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.

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.

Analysis of thermal decomposition kinetics of chicken feather fiber reinforced Poly-lactic acid composites filament.

Derivative Thermogravimetric analysis under air was used to observe the thermal decomposition process of Chicken feather fiber (CFF) reinforced Poly-lactic acid (PLA) composite filament of 2.2 mm diameter. The thermal degradation of the sample was initiated at 140 Ö¯C. Approximately 75 % of the thermal degradation occurred between the temperature of 357 Ö¯C and 399 Ö¯C. The composite's activation energy was established using the Coats-Redfern method. The results showed that the activation energy of 112.06 kJ/mol is utilized for the sample throughout the temperature range of 23 Ö¯C to 398 Ö¯C. A low activation energy is indicative of rapid chemical reactions between the CFF and PLA molecules. The results from TGA and DTGA indicate that the addition of CFF in the PLA matrix enhanced the thermal stability.

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.

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.

Antecedents for online food delivery platform leading to continuance usage intention via e-word-of-mouth review adoption.

The focus of hospitality initially was on ambience and novelty to attract customers. With the rise of the digital revolution, the hospitality industry has also undergone significant change. Long-distance travel at the workplace, odd working hours, and a variety of food options have driven people staying in Indian metropolises towards online food delivery (OFD) services. The popularity of OFD services has risen because of their practicality, simplicity, and a rise in consumer confidence in digital payments. Specifically, for the food industry, digitalization has opened new horizons to capture customers. The competition is not among the big brands, but big brands are competing with homemakers who run tiffin services, and street food hawkers who claim to provide traditional Dhaba-style food and fast food. The customers are loaded with unlimited options to choose the food in terms of price, cuisine, quality, etc. The present research examines the associations between service quality of OFD services, perceived ease of use, and word-of-mouth review adoption, leading to expectation confirmation modeling. The path analysis was carried out using data from 500 Indian respondents residing in Tier-I cities who have been using OFD services regularly. The research outcome shows that servqual has a positive influence on perceived ease of use and confirmation. Additionally, it encourages continued usage intentions because of its favorable impact on the adoption of e-word-of-mouth reviews.

Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques.

The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.

Review on the Relationship between Nano Modifications of Geopolymer Concrete and Their Structural Characteristics.

The main objective of this review is to study some important nanomaterials and their impact on the performance of geopolymer concrete. This paper is an investigation into trends and technology in the development of different nanomaterials to develop higher structural performance geopolymer concrete. The effect of the alkaline to binder and sodium silicate to sodium hydroxide ratio on the performances of geopolymer performances is studied. The relationship between setting time and slump is evaluated through the ternary plot, the variation in compressive strength values is evaluated using the kernel density plot, and the relationship between split tensile and flexural strength is investigated using the scattering interval plot. Regression analysis is carried out among water absorption and bulk-density result values obtained from previous literature. As the molarity and alkaline to binder (A/B) ratios increase, the strength development of geopolymer concrete increases up to a specific limit. The addition of a small quantity of nanomaterials, namely, nano silica, nano alumina, carbon nano tubes, and nano clay, led to the maximum strength development of geopolymer concrete. Incorporating these nanomaterials into the geopolymer significantly refines the structural stability, improving its durability. The various products in GP composites emerging from the incorporation of highly reactive SEM, XRD, and FTIR analysis of nanomaterials reveal that the presence of nanomaterials, which enhances the rate of polymerization, leads to better performance of the geopolymer.