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Oily wastewater is a major environmental issue resulting from different industrial and manufacturing activities. Contaminated water with oil represents a significant environmental hazard that can harm numerous life forms. Several methodologies have been tested for the removal of oily wastewater from aqueous solutions, and adsorption in a flow-through reactor is an effective mechanism to reduce these effluents. This study focuses on evaluating the ability of Fe3O4/Bent material to adsorb gasoline emulsion from a solution using a fixed-bed column, and it involves analyzing the resulting breakthrough curves. The FT-IR, SEM, EDX, and XRD techniques were used to characterize Fe3O4/Bent. Various ranges of variables were examined, including bed height (2–4 cm), flow rate (3–3.8 mL/min), and initial concentration (200–1000 mg/L), to determine their impacts on the mass transfer zone (MTZ) length and the adsorption capacity (qe). It was shown that a higher bed height and a lower flow rate contributed to a longer time of breakthrough and exhaustion. At the same time, it was noted that under high initial gasoline concentrations, the fixed-bed system rapidly reached breakthrough and exhaustion. Models like the Yoon–Nelson and Thomas kinetic column models were employed to predict the breakthrough curves. Thomas and Yoon–Nelson’s breakthrough models provided a good fit for the breakthrough curves with a correlation coefficient of R2 > 0.95. Furthermore, with a fixed-bed system, the Thomas and Yoon–Nelson models best describe the breakthrough curves for gasoline removal.

In this work, MXene as a hydrophilic 2D nanosheet has been suggested to tailor the polyphenylsulfone (PPSU) flat sheet membrane characteristics via bulk modification. The amount of MXene varied in the PPSU casting solution from 0–1.5 wt.%, while a series of characterization tools have been employed to detect the surface characteristics changes. This included atomic force microscopy (AFM), scanning electron microscopy (SEM), contact angle, pore size and porosity, and Fourier-transform infrared spectroscopy (FTIR). Results disclosed that the MXene content could significantly influence some of the membranes’ surface characteristics while no effect was seen on others. The optimal MXene content was found to be 0.6 wt.%, as revealed by the experimental work. The roughness parameters of the 0.6 wt.% nanocomposite membrane were notably enhanced, while greater hydrophilicity has been imparted compared to the nascent PPSU membrane. This witnessed enhancement in the surface characteristics of the nanocomposite was indeed reflected in their performance. A triple enhancement in the pure water flux was witnessed without compromising the retention of the membranes against the Cu2+, Cd2+ and Pd2+ feed. In parallel, high, and comparable separation rates (>92%) were achieved by all membranes regardless of the MXene content. In addition, promising antifouling features were observed with the nanocomposite membranes, disclosing that these nanocomposite membranes could offer a promising potential to treat heavy metals-containing wastewater for various applications.

In the current work, a Gum, Arabic-modified Graphene (GGA), has been synthesized via a facile green method and employed for the first time as an additive for enhancement of the PPSU ultrafiltration membrane properties. A series of PPSU membranes containing very low (0–0.25) wt.% GGA were prepared, and their chemical structure and morphology were comprehensively investigated through atomic force microscopy (AFM), Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction (XRD), and field emission scanning electron microscopy (FESEM). Besides, thermogravimetric analysis (TGA) was harnessed to measure thermal characteristics, while surface hydrophilicity was determined by the contact angle. The PPSU-GGA membrane performance was assessed through volumetric flux, solute flux, and retention of sodium alginate solution as an organic polysaccharide model. Results demonstrated that GGA structure had been successfully synthesized as confirmed XRD patterns. Besides, all membranes prepared using low GGA content could impart enhanced hydrophilic nature and permeation characteristics compared to pristine PPSU membranes. Moreover, greater thermal stability, surface roughness, and a noticeable decline in the mean pore size of the membrane were obtained.

