generation of renewable energy and transfer it for industrial use could help to minimize energy import shock, reduce energy cost and increase export competitiveness. The findings suggest that national trade strategies should be linked up with renewable energy use and policymakers should rethink about restructuring trade based exchange rate system in Nepal.

Abstract The two laser dyes used were methyl blue and methylene blue, which exhibited favourable optical properties. The linear optical properties were enhanced by incorporating methyl blue or methylene blue dyes into the copper oxide nanoparticles added to polymethyl methacrylate dissolved in Acetone, improving the optical properties important for future applications in laser materials. The prepared samples were examined by a UV-Vis spectrophotometer at wavelengths ranging from 200 to 800 nm. The results showed that the incorporation of methylene blue dye into the (CuO-PMMA) nanocomposite thin films significantly increased some optical properties, including absorbance, absorption coefficient, extinction coefficient, and refractive index. Meanwhile, the transmittance and energy gap decreased with increasing dye concentration. The addition of methyl blue dye had less effect on these properties and yielded values close to those of the nanocomposites that did not contain the dye. The structural properties of the nanocomposites were studied. The structural properties included Field emission scanning electron microscopy (FE-SEM) images and XRD analysis. The SEM images showed the distribution of copper oxide nanoparticles within the PMMA polymer and demonstrated the diffusion of laser dyes within the nanocomposites. Methyl blue dye forms rough, irregular aggregates on the surface, creates darker areas and reduces solubility. In contrast, methylene blue forms a uniform thin layer that covers the nanoparticles and enhances surface contact through hydrogen bonds, which masks the material crystallinity and affects its mechanical and optical properties.

Abstract The adsorption of the potentially toxic industrial dyes, Acid Red 18 (AR18), Acid Yellow 23 (AY23), Reactive Yellow 84 (RY84), and Reactive Black 5 (RB5). was assessed in this work using non-activated and activated sunflower seed husks as environmentally sound adsorbents. Hydrochloric, phosphoric and sulfuric acids activated the sunflower seed husk, and the best chemical activation method was determined by surface morphology and the adsorption performance. The optimal temperature for thermal treatment to convert biomass into activated carbon has been established. The structural and chemical characteristics of the adsorbents were investigated by the characterization techniques such as Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDX), Transmission Electron Microscopy (TEM), and Fourier-Transform Infrared Spectroscopy (FTIR). The percentage removals of Reactive Yellow, Reactive Black 5, Acid Red, and Acid Yellow 23 were determined to be (95.02%, 90.00%, 85.00% and 70.00% for the acid-activated sunflower seed husks. These values were significantly higher than the adsorption capacities of non-activated husks, 80.02%, 75.27%, 55.70%, and 45.00%, respectively, for the corresponding dyes. The findings demonstrate an improvement in the properties of acid-activated sunflower seed husks in the adsorption of dyes (Reactive Black 5 and Acid Red), reflecting the influence of acid activation on expansion of the specific surface area and availability of functional groups. Adsorption efficiency was highest under acidic conditions due to enhanced electrostatic attraction between the adsorbent surface and dye molecules. Reactive dyes showed greater sensitivity to pH variation, with efficiency dropping sharply in alkaline media. In contrast, Acid dyes—particularly Acid Yellow—retained higher performance across the pH range, indicating additional binding mechanisms beyond electrostatic interactions.

