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Advancing sustainable CO2 mitigation: Experimental and computational analysis of thermal carbon chitosan sorbent for automotive exhaust capture
No full textThis study investigated the efficiency of thermal carbon chitosan (TCCS) sorbent for CO2 capture from vehicle exhaust emissions within a designed adsorption system. TCCS was synthesized and meticulously characterized using a series of analytical techniques, including Brunauer-Emmett-Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA), Energy Dispersive X-ray Spectroscopy (EDX), and Differential Scanning Calorimetry (DSC). The TCCS adsorbent showed high thermal stability and a heating value (HHV) of 23.5 MJ/kg. Adsorption isotherm study demonstrated that the maximum capacity of CO2 adsorption is 0.084 kg.CO2/kg.TCCS, as well as confirmation of the exothermic nature of the process with an enthalpy change (ΔH) of −26.42 kJ/mol. Kinetics study indicated that the adsorption mechanism was physical in nature, characterized by an activation energy (ED) of 4.27 kJ/mol, which is lower than the threshold of 8 kJ/mol. The experimental breakthrough curve revealed a breakpoint time (tb) of 1280 s, a saturation time (ts) of 2300 s and illustrated that about 70 % of the adsorption bed (Hb) was used during the CO2 adsorption process. To further validate the experimental results, a Computational Fluid Dynamics (CFD) simulation was conducted, revealing a strong correlation with the experimental data. The low error values between the experimental and CFD predicted results underscore the reliability of the TCCS-based adsorption system for effective CO2 capture. This research contributes valuable insight into the potential of TCCS as a sustainable adsorbent for mitigating CO2emissions from automotive sources
Antimicrobial Susceptibility and Molecular Characterization of Corynebacterium pseudotuberculosis Isolates Recovered From Caseous Lymphadenitis in Sheep
No full textCorynebacterium pseudotuberculosis is a well-recognized etiological agent responsible for caseous lymphadenitis (CLA) and ulcerative lymphangitis (UL) in small and large ruminants, respectively. This pathogen is frequently associated with poor therapeutic outcomes in animals. In the present study, 30 bacterial isolates, recovered from 300 lymph node and pus samples (10%), were identified as Corynebacterium spp. Molecular confirmation using 16S rRNA and pld gene PCR verified 11 of these isolates as C. pseudotuberculosis, while one isolate was identified as C. jeikeium, a first record from sheep in Egypt. The isolates were subjected to antimicrobial susceptibility and genotyping using enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR). Cluster analysis classified the isolates into two major clusters: Cluster 1 (C1) represents C. jeikeium, and cluster 2 (C2) represents C. pseudotuberculosis. Cluster 2 was further subdivided into four subclusters (2A–2D), reflecting epidemiological linkages among livestock in Giza and Cairo governorates. Additionally, the antimicrobial potential of silver nanoparticles (AgNPs) and silver nitazoxanide-loaded nanoparticles (Ag-NPs/NTZ) was assessed against both C. pseudotuberculosis and C. jeikeium. The results revealed extensive multidrug resistance (MDR) among C. pseudotuberculosis and C. jeikeium isolates to several antibiotic classes. However, all C. pseudotuberculosis strains demonstrated 100% sensitivity to vancomycin, amoxicillin–clavulanic acid, gentamicin, and amikacin. These findings support the recommendation of these agents for effective control of CLA. Furthermore, silver nitazoxanide nanoparticles showed a promising in vitro effect against both Corynebacterium spp. recovered in this study and may represent a potential and novel adjunctive approach for managing C. pseudotuberculosis infections
Investigating vector-like leptons decaying into an electron and missing transverse energy in e+ e− collisions with s = 500 GeV at the ILC
No full textThis analysis focuses on probing the lepton portal dark matter using Monte Carlo simulated samples from electron-positron collisions at the International Linear Collider (ILC) of 500 GeV center of mass energy with an integrated luminosity of 1000 fb−1. The study examines a benchmark scenario where the dark matter is a scalar particle produced as a daughter particle of the vector-like lepton. The signal topology consists of missing transverse energy and dilepton. If no new physics is discovered, the study sets 95% confidence level exclusion limits on the mass of vector-like leptons
Revolutionizing Aripiprazole Delivery: Improving Solubility and Permeation Via Solid dispersion and In-situ Intranasal Gelling Systems
No full textAripiprazole (ARI) is a class IV drug of poor solubility and permeability. This study focused on improving the ARI solubility and permeability via solid dispersion technique (SDs), which was further loaded through intranasal in-situ gel to target the brain directly. ARI- SDs were prepared by Solvent evaporation technique. SD9 was the optimum solid dispersion that enhanced the solubility of ARI by 6.5 folds and selected for preparation of in-situ intranasal gel using Chitosan and HPMC K15M. A 33 factorial design was utilized to optimize the percentage of drug released after 24 h, gelation time and gelation temperature. The gel formulations were investigated for pH, viscosity, drug content%, gelation time and temperature, gel strength, gel spreadability, mucoadhesive strength and percentage of drug released after 24 h. The optimized formula has achieved 93.4 ± 0.74 % release of ARI after 24 h, a gelation time of 76 s and gelation temperature of 33.2OC. The optimized formula was then investigated for ex-vivo permeation, nasal ciliotoxicity and showed a 7.5 folds permeability enhancement compared to ARI suspension without any sign of intranasal mucosal damage. In conclusion, the prepared in-situ gel of ARI would be a good alternative to oral delivery for schizophrenic patients