Among many contaminants in wastewater, organic phenol compounds presented a major concern to endanger the water resources safety. In the present study, blend nanofiltration (NF) membranes comprising polyphenylsulfone (PPSU) and polyethersulfone (PES) were prepared via the non-induced phase separation and their performance was examined against 4-Nitrophenol (4-NP). The PES ratio in the dope solution was varied from 6 to 9 wt.% to probe the impact of PES on the retention and permeation characteristics of the final membranes. A series of experimental tools were employed to estimate the characteristics of the membranes, including surface and cross-section, hydrophilicity, pore size and pore size distribution. Performance evaluation of the NF membranes was conducted considering two operational variables; pH and initial feed solution. About 99% removal of 4-NP along with 6.2 L/m2·h·bar was achieved at the optimum operating conditions as revealed by optimization and mathematical modelling.

The present work reports the performance of three types of polyethersulfone (PES) membrane in the removal of highly polluting and toxic lead Pb2+ and cadmium Cd2+ ions from a single salt. This study investigated the effect of operating variables, including pH, types of PES membrane, and feed concentration, on the separation process. The transport parameters and mass transfer coefficient (k) of the membranes were estimated using the combined film theory-solution-diffusion (CFSD), combined film theory-Spiegler-Kedem (CFSK), and combined film theory-finely-porous (CFFP) membrane transport models. Various parameters were used to estimate the enrichment factors, concentration polarization modulus, and Péclet number. The pH values significantly affected the permeation flux of the Pb2+ solution but only had a slight effect on the Cd2+ solution. However, Cd2+ rejection was highly improved by increasing the pH value. The rejection of the PES membranes increased greatly as the heavy metal concentration rose, while the heavy metal concentration moderately affected the permeation flux. The maximum rejection of Pb2+ in a single-salt solution was 99%, 97.5%, and 98% for a feed solution containing 10 mg Pb/L at pH 6, 6.2, and 5.7, for PES1, PES2, and PES3, respectively. The maximum rejection of Cd2+ in single-salt solutions was 78%, 50.2%, and 44% for a feed solution containing 10 mg Cd/L at pH 6.5, 6.2, and 6.5, for PES1, PES2, and PES3, respectively. The analysis of the experimental data using the CFSD, CFSK, and CFFP models showed a good agreement between the theoretical and experimental results. The effective membrane thickness and active skin layer thickness were evaluated using the CFFP model, indicating that the Péclet number is important for determining the mechanism of separation by diffusion.

This study investigated the impact of implanting TiO2-NPs within a membrane to minimize the influence of long-term operation on the membrane characteristics. Four poly vinyle chloride-titanium oxide (PVC-TiO2-NPs) membranes were prepared to create an ultrafiltration membrane (UF) that would effectively treat actual refinery wastewater. The hypothesis of this work was that TiO2-NPs would function as a hydrophilic modification of the PVC membrane and excellent self-cleaning material, which in turn would greatly extend the membrane’s lifetime. The membranes were characterized via Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy dispersive X-ray (EDX), atomic force microscope (AFM), and scanning electron microscope (SEM). The removal efficiency of turbidity, total suspended solid (TSS), oil and grease, heavy metals and chemical oxygen demand (COD) were investigated. Contact angle (CA) reduced by 12.7% and 27.5% on the top and bottom surfaces, respectively. The PVC membrane with TiO2-NPs had larger mean pore size on its surface and more holes with larger size inside the membrane structure. The addition of TiO2-NPs could remarkably enhance the antifouling property of the PVC membrane. The pure water permeability (PWP) of the membrane was enhanced by 95.3% with an increase of TiO2 to 1.5 gm/100gm. The PWP after backwashing was reduced from 22.3% for PVC to 10.1% with 1.5 gm TiO2-NPs. The long-term performance was improved from five days for PVC to 23 d with an increase in TiO2-NPs to 1.5 gm. The improvements of PVC-TiO2-NPs long-term were related to the enhancement of the hydrophilic character of the membrane and increase tensile strength due to the reinforcement effect of TiO2-NPs. These results clearly identify the impact of the TiO2-NPs content on the long-term PVC/TiO2-NPs performance and confirm our hypothesis that it is possible to use TiO2-NPs to effectively enhance the lifetime of membranes during their long-term operation.

