Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters.

Cardiovascular diseases, particularly coronary artery disease, pose big challenges to human life. Deployment of the stent is a preferable treatment for the above-mentioned disease. However, stents are usually made up of shape memory alloy called Nitinol. The poorer surface finish on the machined nitinol stents accelerates the migration of Nickel ions from the implanted nitinol stent, which is considered toxic and can lead to stenosis. The current study deals with controlling surface quality by minimising surface roughness and improving corrosion resistance. Femtosecond laser (fs-laser 10-15 s) micromachining was employed to machine the Nitinol surface to achieve sub-micron surface roughness. The Grey relational analysis (GRA)-coupled design of the experimental technique was implemented to determine optimal levels of four micromachining parameters (laser power, pulse frequency, scanning speed, and scanning pattern) varied at three levels to achieve minimum surface roughness and to maximise the volume ablation. The results show that to yield minimum surface roughness and maximum volume ablation, laser power and scanning speed are in a higher range. In contrast, the pulse frequency is lower, and the scanning pattern is in a zig-zag manner. ANOVA results manifest that scanning speed is the predominant factor in minimising surface roughness, followed by pulse frequency. Furthermore, the corrosion behaviour of the machined nitinol specimens was evaluated, and the results show that specimens with lower surface roughness had lower corrosion rates.

Perspectives on cultivation and harvesting technologies of microalgae, towards environmental sustainability and life cycle analysis.

The development of algae is seen as a potential and ecologically sound approach to address the increasing demands in multiple sectors. However, successful implementation of processes is highly dependent on effective growing and harvesting methods. The present study provides a complete examination of contemporary techniques employed in the production and harvesting of algae, with a particular emphasis on their sustainability. The review begins by examining several culture strategies, encompassing open ponds, closed photobioreactors, and raceway ponds. The analysis of each method is conducted in a systematic manner, with a particular focus on highlighting their advantages, limitations, and potential for expansion. This approach ensures that the conversation is in line with the objectives of sustainability. Moreover, this study explores essential elements of algae harvesting, including the processes of cell separation, dewatering, and biomass extraction. Traditional methods such as centrifugation, filtration, and sedimentation are examined in conjunction with novel, environmentally concerned strategies including flocculation, electro-coagulation, and membrane filtration. It evaluates the impacts on the environment that are caused by the cultivation process, including the usage of water and land, the use of energy, the production of carbon dioxide, and the runoff of nutrients. Furthermore, this study presents a thorough examination of the current body of research pertaining to Life Cycle Analysis (LCA) studies, presenting a perspective that emphasizes sustainability in the context of algae harvesting systems. In conclusion, the analysis ends up with an examination ahead at potential areas for future study in the cultivation and harvesting of algae. This review is an essential guide for scientists, policymakers, and industry experts associated with the advancement and implementation of algae-based technologies.

Exploring the impact of defect energy levels in CdTe/Si dual-junction solar cells using wxAMPS.

A numerical analysis of a CdTe/Si dual-junction solar cell in terms of defect density introduced at various defect energy levels in the absorber layer is provided. The impact of defect concentration is analyzed against the thickness of the CdTe layer, and variation of the top and bottom cell bandgaps is studied. The results show that CdTe thin film with defects density between 1014 and 1015 cm-3 is acceptable for the top cell of the designed dual-junction solar cell. The variations of the defect concentrations against the thickness of the CdTe layer indicate that the open circuit voltage, short circuit current density, and efficiency (ƞ) are more affected by the defect density at higher CdTe thickness. In contrast, the Fill factor is mainly affected by the defect density, regardless of the thin film's thickness. An acceptable defect density of up to 1015 cm-3 at a CdTe thickness of 300 nm was obtained from this work. The bandgap variation shows optimal results for a CdTe with bandgaps ranging from 1.45 to 1.7 eV in tandem with a Si bandgap of about 1.1 eV. This study highlights the significance of tailoring defect density at different energy levels to realize viable CdTe/Si dual junction tandem solar cells. It also demonstrates how the impact of defect concentration changes with the thickness of the solar cell absorber layer.

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.

