Optimizing Ammonia Detection with a Polyaniline-Magnesia Nano Composite.

Polyaniline-magnesia (PANI/MgO) composites with a fibrous nanostructure were synthesized via in situ oxidative polymerization, enabling uniform MgO integration into the polyaniline matrix. These composites were characterized using FTIR spectroscopy to analyze intermolecular bonding, XRD to assess crystallographic structure and phase purity, and SEM to examine surface morphology and topological features. The resulting PANI/MgO nanofibers were utilized to develop ammonia (NH3) gas-sensing probes with evaluations conducted at room temperature. The study addresses the critical challenge of achieving high sensitivity and selectivity in ammonia detection at low concentrations, which is a problem that persists in many existing sensor technologies. The nanofibers demonstrated high selectivity and optimal sensitivity for ammonia detection, which was attributed to the synergistic effects between the polyaniline and MgO that enhance gas adsorption. Furthermore, the study revealed that the MgO content critically influences both the morphology and the sensing performance, with higher MgO concentrations improving sensor response. This work underscores the potential of PANI/MgO composites as efficient and selective ammonia sensors, highlighting the importance of MgO content in optimizing material properties for gas-sensing applications.

Exergetic investigation of the influence of injector location in HCCI engines utilizing DEE as pilot fuel and biogas as primary fuel.

To address the global demand for sustainable energy, integrating biogas into internal combustion engines is becoming more important. Homogeneous Charge Compression Ignition (HCCI) engines, known for high efficiency and low emissions, offer a promising solution. This study investigates the optimal injector location for using biogas in HCCI engines, with diethyl ether (DEE) as the pilot fuel, evaluating three positions: (i) at the port, (ii) 6 cm away (Manifold 1), and (iii) 10 cm away (Manifold 2). Through experiments and simulations, the impact of injector location on engine performance is analyzed across various parameters, including methane fractions, engine loads, and exhaust gas compositions. Results show that port injection achieves the highest first law and exergy efficiencies but increases emissions of hydrocarbons (HC), carbon monoxide (CO), and smoke. At 15 Nm load, Manifold 1 shows a 27.34 % reduction in exergy efficiency compared to port injection, while Manifold 2 exhibits an 18.49 % decrease at higher loads. Despite lower efficiencies, Manifold 1 effectively reduces harmful emissions. The study also considers exergo-economic and sustainability aspects, highlighting that while port injection is optimal for efficiency, Manifold 1 excels in minimizing HC and CO emissions, with a 50 % reduction in HC and 71.43 % reduction in CO emissions at 15 Nm load compared to port injection. Manifold 2 achieves the lowest smoke emissions across all loads. This investigation provides crucial insights into optimizing HCCI engines for biogas utilization, emphasizing injector location, fuel composition, and operating parameters to enhance performance and reduce environmental impact.

Innovations in metal-organic frameworks (MOFs): Pioneering adsorption approaches for persistent organic pollutant (POP) removal.

Adsorption is a promising way to remove persistent organic pollutants (POPs), a major environmental issue. With their high porosity and vast surface areas, MOFs are suited for POP removal due to their excellent adsorption capabilities. This review addresses the intricate principles of MOF-mediated adsorption and helps to future attempts to mitigate organic water pollution. This review examines the complicated concepts of MOF-mediated adsorption, including MOF synthesis methodologies, adsorption mechanisms, and material tunability and adaptability. MOFs' ability to adsorb POPs via electrostatic forces, acid-base interactions, hydrogen bonds, and pi-pi interactions is elaborated. This review demonstrates its versatility in eliminating many types of contaminants. Functionalizing, adding metal nanoparticles, or changing MOFs after they are created can improve their performance and remove contaminants. This paper also discusses MOF-based pollutant removal issues and future prospects, including adsorption capacity, selectivity, scale-up for practical application, stability, and recovery. These obstacles can be overcome by rationally designing MOFs, developing composite materials, and improving material production and characterization. Overall, MOF technology research and innovation hold considerable promise for environmental pollution solutions and sustainable remediation. Desorption and regeneration in MOFs are also included in the review, along with methods for improving pollutant removal efficiency and sustainability. Case studies of effective MOF regeneration and scaling up for practical deployment are discussed, along with future ideas for addressing these hurdles.