Enhancing Polyacrylonitrile Nanofibers Antiviral Activity Using Greenly Synthesized Silver Nanoparticles
No full textDeveloping efficient antiviral protectives is a new approach against respiratory emerging viruses. This study aims to synthesize silver nanoparticles (Ag NPs) via a green technique using crocin to provide a virucidal effect and to enhance the protection of polyacrylonitrile (PAN) nanofibrous face masks or respirators against viruses. The influence of formulation and process variables on the particle size (PS) of Ag NPs was studied using d-optimal response surface design. The selected NPs were loaded into PAN nanofibers (NFs). MTT colorimetric assay was performed to determine the safety of the prepared NPs and NFs on Vero cells. Further, an immunofluorescent assay was performed to determine the composite\u27s ability to inhibit the ACE2-SARS-CoV-2 spike protein interaction and prevent viral infection. The selected NPs possessed a small PS of 23.21 ± 0.86 nm, a PDI of 0.23 ± 0.019, and a ZP of –21.8 ± 1.82 mV. The optimum NF composite was fabricated with a PAN concentration of 8% w/v loaded with 0.25% w/w Ag NPs, with a feeding rate of 0.7 mL/h and an applied voltage of 23.5 kV. The resultant NFs displayed an acceptable morphology and a mean diameter of 378.88 ± 91.12 nm. In vitro cytotoxicity studies on Vero cells revealed the biocompatibility of crocin and Ag NPs. Moreover, Ag-PAN NFs were proven biologically safe. The immunofluorescent assay showed that Ag-PAN NFs demonstrated the least IC50 value of 10.99 µg/mL, indicating their potent effect on inhibiting SARS-CoV-2 infection. Ag-PAN NFs are a promising safe antiviral composite that has the potential to be used in face masks
Effects of Grain Orientation and Confinement on Dynamic Compressive Behavior of Highly Oriented MAX Phase Ta2AlC
No full textMAX phases are unique ternary carbides and nitrides that bridge the gap between metals and ceramics. Specifically, Ta2AlC, the MAX phase with the highest bulk modulus, offers a unique combination of metallic and ceramic properties, making it particularly well-suited for extreme applications. Fully dense, coarse-grained, and textured Ta2AlC was fabricated in bulk, achieving a global grain orientation along the c-axis with an orientation factor of 0.63. The uniaxial quasi-static and dynamic, and biaxial dynamic response was evaluated parallel ( role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 14.4px; font-size-adjust: none; word-spacing: normal; overflow-wrap: normal; text-wrap-mode: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative; \u3e) and perpendicular (⊥) to the c-axis. The average dynamic strength in ⊥ c-axis orientation was 824±39 MPa, 19% higher than the uniaxial quasi-static compressive strength of 690±55 MPa in the same orientation. The biaxial dynamic strength in this orientation, when applying a moderate 80 MPa planar confinement along basal planes ( role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 14.4px; font-size-adjust: none; word-spacing: normal; overflow-wrap: normal; text-wrap-mode: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative; \u3e c-axis) had the highest average compressive strength of 1097±72 MPa. Scanning electron microscopy fractography indicates a consistent fracture mechanism within the grain orientation across different strain rates under uniaxial loading. During biaxial loading, crack propagation was delayed, with qualitative indications of shear band formation. Concurrently, both the quantity and mode of kink band formation appeared to increase, leading to an overall enhancement in final strength. The link between macroscopic failure behavior captured from ultra-high-speed imaging and microscopic fractography is discussed
Enhancing topical delivery of N-acetylcysteine and collagen via a novel electrospun collagen/PMMA nanofibrous mats as facial mask development: Nanofibers optimization and In vitro experiments
No full textThis study endeavours to craft a potent topical facial mask using electrospun nanofibers. The nanofibers were carefully fabricated based on loading of N-acetylcysteine (NAC) onto electrospun marine collagen (MC), and poly (methyl methacrylate) (PMMA) nanofibers. Through precise adjustments of electrospinning parameters, a uniform array of nanoscale fibres with a smooth surface was achieved. The morphology and chemical structure configuration of nanofibers (NFs) were examined using SEM, FTIR, and XRD analyses. Furthermore, the cytotoxic impact was assessed on a human normal fibroblast cell line (BJ1), while protein levels were gauged using the Bradford dye-binding assay (Coomassie assay). Also, the antioxidant potential of various NFs scaffolds was evaluated through superoxide dismutase activity (SOD), catalase activity (CAT), and free radical scavenging assays. Controlled release of NAC from nanofibers was observed. Notably, PMMA/MC/NAC NFs exhibited notable antioxidant and strong antagonistic antimicrobial effects. After an 8-h treatment period, Staphylococcus epidermidis showed the highest percentage of biofilm reduction (96.46 ± 2.09 %) caused by this formula. Moreover, this formula effectively decreased the biofilm production of Candida glabrata (97.18 ± 0.48 %) and Klebsiella pneumoniae (96.38 ± 2.47 %) after a 12-h exposure period. These compelling findings put PMMA/MC/ NAC NFs as promising candidate for facial masks and skincare formulations