Influence of varying concentrations of TiO2 nanoparticles and engine speed on the performance and emissions of diesel engine operated on waste cooking oil biodiesel blends using response surface methodology.

For a few decades now fast depleting fossil fuels has been a major challenge. Fast expanding population and increased rate of urbanization has increased energy demand. This makes the current scenario worse. Fossil fuels' emissions are another challenge. Apart from fossil fuel emissions, the untreated disposal of waste cooking oil presents another environment's sustainability challenge. The treatment of waste cooking oil as fuel presents a tangible solution to challenge. In this research article, impact of the engine speed and the concentration of titanium dioxide (TiO2) nanoparticles (NPs) in diesel-biodiesel blended fuels on the engine's performance. The emission characteristics of a single-cylinder four-stroke diesel engine has also been examined. TiO2 NPs were produced by a sol-gel methodology. The diesel-biodiesel combination was fortified with TiO2 NPs at 40, 80 and 120 ppm. These mixtures were used to power the diesel engine, which was then run at 1150, 1400, 1650, 1900 and 2150 RPM. Interaction between engine speeds and nanoparticle concentrations and investigation of their combined effect on engine performance and emissions was done using response surface methodology. The minimum BSFC of 0.33994 kg/kWh and maximum BTE of 25.90% were found for B30 + 120 ppm biodiesel blend at 2150 rpm as compared to all other tested fuels. The emissions including CO and HC emissions were recorded as 25.61486 kg/kWh and 0.05289kg/kWh respectively at 2150 rpm for B30 + 120 ppm biodiesel blend while NOx on the contrary side exhibits a slight escalation with increasing engine speed and nanoparticles concentration. The findings of the experiments demonstrated that adding TiO2 nanoparticles to diesel-biodiesel blends is an effective way to enhance the performance of diesel engines while simultaneously reducing the emissions. It was also discovered that the mathematical model that was built can efficiently estimate the performance of the engine and the emission levels.

Cardiovascular diseases prediction by machine learning incorporation with deep learning.

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle.

Recently, solar photovoltaic (PV) technology has shown tremendous growth among all renewable energy sectors. The attractiveness of a PV system depends deeply of the module and it is primarily determined by its performance. The quantity of electricity and power generated by a PV cell is contingent upon a number of parameters that can be intrinsic to the PV system itself, external or environmental. Thus, to improve the PV panel performance and lifetime, it is crucial to recognize the main parameters that directly influence the module during its operational lifetime. Among these parameters there are numerous factors that positively impact a PV system including the temperature of the solar panel, humidity, wind speed, amount of light, altitude and barometric pressure. On the other hand, the module can be exposed to simultaneous environmental stresses such as dust accumulation, shading and pollution factors. All these factors can gradually decrease the performance of the PV panel. This review not only provides the factors impacting PV panel's performance but also discusses the degradation and failure parameters that can usually affect the PV technology. The major points include: 1) Total quantity of energy extracted from a photovoltaic module is impacted on a daily, quarterly, seasonal, and yearly scale by the amount of dust formed on the surface of the module. 2) Climatic conditions as high temperatures and relative humidity affect the operation of solar cells by more than 70% and lead to a considerable decrease in solar cells efficiency. 3) The PV module current can be affected by soft shading while the voltage does not vary. In the case of hard shadowing, the performance of the photovoltaic module is determined by whether some or all of the cells of the module are shaded. 4) Compared to more traditional forms of energy production, PV systems offer a significant number of advantages to the environment. Nevertheless, these systems can procure greenhouse gas emissions, especially during the production stages. In conclusion, this study underlines the importance of considering multiple parameters while evaluating the performance of photovoltaic modules. Environmental factors can have a major impact on the performance of a PV system. It is critical to consider these factors, as well as intrinsic and other intermediate factors, to optimize the performance of solar energy systems. In addition, continuous monitoring and maintenance of PV systems is essential to ensure maximum efficiency and performance.

Anti-obesity effect of the bacterial product nisin in an NIH Swiss mouse model.