Biochemical characterization of immobilized recombinant subtilisin and synthesis and functional characterization of recombinant subtilisin capped silver and zinc oxide nanoparticles.

This pioneering research explores the transformative potential of recombinant subtilisin, emphasizing its strategic immobilization and nanoparticle synthesis to elevate both stability and therapeutic efficacy. Achieving an impressive 95.25 % immobilization yield with 3 % alginate composed of sodium along with 0.2 M CaCl2 indicates heightened pH levels and thermal resistance, with optimal action around pH 10 as well as 80 °C temperature. Notably, the Ca-alginate-immobilized subtilisin exhibits exceptional storage longevity and recyclability, affirming its practical viability. Comprehensive analyses of the recombinant subtilisin under diverse conditions underscore its adaptability, reflected in kinetic enhancements with increased Vmax (10.7 ± 15 × 103 U/mg) and decreased Km (0.19 ± 0.3 mM) values post-immobilization using N-Suc-F-A-A-F-pNA. UV-visible spectroscopy confirms the successful capping of nanoparticles made of Ag and ZnO by recombinant subtilisin, imparting profound antibacterial efficacy against diverse organisms and compelling antioxidant properties. Cytotoxicity was detected against the MCF-7 breast cancer line of cells, exhibiting IC50 concentrations at 8.87 as well as 14.52 µg/mL of AgNP as well as ZnONP, correspondingly, indicating promising anticancer potential. Rigorous characterization, including FTIR, SEM-EDS, TGA and AFM robustly validate the properties of the capped nanoparticles. Beyond therapeutic implications, the investigation explores industrial applications, revealing the versatility of recombinant subtilisin in dehairing, blood clot dissolution, biosurfactant activity, and blood stain removal. In summary, this research unfolds the exceptional promise of recombinant subtilisin and its nanoparticles, presenting compelling opportunities for diverse therapeutic applications in medicine. These findings contribute substantively to biotechnology and healthcare and stimulate avenues for further innovation and exploration.

Exploring the Potential of Promising Sensor Technologies for Concrete Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for maintaining concrete infrastructure. The data collected by these sensors are processed and analyzed using various analysis tools under different loadings and exposure to external conditions. Sensor-based investigation on concrete has been carried out for technologies used for designing structural health monitoring sensors. A Sensor-Infused Structural Analysis such as interfacial bond-slip model, corroded steel bar, fiber-optic sensors, carbon black and polypropylene fiber, concrete cracks, concrete carbonation, strain transfer model, and vibrational-based monitor. The compressive strength (CS) and split tensile strength (STS) values of the analyzed material fall within a range from 26 to 36 MPa and from 2 to 3 MPa, respectively. The material being studied has a range of flexural strength (FS) and density values that fall between 4.5 and 7 MPa and between 2250 and 2550 kg/m3. The average squared difference between the predicted and actual compressive strength values was found to be 4.405. With cement ratios of 0.3, 0.4, and 0.5, the shear strength value ranged from 4.4 to 5.6 MPa. The maximum shear strength was observed for a water-cement ratio of 0.4, with 5.5 MPa, followed by a water-cement ratio of 0.3, with 5 MPa. Optimizing the water-cement ratio achieves robust concrete (at 0.50), while a lower ratio may hinder strength (at 0.30). PZT sensors and stress-wave measurements aid in the precise structural monitoring, enhanced by steel fibers and carbon black, for improved sensitivity and mechanical properties. These findings incorporate a wide range of applications, including crack detection; strain and deformation analysis; and monitoring of temperature, moisture, and corrosion. This review pioneers sensor technology for concrete monitoring (Goal 9), urban safety (Goal 11), climate resilience (Goal 13), coastal preservation (Goal 14), and habitat protection (Goal 15) of the United Nations' Sustainable Development Goals.