In this work, a flat-sheet blend membrane was fabricated by a traditional phase inversion method, using the polymer blends poly phenyl sulfone (PPSU) and polyether sulfone (PES) for the ultrafiltration (UF) application. It was hypothesized that adding PES to the PPSU polymer blend would improve the properties of the PPSU membrane. The effect of the PES concentration on the blend membrane properties was investigated extensively. The characteristics of PPSU-PES blend membranes were investigated using atomic force microscopy (AFM), scanning electron microscopy (SEM), contact angle measure, and contaminant (dye) elimination efficiency. This study showed that PES clearly affected the structural formation of the blended membranes. A considerable increase in the average roughness (about 93%) was observed with the addition of 4% PES, with a higher mean pore size accompanied by a rise in the pores’ density on the surface of the membrane. The addition of up to 4% PES had a significant influence on the hydrophilic character of the PPSU-PES membrane, by lowering the value of the contact angle (CA) (i.e., to 56.9°). The performance of the PPSU-PES composite membranes’ UF performance was systematically investigated, and the membrane pure water permeability (PWP) was enhanced by 25% with the addition of 4% PES. The best separation removal factor achieved in the current investigation for dye (Drupel Black NT) was 96.62% for a PPSU-PES (16:4 wt./wt.%) membrane with a 50% feed dye concentration.

Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community.

Synthetic Aperture Radar (SAR) is a well-established 2D imaging technique employed as a consolidated practice in several oil spill monitoring services. In this scenario, onboard detection undoubtedly represents an interesting solution to reduce the latency of these services, also enabling transmission to the ground segment of alert signals with a notable reduction in the required downlink bandwidth. However, the reduced computational capabilities available onboard require alternative approaches with respect to the standard processing flows. In this work, we propose a feasibility study of oil spill detection applied directly to raw data, which is a solution not sufficiently addressed in the literature that has the advantage of not requiring the execution of the focusing step. The study is concentrated only on the accuracy of detection, while computational cost analysis is not within the scope of this work. More specifically, we propose a complete framework based on the use of a Residual Neural Network (ResNet), including a simple and automatic simulation method for generating the training data set. The final tests with ERS real data demonstrate the feasibility of the proposed approach showing that the trained ResNet correctly detects ships with a Signal-to-Clutter Ratio (SCR) > 10.3 dB.

Removing the hazardous and unstable radioactive isotopes has been considered an arduous task, though they are in minimal concentrations. Cesium-137 (137Cs+) is a primary fission product produced by nuclear processes. Even at low concentrations, such radioactive material is a menacing source of contaminants for the environment. The current study aims to separate 137Cs+ from a real contaminated aqueous solution via an ion exchange mechanism using ammonium molybdophosphate–polyacrylonitrile (AMP-PAN) resin loaded in an extraction chromatographic column that possesses considerable selectivity toward cesium ion (Cs+) due to the specific ion exchange between 137Cs+ and NH4+. Additionally, the proposed interaction mechanism between 137Cs+ with APM-PAN resin has been illustrated in this study. The results disclosed that the optimum efficient removal of 137Cs+ (91.188%) was obtained by the AMP-PAN resin using 2 g·L−1, while the distribution adsorption coefficient (129.359 mL·g−1) was at pH 6. The isothermal adsorption process was testified through the Langmuir and Freundlich models. The estimated maximum adsorption capacity reached 140.81 ± 21.3 mg·g−1 for the Freundlich isotherm adsorption model. Finally, AMP-PAN resin could eliminate 137Cs+ from water effectively through adsorption.

This general review paper presents a condensed view of recent inventions in the Additive Manufacturing (AM) field. It outlines factors affecting the development and commercialization of inventions via research collaboration and discusses breakthroughs in materials and AM technologies and their integration with emerging technologies. The paper explores the impact of AM across various sectors, including the aerospace, automotive, healthcare, food, and construction industries, since the 1970s. It also addresses challenges and future directions, such as hybrid manufacturing and bio-printing, along with socio-economic and environmental implications. This collaborative study provides a concise understanding of the latest inventions in AM, offering valuable insights for researchers, practitioners, and decision makers in diverse industries and institutions.