Obesity is a life-threatening metabolic disorder that predisposes individuals to other diseases. In this study, the effect of nisin, a bacteriocin produced by some bacteria, on an animal model of obesity based on selected parameters was investigated. Forty Swiss NIH mice were randomly divided into four groups and received either a placebo (saline) or nisin (25, 50, or 100 µg/kg, ip) daily for 8 weeks. The mice in all groups were fed a high-sugar diet throughout the experiment. Bodyweight and food intake were measured weekly, and at the end of the experiment, the levels of FBS, serum triglyceride, cholesterol, high-density lipoprotein, low-density lipoprotein, and hepatic enzymes were tested, and red and white blood cell counts, hemoglobin concentration, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration were determined. Finally, the expression levels of some obesity-related genes, including stearoyl-CoA desaturase-1 (SCD-1), glucose transporter-4 (GLUT4), zinc finger protein 423 (zfp423), 422 (ap2), and tumor necrosis factor-alpha (TNF-α), were assessed using reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR). After the experiment, the body weights, abdominal fat, and body mass index were significantly lower in the nisin-treated groups than in the control group. The highest effect was observed with 50 µg/kg nisin. The expression of SCD-1, GLUT4, 422(ap2), and TNF-α decreased significantly following treatment with nisin. No significant differences were observed in the other studied parameters, and no toxic effects were observed for nisin under these experimental conditions. The results suggested that nisin could have antiobesity effects.

Energy, exergy and economic (3E) analysis of flat-plate solar collector using novel environmental friendly nanofluid.

The use of solar energy is one of the most prominent strategies for addressing the present energy management challenges. Solar energy is used in numerous residential sectors through flat plate solar collectors. The thermal efficiency of flat plate solar collectors is improved when conventional heat transfer fluids are replaced with nanofluids because they offer superior thermo-physical properties to conventional heat transfer fluids. Concentrated chemicals are utilized in nanofluids' conventional synthesis techniques, which produce hazardous toxic bi-products. The present research investigates the effects of novel green covalently functionalized gallic acid-treated multiwall carbon nanotubes-water nanofluid on the performance of flat plate solar collectors. GAMWCNTs are highly stable in the base fluid, according to stability analysis techniques, including ultraviolet-visible spectroscopy and zeta potential. Experimental evaluation shows that the thermo-physical properties of nanofluid are better than those of base fluid deionized water. The energy, exergy and economic analysis are performed using 0.025%, 0.065% and 0.1% weight concentrations of GAMWCNT-water at varying mass flow rates 0.010, 0.0144, 0.0188 kg/s. The introduction of GAMWCNT nanofluid enhanced the thermal performance of flat plate solar collectors in terms of energy and exergy efficiency. There is an enhancement in efficiency with the rise in heat flux, mass flow rate and weight concentration, but a decline is seen as inlet temperature increases. As per experimental findings, the highest improvement in energy efficiency is 30.88% for a 0.1% weight concentration of GAMWCNT nanofluid at 0.0188 kg/s compared to the base fluid. The collector's exergy efficiency increases with the rise in weight concentration while it decreases with an increase in flow rate. The highest exergy efficiency is achieved at 0.1% GAMWCNT concentration and 0.010 kg/s mass flow rate. GAMWCNT nanofluids have higher values for friction factor compared to the base fluid. There is a small increment in relative pumping power with increasing weight concentration of nanofluid. Performance index values of more than 1 are achieved for all GAMWCNT concentrations. When the solar thermal collector is operated at 0.0188 kg/s and 0.1% weight concentration of GAMWCNT nanofluid, the highest size reduction, 27.59%, is achieved as compared to a flat plate solar collector with water as a heat transfer fluid.

Biomimicking Nature-Inspired Design Structures-An Experimental and Simulation Approach Using Additive Manufacturing.