Unleashing the electrochemical performance of zirconia nanoparticles on valve-regulated lead acid battery.

The electrochemical act of valve-regulated lead acid batteries can be enhanced by conductive materials like metal oxides. This work aims to examine the preparation and influence of zirconia on poly(vinyl alcohol) based gel valve-regulated lead acid battery. Characterizations like Fourier transform infrared spectroscopy, ionic conductivity, water retention study, cyclic voltammetry, electrochemical impedance spectroscopy and galvanostatic charge-discharge techniques were done. The optimized gel system exhibited a discharge capacity of 198.45 µAh cm-2 at the current density of 0.6 mA cm-2. The battery cell with an optimized gel matrix displayed a maximum discharge capacity of 22.5 µAh at a current of 20 µA. After 500 continuous cycles, the battery attained a discharge capacity retention of 91 %. The presence of zirconia will increase the electrochemical performance of gel valve-regulated lead acid batteries.

Investigation on effect of cerium oxide additive in waste plastic oil fueled CI engine.

In this study, the effects of incorporating cerium oxide into diesel and WPO blends were investigated to determine the potential of the blend as a fuel additive. The study aimed to assess engine-performance, emission, and combustion properties of the blend. The experiments utilized a single-cylinder diesel engine, and researchers prepared two different blends of WPO with 25% WPO in diesel and 50% WPO in diesel. Cerium oxide was added to these blends at concentrations of 25 ppm and 50 ppm using an ultrasonicator. The results demonstrated that increasing cerium oxide content in the blend (50 ppm) led to reduced CO, HC, and NOx emissions at higher loads. For instance, B50 + 50 ppm exhibited lower CO and NOx emissions, while B25 + 50 ppm demonstrated lower HC and smoke emissions. Furthermore, raising the CeO2 content from 25 ppm to 50 ppm resulted in a 3% increase in brake thermal efficiency. Moreover, cerium oxide positively impacted combustion and performance properties of the blends. Among the tested blends, the B50 + 50 ppm combination showcased the highest brake thermal efficiency, optimal air-fuel ratio, and the lowest specific fuel consumption. In conclusion, employing cerium oxide as a fuel additive in diesel-WPO blends offers a promising approach for realizing a sustainable and environmentally friendly future.

A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

Experimental Analysis for the Performance Assessment and Characteristics of Enhanced Magnesium Composites Reinforced with Nano-Sized Silicon Carbide Developed Using Powder Metallurgy.

Magnesium, which is lightweight and abundant by nature, was widely used in the 19th century to make parts for automobiles and airplanes. Due to their superior strength-to-weight ratios, magnesium alloys were favored for engineering applications over unadulterated magnesium. These alloys result from the combination of magnesium with various metals, including aluminum (Al), titanium (Ti), zinc (Zn), manganese (Mn), calcium (Ca), lithium (Li), and zirconium (Zr). In this study, an alloy of magnesium was created using the powder metallurgy (PM) technique, and its optimal performance was determined through the Taguchi-Gray (TG) analysis method. To enhance the alloy's mechanical properties, diverse weight fractions of silicon carbide (SiC) were introduced. The study primarily focused on the Mg-Zn-Cu-Mn alloy, achieving the optimal composition of Mg-3Zn-1Cu-0.7Mn (ZC-31). Subsequently, composites of ZC-31/SiC were produced via PM and the hot extrusion (HE) process, followed by the assessment of the mechanical properties under various strain rates. The use of silicon carbide (SiC) resulted in enhanced composite densities as a consequence of the increased density exhibited by SiC particles. In addition, the high-energy postsintering approach resulted in a decrease in porosity levels. By integrating silicon carbide (SiC) to boost the microhardness, as well as the ultimate compressive and tensile strength of the composite material, we can observe significant improvements in these mechanical properties. The experimental findings also demonstrated that an augmentation in the weight fraction of SiC and the strain rate led to enhanced ductility and a shift toward a more transcrystalline fracture behavior inside the composite material.