A flavone, chrysoeriol is synthetized in several plant species. It comes from several natural sources, especially medicinal plants. The identification and isolation of this compound has been carried out and verified by several research teams using different spectral methods. It seems that the concentration of this molecule is variable and fluctuating depending on the source, the part extracted, the region, and the methods of extraction and characterization. The aim of this paper is to highlight the in vitro and in vivo pharmacological properties of chrysoeriol and to provide insight into its pharmacokinetics. Anticancer, anti-inflammatory, antibacterial, antifungal, anti-osteoporosis, anti-insecticide, and neuroprotective actions have been shown in a number of studies on this chemical. Different mechanisms in theses pharmacological effects include subcellular, cellular, and molecular targets. In vivo pharmacokinetic analysis has proved the good stability of this molecule, showing its promising potential to prevent or treat diseases including cancer, diabetes, inflammation, osteoporosis, Parkinson’s disease, and cardiovascular diseases.

Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though much research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from developing and working with a city-scale simulator of a district heating grid located in Luleå, Sweden. The grid model in the case study is a physics based white-box model, while consumer models are either data-driven black-box or gray-box models. The control system and operator models replicate the manual and automatic operation of the combined heat and power plant. Using the functional mock-up interface standard, a co-simulation environment integrates all the models. Further, the validation of the simulator is discussed. Lessons learned from the project are presented along with future research directions, corresponding to identified gaps and challenges.

Metal additive manufacturing (AM) has gained much attention in recent years due to its advantages including geometric freedom and design complexity, appropriate for a wide range of potential industrial applications. However, conventional metal AM methods have high-cost barriers due to the initial cost of the capital equipment, support, and maintenance, etc. This study presents a low-cost metal material extrusion technology as a prospective alternative to the production of metallic parts in additive manufacturing. The filaments used consist of copper, bronze, stainless steel, high carbon iron, and aluminum powders in a polylactic acid matrix. Using the proposed fabrication technology, test specimens were built by extruding metal/polymer composite filaments, which were then sintered in an open-air furnace to produce solid metallic parts. In this research, the mechanical and thermal properties of the built parts are examined using tensile tests, thermogravimetric, thermomechanical and microstructural analysis.

Today, the current trends of manufacturing are towards the adaptation and implementation of smart manufacturing, which is a new initiative to turn the traditional factories into profitable innovation facilities. However, the concept and technologies are still in a state of infancy, since many manufacturers around the world are not fully knowledgeable about the benefits of smart manufacturing compared to their current practices. This article reviews several aspects of smart manufacturing and introduces its advantages in terms of energy-saving and production efficiency. This article also points out that some areas need further research so that smart manufacturing can be shaped better.

In this work, a novel hybrid SERS platform incorporating hybrid core-shell (HyCoS) AuPd nanoparticles (NPs) and MoS 2 nanoplatelets has been successfully demonstrated for strong surface-enhanced Raman spectroscopy (SERS) enhancement of Rhodamine 6G (R6G). A significantly improved SERS signal of R6G is observed on the hybrid SERS platform by adapting both electromagnetic mechanism (EM) and chemical mechanism (CM) in a single platform. The EM enhancement originates from the unique plasmonic HyCoS AuPd NP template fabricated by the modified droplet epitaxy, which exhibits strong plasmon excitation of hotspots at the nanogaps of metallic NPs and abundant generation of electric fields by localized surface plasmon resonance (LSPR). Superior LSPR results from the coupling of distinctive AuPd core-shell NP and high-density background Au NPs. The CM enhancement is associated with the charge transfer from the MoS 2 nanoplatelets to the R6G. The direct contact via mixing approach with optimal mixing ratio can effectively facilitate the charges transfer to the HOMO and LUMO of R6G, leading to the orders of Raman signal amplification. The enhancement factor (EF) for the proposed hybrid platform reaches ~10 10 for R6G on the hybrid SERS platform.