Whether it is a plant- or animal-based bio-inspiration design, it has always been able to address one or more product/component optimisation issues. Today's scientists or engineers look to nature for an optimal, economically viable, long-term solution. Similarly, a proposal is made in this current work to use seven different bio-inspired structures for automotive impact resistance. All seven of these structures are derived from plant and animal species and are intended to be tested for compressive loading to achieve load-bearing capacity. The work may even cater to optimisation techniques to solve the real-time problem using algorithm-based generative shape designs built using CATIA V6 in unit dimension. The samples were optimised with Rhino 7 software and then simulated with ANSYS workbench. To carry out the comparative study, an experimental work of bioprinting in fused deposition modelling (3D printing) was carried out. The goal is to compare the results across all formats and choose the best-performing concept. The results were obtained for compressive load, flexural load, and fatigue load conditions, particularly the number of life cycles, safety factor, damage tolerance, and bi-axiality indicator. When compared to previous research, the results are in good agreement. Because of their multifunctional properties combining soft and high stiffness and lightweight properties of novel materials, novel materials have many potential applications in the medical, aerospace, and automotive sectors.

Experimental and Computational Study of Mechanical and Thermal Characteristics of h-BN and GNP Infused Polymer Composites for Elevated Temperature Applications.

Polymer-based nanocomposites are being considered as replacements for conventional materials in medium to high-temperature applications. This article aims to discover the synergistic effects of reinforcements on the developed polymer-based nanocomposite. An epoxy-based polymer composite was manufactured by reinforcing graphene nanoplatelets (GNP) and h-boron nitride (h-BN) nanofillers. The composites were prepared by varying the reinforcements with the step of 0.1 from 0.1 to 0.6%. Ultrasonication was carried out to ensure the homogenous dispersion of reinforcements. Mechanical, thermal, functional, and scanning electron microscopy (SEM) analysis was carried out on the novel manufactured composites. The evaluation revealed that the polymer composite with GNP 0.2 by wt % has shown an increase in load-bearing capacity by 265% and flexural strength by 165% compared with the pristine form, and the polymer composite with GNP and h-BN 0.6 by wt % showed an increase in load-bearing capacity by 219% and flexural strength by 114% when compared with the pristine form. Furthermore, the evaluation showed that the novel prepared nanocomposite reinforced with GNP and h-BN withstands a higher temperature, around 340 °C, which is validated by thermogravimetric analysis (TGA) trials. The numerical simulation model is implemented to gather the synthesised nanocomposite's best composition and mechanical properties. The minor error between the simulation and experimental data endorses the model's validity. To demonstrate the industrial applicability of the presented material, a case study is proposed to predict the temperature range for compressor blades of gas turbine engines containing nanocomposite material as the substrate and graphene/h-BN as reinforcement particles.

Optimization of Thermal and Structural Design in Lithium-Ion Batteries to Obtain Energy Efficient Battery Thermal Management System (BTMS): A Critical Review.

Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.

Performance indicators for the optimal BTE of biodiesels with additives through engine testing by the Taguchi approach.

Biodiesel commercialization is questionable due to poor brake thermal efficiency. Biodiesel utilization should be improved with the addition of fuel additives. Hydrogen peroxide is a potential fuel additive due to extra hydrogen and oxygen content, which improves the combustion process. In this experimental study, biodiesel has been produced from Jatropha oil employing catalyzed transesterification homogeneously to examine its influence on the performance and emissions at engine loads with 1500 rpm utilizing a four-stroke single-cylinder diesel engine. D60B40 (having 60% diesel and 40% biodiesel) and D60B30A10 (60% diesel, 30% biodiesel and 10% hydrogen peroxide (H2O2)), are the fuel mixtures in the current study. The addition of H2O2 reduces emissions and enhances the combustion process. This effect occurred due to the micro-explosion of the injected fuel particles (which increases in-cylinder pressure and heat release rate (HRR)). An increase of 20% in BTE and 25% reduction in BSFC for D60B30A10 was observed compared to D60B40. Significant reduction in emissions of HC up to 17.54%, smoke by 24.6% CO2 by 3.53%, and an increase in NOx was noticed when the engine is operated with D60B30A10. The HRR increased up to 18.6%, ID reduced by 10.82%, and in-cylinder pressure increased by 8.5%. Test runs can be minimized as per Taguchi's design of experiments. It is possible to provide the estimates for the full factorial design of experiments. Exhaust gas temperature standards are evaluated and examined for all fuel blends.

Corrosion Characterization at Surface and Subsurface of Iron-Based Buried Water Pipelines.