Advances in identifying and managing emerging contaminants in aquatic ecosystems: Analytical approaches, toxicity assessment, transformation pathways, environmental fate, and remediation strategies.

Emerging contaminants (ECs) are increasingly recognized as threats to human health and ecosystems. This review evaluates advanced analytical methods, particularly mass spectrometry, for detecting ECs and understanding their toxicity, transformation pathways, and environmental distribution. Our findings underscore the reliability of current techniques and the potential of upcoming methods. The adverse effects of ECs on aquatic life necessitate both in vitro and in vivo toxicity assessments. Evaluating the distribution and degradation of ECs reveals that they undergo physical, chemical, and biological transformations. Remediation strategies such as advanced oxidation, adsorption, and membrane bioreactors effectively treat EC-contaminated waters, with combinations of these techniques showing the highest efficacy. To minimize the impact of ECs, a proactive approach involving monitoring, regulations, and public education is vital. Future research should prioritize the refining of detection methods and formulation of robust policies for EC management.

Exploring the Impact of Al2O3 Additives in Gasoline on HCCI-DI Engine Performance: An Experimental, Neural Network, and Regression Analysis Approach.

This study delves into the influence of incorporating alumina (Al2O3) nanoparticles with waste cooking oil (WCO) biofuels in a gasoline engine that employs premixed fuel. During the suction phase, gasoline blends with atmospheric air homogeneously at the location of the inlet manifold. The biodiesel, enhanced with Al2O3 nanoparticles and derived from WCO, is subsequently directly infused into the combustion chamber at 23° before the top dead center. The results highlight that when gasoline operates in the homogeneous charge compression ignition with direct injection (HCCI-DI) mode, there is a notable enhancement in thermal efficiency by 4.23% in comparison to standard diesel combustion. Incorporating the Al2O3 nanoparticles with the WCO biodiesel contributes to an extra rise of 6.76% in thermal efficiency. Additionally, HCCI-DI combustion paves the way for a reduction in nitrogen oxides and smoke emissions, whereas biodiesel laced with Al2O3 nanoparticles notably reduces hydrocarbon and carbon monoxide discharges. Predictive tools such as artificial neural networks and regression modeling were employed to forecast engine performance variables.

Mechanical and Wear Studies of Boron Nitride-Reinforced Polymer Composites Developed via 3D Printing Technology.

In the realm of 3D printing, polymers serve as fundamental materials offering versatility to cater to a diverse array of final product properties and tailored to the specific needs of the creator. Polymers, as the building blocks of 3D printing, inherently possess certain mechanical and wear properties that may fall short of ideal. To address this limitation, the practice of reinforcing polymer matrices with suitable materials has become a common approach. One such reinforcement material is boron nitride (BN), lauded for its remarkable mechanical attributes. The integration of BN as a reinforcing element has yielded substantial enhancements in the properties of polylactic acid (PLA). The central objective of this research endeavor is the development of polymer composites based on PLA and fortified with boron nitride. This study undertakes the comprehensive exploration of the compatibility and synergy between BN and PLA with a keen focus on examining their resultant properties. To facilitate this, various percentages of boron nitride were incorporated into the PLA matrix, specifically at 5% and 10% by weight. The compounding process involved the blending of PLA and boron nitride followed by the creation of composite filaments measuring 1.75 mm in diameter and optimized for 3D printing. Subsequently, test specimens were meticulously fabricated in adherence with ASTM standards to evaluate the ultimate tensile strength, dimensional accuracy, wear characteristics, and surface roughness. The findings from these assessments were systematically compared to the wear properties and mechanical behavior of PLA composites reinforced with boron nitride and the unreinforced PLA material. This study serves as a foundational resource that offers insights into the feasibility and methodologies of incorporating boron nitride into PLA matrices, paving the way for enhanced polymer composite development.