Translation of novel inhalable therapies for respiratory diseases is hampered due to the lack of in vitro cell models that reflect the complexity of native tissue, resulting in many novel drugs and formulations failing to progress beyond preclinical assessments. The development of physiologically-representative tracheobronchial tissue analogues has the potential to improve the translation of new treatments by more accurately reflecting in vivo respiratory pharmacological and toxicological responses. Herein, advanced tissue-engineered collagen hyaluronic acid bilayered scaffolds (CHyA-B) previously developed within our group were used to evaluate bacterial and drug-induced toxicity and inflammation for the first time. Calu-3 bronchial epithelial cells and Wi38 lung fibroblasts were grown on either CHyA-B scaffolds (3D) or Transwell ® inserts (2D) under air liquid interface (ALI) conditions. Toxicological and inflammatory responses from epithelial monocultures and co-cultures grown in 2D or 3D were compared, using lipopolysaccharide (LPS) and bleomycin challenges to induce bacterial and drug responses in vitro. The 3D in vitro model exhibited significant epithelial barrier formation that was maintained upon introduction of co-culture conditions. Barrier integrity showed differential recovery in CHyA-B and Transwell ® epithelial cultures. Basolateral secretion of pro-inflammatory cytokines to bacterial challenge was found to be higher from cells grown in 3D compared to 2D. In addition, higher cytotoxicity and increased basolateral levels of cytokines were detected when epithelial cultures grown in 3D were challenged with bleomycin. CHyA-B scaffolds support the growth and differentiation of bronchial epithelial cells in a 3D co-culture model with different transepithelial resistance in comparison to the same co-cultures grown on Transwell ® inserts. Epithelial cultures in an extracellular matrix like environment show distinct responses in cytokine release and metabolic activity compared to 2D polarised models, which better mimic in vivo response to toxic and inflammatory stimuli offering an innovative in vitro platform for respiratory drug development.

Foodborne microbial diseases have a significant impact on public health, leading to millions of human illnesses each year worldwide. Pork is one of the most consumed meat in Europe but may also be a major source of pathogens introduced all along the farm-to-fork chain. Several quantitative microbial risk assessment (QMRA) have been developed to assess human health risks associated with pork consumption and to evaluate the efficiency of different risk reduction strategies. The present critical analysis aims to review pork QMRA. An exhaustive search was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. It resulted in identification of a collection of 2489 papers including 42 on QMRA, after screening. Among them, a total of 29 studies focused on Salmonella spp. with clear concern on impacts at the slaughterhouse, modeling the spreading of contaminations and growth at critical stages along with potential reductions. Along with strict compliance with good hygiene practices, several potential risk mitigation pathways were highlighted for each slaughterhouse step. The slaughterhouse has a key role to play to ensure food safety of pork-based products but consideration of the whole farm-to-fork chain is necessary to enable better control of bacteria. This review provides an analysis of pork meat QMRA, to facilitate their reuse, and identify gaps to guide future research activities.

Food of animal origin, especially meat products, represent the main vehicle of foodborne pathogens and so are implicated in foodborne outbreaks. Poultry meat is a widely consumed food in various forms, but it is also a reservoir of thermotolerant Campylobacter and Salmonella bacterial species. To assess human health risks associated with pathogenic bacteria in poultry meat, the use of quantitative microbial risk assessment (QMRA) has increased over the years as it is recognized to address complex food safety issues and is recommended by health authorities. The present project reviewed poultry meat QMRA, identified key steps of the farm-to-fork chain with significant impacts on food safety, highlighted current knowledge gaps, and provided risk mitigation advices. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-based systematic analysis was carried out and enabled the collection of 4056 studies including 42 QMRA kept for analysis after screening. The latter emphasized Campylobacter spp. and Salmonella spp. contaminations during the consumer stage as the main concern. The role of consumer handling on cross-contamination and undercooking events were of major concern. Thus, proper hygiene and safety practices by consumers have been suggested as the main intervention and would need to be followed with regular surveys to assess behavior changes and reduce knowledge gaps.

The purpose of this research was to provide a "systematic literature review" of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches-to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.