Oral cancer awareness among dentists: what is missing? A cross-sectional study
No full textIntroduction
Oral cancer (OC) is one of the major global health problems with a high incidence rate in developing countries. Early detection can improve the prognosis and survival rate of the disease. Aim
The current study evaluates dentists’ general awareness, knowledge, attitude, and practice regarding oral cancer. Methods
In the current cross-sectional study, a self-reported questionnaire was distributed to a sample of dentists in Egypt. A total of 700 dentists participated. The questionnaire included 27 questions on oral cancer knowledge, opinions, attitudes, and practices. Results
The highest awareness of risk factors concentrated around tobacco and alcohol consumption, and the most acknowledged clinical presentations were non-healing ulcers, red lesions, white lesions, and induration. The tongue was considered a high-risk site by 69% of participants, followed by the floor of the mouth and the buccal/lip mucosa. Only 37% of the participants carried out proper clinical screening for OC, while only 31% carried out routine lymph node examinations. Sixty-six percent of participants considered themselves incompetent regarding detection of OC. Ninety-two percent of participants acknowledged the important role of dentists in the early detection of oral cancer, and 99% of them thought that oral cancer awareness campaigns are needed and would be effective. Awareness was significantly associated with years of practice. Conclusion
Awareness regarding OC among the Egyptian dentists participating in the current survey showed definitive defects. Hence, efforts to raise awareness of OC among dental practitioners are an important factor in improving/early detection of OC, with the resultant increase in survival rate and decrease in morbidity. This can be reached only through more solid undergraduate syllabi and training as well as workshops and campaigns
Enhanced thermal stability and mechanical properties of polypropylene matrices reinforced with SiO2 nanoparticles
No full textThe current study focuses on incorporating SiO2 nano-fillers into a polypropylene matrix to enhance and regulate the coefficient of thermal expansion (CTE) while simultaneously improving physical, mechanical, and thermal properties. This approach involves adding small quantities of SiO2 filler to occupy vacant spaces between polymer chains, reducing the CTE of the polymer without affecting its structure. Silicon dioxide nanoparticles (SiO2-NPs) were added to a polypropylene (PP) matrix at various concentrations (1%wt, 2%wt, 3%wt, 4%wt, 5%wt, 6%wt). The prepared samples underwent characterization through techniques including x-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), and differential scanning calorimetry (DSC), along with analysis of the coefficient of thermal expansion, Young’s modulus, and tensile strength. Observations revealed that the inclusion of small quantities of filler significantly enhances the mechanical and thermal properties of PP, particularly at 3%wt of SiO2, which exhibited the lowest CTE value. This methodology can be regarded as an effective approach for controlling the CTE value of polymers, potentially opening avenues for various industrial applications of these materials
Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana, Egypt
No full textDownload PDF Download PDF Article Open access Published: 04 May 2025 Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana, Egypt Ali Taha, Nathalie Nazih & Peter Makeen
Scientific Reports volume 15, Article number: 15599 (2025) Cite this article
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Wind energy has become a key answer to the world’s energy problems, providing a clean and sustainable option instead of relying on fossil fuels. Enhancing wind energy systems and energy management is essential through efficient wind speed prediction. However, the complex nature of wind speed data contains significant challenges with existing forecasting models for long-term nonlinear forecasting accuracy, and this causes a lack of wind energy predictions, which may cause false distributions of energy. This study proposes a multi-step methodology that integrates Variational Mode Decomposition (VMD) with advanced machine learning like Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbor (KNN), and transformer-based model (Informer) to improve long-term wind speed forecasting. The approach involves data collection from the NASA Power project, which consists of 35k samples of wind speed data, with performance evaluated on R-squared (R²) score and error metrics. The proposed approach demonstrated state-of-the-art performance, with LightGBM achieving the highest R² of 98% and the lowest error metrics. XGBoost and KNN performed slightly lower in R², achieving 97% score. Despite the high performance of the Informer model, it demonstrated the lowest in scores with a 78% R² score. The study’s novelty lies in highlighting the effectiveness and efficiency of VMD in addressing the complexities of wind speed data and underscores the potential of combining decomposition techniques with advanced machine learning models for accurate wind speed forecasting