Effect of silicon carbide on kerf convergence and irregularity of the surface during abrasive water jet machining of fiber-metal hybrid composites.

The traditional way to machine hybrid composites is hard because they tend to break, have a high retraction, have a high service temperature, and have an uneven surface irregularity. For high-strength fiber/metal composite constructions, alternative machining methods have drawn interest as a solution to these problems. Current research focuses on enhancing the Abrasive Water Jet Machining process by optimizing its variables using a composite material of epoxy reinforced with silicon carbide, stainless steel wire mesh, and Kevlar. The variables assessed are the Nozzle-to-substrate gap (S), the Abrasive discharge molding and different percentages of silicon carbide (SiC) filler (0%, 3%, and 6% by weight), three different types of hybrid laminates (H1, H2, and H3) were produced. The response surface method (RSM) was utilized in this learning, specifically on a central composite design, to calculate and optimize machining variables based on the Kerf convergence ratio (Kt) and Surface irregularity (Ra) as responses. According to the results, the traverse feed velocity, Abrasive discharge proportion, and Nozzle-to-substrate gap are the critical factors in determining Surface irregularity and Kerf convergence width (H1 laminate) for a fiber/metal laminate with 0%, 3% and 6% weight fraction. In the case of a 3% weight fraction H2 laminate, the traverse feed velocity was identified as the primary factor affecting the Kerf convergence ratio. In contrast, traverse feed velocity and Nozzle-to-substrate gap had the most significant influence on Surface irregularity. The findings also indicated that S, followed by Abrasive discharge proportion and traverse feed velocity, are the variables that have the most significant influence when cutting 6 wt% SiC filler particle fiber/metal laminate (H3 laminate). For Surface irregularity, the combination of traverse feed velocity and Nozzle-to-substrate gap had the most significant impact. To validate the optimization results, confirmatory tests was conducted, and the findings were very similar to the experimental values, indicating the accuracy and effectiveness of the optimization process. To better understand the manufacturing processes, a scanning electron microscope was used to examine the morphological features of the machined surfaces, such as delamination, fibre breakage, and fibre pull-out.

Influence of Alloying Materials Al, Cu, and Ca on Microstructures, Mechanical Properties, And Corrosion Resistance of Mg Alloys for Industrial Applications: A Review.

Magnesium is renowned for its favorable low-density attributes, rendering it a viable choice for commercial engineering applications in which weight has substantial design implications. Magnesium (Mg) stands as a readily obtainable metallic element, exhibiting robustness, efficient heat dissipation, and excellent damping properties. The utilization of pure magnesium remains infrequent due to its susceptibility to instability under high temperatures and pronounced vulnerability to corrosion within humid environments. Hence, the incorporation of magnesium alloys into the design process of aircraft, automotive, and biomedical applications assumes paramount importance. This Review presents a comprehensive review of research endeavors and their resultant achievements concerning the advancement of magnesium alloys. Specifically focusing on aerospace, automotive, and biomedical applications, the Review underscores the pivotal role played by alloying constituents, namely aluminum (Al), copper (Cu), calcium (Ca), and PEO coatings, in influencing the microstructural attributes, mechanical potency, and resistance to corrosion.

Optimization of subtilisin production from Bacillus subtilis strain ZK3 and biological and molecular characterization of synthesized subtilisin capped nanoparticles.