Abstract Background: Studies have shown that trace elements may adversely affect male reproduction, even at low levels. The toxic effects of heavy metals on the reproductive system have been mainly studied in   animal experiments, and epidemiological evidence for populations exposed to the general environment is limited and inconsistent. Objectives: Our aim in this study was to analyze the relationship between semen quality and multiple metals or metalloids (Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb, As and Se) in men undergoing premarital medical examinations (PME) and living in the same city. Methods: Among 1202 PME men, semen quality and sperm DNA integrity were measured by using flow cytometry. Urine heavy metals were tested using a mass spectrometer at our infertility clinic. The urinary levels of various metals and the sperm DNA fragmentation rate of men with normal sperm parameters (NSP) and abnormal sperm parameters (ASP) were compared. Results: Among1202 males, 42.0% (505/1202) were smokers, and 42.9% (516/1202) were alcohol users. A total of 594 men (594/1202, 49.4%) had NSP such as sperm concentration, total sperm, sperm motility and sperm morphology, whereas 608 men (608/1202, 50.6%) had some type of sperm-pathology (ASP); 600 had oligo- or astheno- or terato- or oligoasthenoteratozoospermia (OAT), 8 azoospermia 8/1202, 0.7%). The males of long-term outdoor work, smoking and drinking alcohol in ASP group were 252 (41.4%) , 308 (50.7%) and 283 (46.5%), respectively, and more than NSP group [(180, 30.3%), (33.1%) and (39.2%)] (P<0.05). There were no significant differences between ASP and NSP in semen volume, liquefaction time, pH, and round cells. The DNA fragmentation index shouwed a statistically significant difference between the two groups (13.3% ± 5.9%, 585 NSPs; vs 16.6% ± 14.1%, 585 ASPs, P<0.001). The mean concentrations of vanadium (V), chromium (Cr), cobalt (Co) and lead (Pb) and metalloid arsenic (As) were statistically significant between the two groups [(0.58 ± 0.42 µg, 483 NSPs; vs 0.65 ± 0.62 µg, 483 ASPs, P=0.043), (5.86 ± 15.14 µg, 483 NSPs; vs 11.94 ± 46.76 µg, 483 ASPs, P=0.007), (0.29 ± 0.26 µg, 483 NSPs; vs 038 ± 0.66 µg, 483 ASPs, P=0.008), (4.26 ± 1.90 µg, 483 NSPs; vs 6.47 ± 18.00 µg, 483 ASPs, P=0.008) and (39.69 ± 59.92 µg 592 NSPs; vs 51.85 ± 78.58 µg 598 ASPs)]. Conclusion: Our study suggests that high levels of vanadium, chromium, cobalt, lead and arsenic in urine may adversely affect sperm count, motility, morphology and sperm DNA integrity. Long time of outdoor work, smoking and alcohol consumption are more strongly associated with poorer sperm quality.

Abstract The release of volatile organic compounds (VOCs) during fused deposition modeling additive manufacturing (FDM-AM) has become a critical concern due to its implications for occupational health, indoor air quality, and material performance. In this study, a quantitative chromatographic analysis was conducted to characterize and evaluate VOC emissions from commonly used thermoplastic filaments during FDM-AM. Gas chromatography coupled with mass spectrometry (GC–MS) was employed to separate and identify volatile fractions, while flame ionization detection (FID) provided quantitative assessment of emission concentrations. Representative results revealed the presence of styrene, ethylbenzene, formaldehyde, acetaldehyde, and other low-molecular-weight aldehydes and ketones, with emission profiles varying significantly across polymer types such as ABS, PLA, and PETG. Peak intensities correlated strongly with extrusion temperature, suggesting that process parameters directly influence VOC release. Comparative analysis indicated that ABS exhibited the highest emission intensity, dominated by aromatic hydrocarbons, while PLA produced lower total VOCs but higher proportions of lactide-derived species. The findings underscore the necessity of systematic monitoring of VOCs in FDM-AM environments and provide quantitative evidence for optimizing process conditions and implementing adequate ventilation systems. This work establishes a framework for linking chromatographic signatures of volatile compounds with material choice and processing parameters, contributing to safer and more sustainable additive manufacturing practices.

Abstract The creation, delivery, and dissemination of chemical engineering knowledge in a globally interconnected setting are being redefined by the digital transformation of higher education. Massive Open Online Courses (MOOCs) have emerged as important venues for increasing access to engineering education while fostering interdisciplinary cooperation and cross-cultural learning. In order to investigate how MOOCs promote digital transformation and Intercultural Communicative Competence (ICC) in chemical engineering education, this study systematically reviews 24 peer-reviewed articles (2020-2025) using the PRISMA methodology. According to research, MOOCs foster educational innovation by incorporating flipped learning, simulation-based experimentation, and group projects that improve students' technical and intercultural competencies. In order to solve sustainability and process-design issues, they also enable interdisciplinary integration by linking chemical, computational, and environmental disciplines. However, enduring obstacles include unequal access to technology, a lack of established ICC evaluation tools, and poor cultural contextualisation limit wider impact. This review offers a conceptual framework for a Global Chemical Engineering Education Ecosystem based on constructivist, connectivist, and intercultural communication theories. MOOCs are positioned as socio-technical environments that connect digital, interdisciplinary, and intercultural learning processes. The study comes to the conclusion that pedagogical intentionality, institutional backing, and inclusive digital infrastructures that train internationally competent and morally conscious engineers are necessary for MOOCs to have their revolutionary potential.