Water pipe surface deterioration is the result of continuous electrochemical reactions attacking the surface due to the interaction of the pipe surface with environments through the time function. The study presents corrosion characterization at the surface and sub-surface of damaged ductile iron pipe (DIP) and galvanized steel (GS) pipes which served for more than 40 and 20 years, respectively. The samples were obtained from Addis Ababa city water distribution system for the analysis of corrosion morphology patterns at different surface layers. Mountains 8.2 surface analysis software was utilized based on the ISO 25178-2 watershed segmentation method to investigate corrosion features of damaged pipe surface and to evaluate maximum pit depth, area, and volume in-situ condition. Based on the analysis maximum values of pit depth, area and volume were 380 µ m, 4000 µm2, and 200,000 µm3, respectively, after 25% loss of the original 8 mm thickness of DIP. Similarly, the pit depth of the GS pipe was 390 µm whereas the maximum pit area and volume are 4000 µm2 and 16,000 µm3, respectively. In addition, characterizations of new pipes were evaluated to study microstructures by using an optical microscope (OM), and a scanning electron microscope (SEM) was used to analyze corrosion morphologies. Based on the SEM analysis, cracks were observed at the sub-surface layer of the pipes. The results show that uniform corrosion attacked the external pipe surface whereas pitting corrosion damaged the subsurface of pipes. The output of this study will be utilized by water suppliers and industries to investigate corrosion phenomena at any damage stage.

Parametric Analysis of Epoxy/Crumb Rubber Composite by Using Taguchi-GRA Hybrid Technique.

Effect of parameters affecting solid particle erosion of crumb rubber epoxy composite is investigated. Five important process parameters-impact velocity, impingement angle, standoff distance, erodent size, and crumb rubber content-are taken into consideration. Erosion rate and erosion efficiency are included as the chief objectives. The Taguchi coupled gray relational analysis type statistical model is implemented to study interaction, parameters' effect on responses, and optimized parameters. ANOVA and regression model affirmed impingement angle and crumb rubber content play a significant role to minimize the erosion. Validity of the proposed model is justified with the standard probability plot and R2 value. A confirmation experiment conducted with A2B2C3D3E3 condition registers noticeable enhancement in GRG to the tune of 0.0893.

The anthropometric assessment of body composition and nutritional status in children aged 2-15 years: A cross-sectional study from three districts in Bangladesh.

BACKGROUND: Early life nutrition plays a critical role in the development of better health and nutrition in adulthood. However, assessing the nutritional status of Bangladeshi children and adolescents through measurement of body composition using skinfold thickness is barely studied. The current study aims to determine children's body composition and nutritional status, and contributing factors among children aged 2 to 15 years in the northern part of Bangladesh. METHODS: This is a descriptive cross-sectional study done in Bangladesh. Anthropometric methods, including multiple skinfold thickness and basic anthropometric and socio-demographic characteristics, were used. Body composition was calculated from multiple skinfold thicknesses using the standard regression equation. Nutritional status was measured using Z score according to WHO 2007 reference standard. A total of 330 children from Naogaon, Bogra and Kurigram districts in Bangladesh were examined from April 2019 to September 2019. RESULTS: The Nutritional status of 2-15 years old child is exceedingly poor in the northern part of Bangladesh. Fat mass and fat-free mass were higher among children from Kurigram district than from Bogra and Naogaon district. Body fat percentages and arm fat area were greater among female children than males. The overall prevalence of stunting, underweight and wasting was around 25%, 32% and 29%, respectively, and the rate was higher among girls and children aged 2-5 years. The average SD score for weight-for-age, height-for-age, and BMI-for-age was -1.295, -0.937 and -1.009. The median weight-for-age and height-for-age Z scores of boys and girls were below the WHO reference percentile rank. Girls were twice (OR:1.951, CI:1.150-3.331) as likely to suffer from being underweight than boys. Children who don't practice handwashing are three times (OR:3.531, CI:1.657-7.525) more likely to be underweight. Children become underweight and stunted when their family income is not sufficient to maintain their nutritional requirements. CONCLUSIONS: The children of the three northern districts had a poor nutritional status, and family income was the potential contributing factor. Therefore, interventions like the promotion of income-generating activities and integrated approaches to ensuring food diversification could be an option to address the nutritional problem of children of the three northern districts of Bangladesh.