The increase and dissemination of multi-drug resistant bacteria have presented a major healthcare challenge, making bacterial infections a significant concern. The present research contributes towards the production of bioactive subtilisin from a marine soil isolate Bacillus subtilis strain ZK3. Custard apple seed powder (raw carbon) and mustard oil cake (raw nitrogen) sources showed a pronounced effect on subtilisin production. A 7.67-fold enhancement in the production was evidenced after optimization with central composite design-response surface methodology. Subtilisin capped silver (AgNP) and zinc oxide (ZnONP) nanoparticles were synthesized and characterized by UV-Visible spectroscopy. Subtilisin and its respective nanoparticles revealed significant biological properties such as, antibacterial activity against all tested pathogenic strains with potential against Escherichia coli and Pseudomonas aeruginosa. Prospective antioxidant behavior of subtilisin, AgNP and ZnONP was evidenced through radical scavenging assays with ABTS and DPPH. Subtilisin, AgNP and ZnONP revealed cytotoxic effect against cancerous breast cell lines MCF-7 with IC50of 83.48, 3.62 and 7.57 µg/mL respectively. Characterizations of nanoparticles were carried out by Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive X-ray, X-ray diffraction, thermogravimetric analysis and atomic force microscopy analysis to elucidate the structure, surface and thermostability properties. The study proposes the potential therapeutic applications of subtilisin and its nanoparticles, a way forward for further exploration in the field of healthcare.

Distribution of Carbon Nanotubes in an Aluminum Matrix by a Solution-Mixing Process.

In order to overcome the limitations of standard ball-mill mixing processes to fabricate a uniformly dispersed carbon nanotube (CNT) reinforcement composite without damaging CNTs in matrix powder, a unique and easy solution-mixing process was developed. The present study aims to synthesize Al-0.5 wt % CNT composites using ball-milling and solution-mixing processes and compares their CNT dispersion and structural and thermal properties. Compared with the ball-milling process, the solution-mixing process was simple and effective for the uniform distribution of CNTs without structural damage. Various methods were utilized to examine the structural characteristics of the composite powder. These techniques included high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Raman spectroscopy, and particle size analysis. Raman spectroscopy observes an increase of defects in ball-milled composites, and the particle size analyzer confirms the structural deformation, resulting in the degradation of composite powder mechanical properties. In the solution-mixing process, aluminum particles and the structure of CNTs are well-preserved even after mixing. Thermogravimetric analysis (TGA) was used to research the thermal stability of the composite materials. The results validated the impact of CNTs on thermal characteristics enhancement (improved thermal resistance) when compared with pure aluminum, suggesting potential uses in the aerospace industry, transport, and construction sectors.

Response surface methodology based optimization of keratinase from Bacillus velezensis strain ZBE1 and nanoparticle synthesis, biological and molecular characterization.

The increasing demands of keratinases for biodegradation of recalcitrant keratinaceous waste like chicken feathers has lead to research on newer potential bacterial keratinases to produce high-value products with biological activities. The present study reports a novel keratinolytic bacterium Bacillus velezensis strain ZBE1 isolated from deep forest soil of Western Ghats of Karnataka, which possessed efficient feather keratin degradation capability and induced keratinase production. Production kinetics depicts maximum keratinase production (11.65 U/mL) on 4th day with protein concentration of 0.61 mg/mL. Effect of various physico-chemical factors such as, inoculum size, metal ions, carbon and nitrogen sources, pH and temperature influencing keratinase production were optimized and 3.74 folds enhancement was evidenced through response surface methodology. Silver (AgNP) and zinc oxide (ZnONP) nanoparticles with keratin hydrolysate produced from chicken feathers by the action of keratinase were synthesized and verified with UV-Visible spectroscopy that revealed biological activities like, antibacterial action against Bacillus cereus and Escherichia coli. AgNP and ZnONP also showed potential antioxidant activities through radical scavenging activities by ABTS and DPPH. AgNP and ZnONP revealed cytotoxic effect against MCF-7 breast cancer cell lines with IC50 of 5.47 µg/ml and 62.26 µg/ml respectively. Characterizations of nanoparticles were carried out by Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive X-ray, X-ray diffraction, thermogravimetric analysis and atomic force microscopy analysis to elucidate the thermostability, structure and surface attributes. The study suggests the prospective applications of keratinase to trigger the production of bioactive value-added products and significant application in nanotechnology in biomedicine.

Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach.

Micrococcus luteus, also known as M. luteus, is a bacterium that inhabits mucous membranes, human skin, and various environmental sources. It is commonly linked to infections, especially among individuals who have compromised immune systems. M. luteus is capable of synthesizing the enzyme superoxide dismutase (SOD) as a component of its protective response to reactive oxygen species (ROS). This enzyme serves as a promising target for drug development in various diseases. The current study utilized a subtractive genomics approach to identify potential therapeutic targets from M. luteus. Additionally, genome mining was employed to identify and characterize the biosynthetic gene clusters (BGCs) responsible for the production of secondary metabolites in Bacillus licheniformis (B. licheniformis), a bacterium known for its production of therapeutically relevant secondary metabolites. Subtractive genomics resulted in identification of important extracellular protein SOD as a drug target that plays a crucial role in shielding cells from damage caused by ROS. Genome mining resulted in identification of five potential ligands (secondary metabolites) from B. licheniformis such as, Bacillibactin (BAC), Paenibactin (PAE), Fengycin (FEN), Surfactin (SUR) and Lichenysin (LIC). Molecular docking was used to predict and analyze the binding interactions between these five ligands and target protein SOD. The resulting protein-ligand complexes were further analyzed for their motions and interactions of atoms and molecules over 250 ns using molecular dynamics (MD) simulation analysis. The analysis of MD simulations suggests, Bacillibactin as the probable candidate to arrest the activities of SOD. All the five compounds reported in this study were found to act by directly/indirectly interacting with ROS molecules, such as superoxide radicals (O2-) and hydrogen peroxide (H2O2), and transforming them into less reactive species. This antioxidant activity contributes to its protective effects against oxidative stress-induced damage in cells making them likely candidate for various applications, including in the development of antioxidant-based therapies, nutraceuticals, and functional foods.

Iron pill aspiration syndrome: A case report and literature review.

Aspiration of iron pill containing ferrous sulfate into the airway can induce fulminant chemical burn and necrosis of the airway mucosa. Acute chemical burn and inflammatory response can result in life-threatening airway compromise. It can also result in long-term sequelae including but not limited to fibrosis and airway stenosis. Considering the common use of iron supplements, and the potential severity of aspiration related airway injury, clinicians should be fully cognizant of the interaction between aspirated iron and airway passages. Herein, we present a case report with pertinent review of the literature.

Studies on Evaluation of the Thermal Conductivity of Alumina Titania Hybrid Suspension Nanofluids for Enhanced Heat Transfer Applications.

Extensive investigations were made and empirical relations were proposed for the thermal conductivity of mono-nanofluids. The effect of concentration, diameter, and thermal properties of participating nanoparticles is missing in the majority of existing thermal conductivity models. An attempt is made to propose a model that considers the influence of such missing parameters on the thermal conductivity of hybrid nanofluids. Al2O3-TiO2 hybrid nanofluids have a 0.1% particle volume concentration prepared with distinct particle volume ratios (k - 1:6 - k, k = 1 to 6) in DI water. The samples were characterized, and the size and shape of the nanoparticles were verified. Also, the influence of varying particle volume ratios and the fluid temperature (varying from 283 to 308 K) were examined. 2.4 and 2.1% enhancements were observed in the thermal conductivity of alumina (5:0) and titania (0:5) nanofluids (having 0.1% volume concentration), respectively. Due to the low thermal conductivity of titania nanoparticles, the conductivity of the hybrid solution is above that of titania and below that of alumina nanofluids. An empirical relation for the thermal conductivity of hybrid nanofluids is established and validated considering the individual particle size, volume ratio, and thermal conductivity of particles.