Leverage of Environmental Pollutant Crump Rubber on the Dry Sliding Wear Response of Epoxy Composites.

The effect of crump rubber on the dry sliding wear behavior of epoxy composites is investigated in the present study. Wear tests are carried out for three levels of crump rubber (10, 20, and 30 vol.%), normal applied load (30, 40, and 50 N), and sliding distance (1, 3, and 5 km). The wear behavior of crump rubber-epoxy composites is investigated against EN31 steel discs. The hybrid mathematical approach of Taguchi-coupled Grey Relational Analysis (GRA)-Principal Component Analysis (PCA) is used to examine the influence of crump rubber on the tribological response of composites. Mathematical and experimental results reveal that increasing crump rubber content reduces the wear rate of composites. Composites also show a significant decrease in specific wear values at higher applied loads. Furthermore, the coefficient of friction also shows a decreasing trend with an increase in crump rubber content, indicating the effectiveness of reinforcing crump rubber in a widely used epoxy matrix. Analysis of Variance (ANOVA) results also reveal that the crump rubber content in the composite is a significant parameter to influence the wear characteristic. The post-test temperature of discs increases with an increase in the applied load, while decreasing with an increase in filler loading. Worn surfaces are analyzed using scanning electron microscopy to understand structure-property correlations. Finally, existing studies available in the literature are compared with the wear data of the present study in the form of a property map.

Statistical Analysis of Polymer Nanocomposites for Mechanical Properties.

Epoxy resins, due to their high stiffness, ease of processing, good heat, and chemical resistance obtained from cross-linked structures, have found applications in electronics, adhesives coatings, industrial tooling, and aeronautic and automotive industries. These resins are inherently brittle, which has limited their further application. The emphasis of this study is to improve the properties of the epoxy resin with a low-concentration (up to 0.4% by weight) addition of Multi-Walled Carbon Nanotubes (MWCNTs). Mechanical characterization of the modified composites was conducted to study the effect of MWCNTs infusion in the epoxy resin. Nanocomposites samples showed significantly higher tensile strength and fracture toughness compared to pure epoxy samples. The morphological studies of the modified composites were studied using Scanning Electron Microscopy (SEM).

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates.

The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg-Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

Engine performance and emission characteristics of palm biodiesel blends with graphene oxide nanoplatelets and dimethyl carbonate additives.

This study investigated the engine performance and emission characteristics of biodiesel blends with combined Graphene oxide nanoplatelets (GNPs) and 10% v/v dimethyl carbonate (DMC) as fuel additives as well as analysed the tribological characteristics of those blends. 10% by volume DMC was mixed with 30% palm oil biodiesel blends with diesel. Three different concentrations (40, 80 and 120 ppm) of GNPs were added to these blends via the ultrasonication process to prepare the nanofuels. Sodium dodecyl sulphate (SDS) surfactant was added to improve the stability of these blends. GNPs were characterised using Scanning Electron Microscope (SEM) and Fourier Transform Infrared (FTIR), while the viscosity of nanofuels was investigated by rheometer. UV-spectrometry was used to determine the stability of these nanoplatelets. A ratio of 1:4 GNP: SDS was found to produce maximum stability in biodiesel. Performance and emissions characteristics of these nanofuels have been investigated in a four-stroke compression ignition engine. The maximum reduction in BSFC of 5.05% and the maximum BTE of 22.80% was for B30GNP40DMC10 compared to all other tested blends. A reduction in HC (25%) and CO (4.41%) were observed for B30DMC10, while a reduction in NOx of 3.65% was observed for B30GNP40DMC10. The diesel-biodiesel fuel blends with the addition of GNP exhibited a promising reduction in the average coefficient of friction 15.05%, 8.68% and 3.61% for 120, 80 and 40 ppm concentrations compared to B30. Thus, combined GNP and DMC showed excellent potential for utilisation in diesel engine operation.