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.
In the present review, we focused on the fundamental concepts of hydrogels-classification, the polymers involved, synthesis methods, types of hydrogels, properties, and applications of the hydrogel. Hydrogels can be synthesized from natural polymers, synthetic polymers, polymerizable synthetic monomers, and a combination of natural and synthetic polymers. Synthesis of hydrogels involves physical, chemical, and hybrid bonding. The bonding is formed via different routes, such as solution casting, solution mixing, bulk polymerization, free radical mechanism, radiation method, and interpenetrating network formation. The synthesized hydrogels have significant properties, such as mechanical strength, biocompatibility, biodegradability, swellability, and stimuli sensitivity. These properties are substantial for electrochemical and biomedical applications. Furthermore, this review emphasizes flexible and self-healable hydrogels as electrolytes for energy storage and energy conversion applications. Insufficient adhesiveness (less interfacial interaction) between electrodes and electrolytes and mechanical strength pose serious challenges, such as delamination of the supercapacitors, batteries, and solar cells. Owing to smart and aqueous hydrogels, robust mechanical strength, adhesiveness, stretchability, strain sensitivity, and self-healability are the critical factors that can identify the reliability and robustness of the energy storage and conversion devices. These devices are highly efficient and convenient for smart, light-weight, foldable electronics and modern pollution-free transportation in the current decade.
This study was based on the experimental performance evaluation of a wood polymer composite (WPC) that was synthesized by incorporating untreated and treated rice husk (RH) fibers into a polypropylene random copolymer matrix. The submicron-scale RH fibers were alkali-treated to modify the surface and introduce new functional groups in the WPC. A compatibilizer (maleic anhydride) and a thermos-mechanical properties modifier (polypropylene grafted with 30 % glass fiber) were used in the WPC. The effects of untreated and treated RH on the WPC panels were studied using FESEM, FTIR, and microscope images. A pin-on-disk setup was used to investigate the bulk tribological properties of PPRC and WPC. The complex relationship between the friction coefficient of different loading of RH fibers in the WPC, as a function of sliding distance, was analyzed along with the temperature and morphology of the surface. It was observed that untreated RH acted as a friction modifier, while treated RH acted as a solid lubricant. Microhardness was calculated using the QCSM module on nanoindentation. It was found that untreated RH led to an increase in microhardness, while treated RH caused a decrease in hardness compared to PPRC.
The aim of this study was to prepare and evaluate α-mangostin-loaded polymeric nanoparticle gel (α-MNG-PLGA) formulation to enhance α-mangostin delivery in an epidermal carcinoma. The poly (D, L-lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) were developed using the emulsion-diffusion-evaporation technique with a 3-level 3-factor Box-Behnken design. The NPs were characterized and evaluated for particle size distribution, zeta potential (mV), drug release, and skin permeation. The formulated PLGA NPs were converted into a preformed carbopol gel base and were further evaluated for texture analysis, the cytotoxic effect of PLGA NPs against B16-F10 melanoma cells, and in vitro radical scavenging activity. The nanoscale particles were spherical, consistent, and average in size (168.06 ± 17.02 nm), with an entrapment efficiency (EE) of 84.26 ± 8.23% and a zeta potential of -25.3 ± 7.1 mV. Their drug release percentages in phosphate-buffered solution (PBS) at pH 7.4 and pH 6.5 were 87.07 ± 6.95% and 89.50 ± 9.50%, respectively. The release of α-MNG from NPs in vitro demonstrated that the biphasic release system, namely, immediate release in the initial phase, was accompanied by sustained drug release. The texture study of the developed α-MNG-PLGA NPs gel revealed its characteristics, including viscosity, hardness, consistency, and cohesiveness. The drug flux from α-MNG-PLGA NPs gel and α-MNG gel was 79.32 ± 7.91 and 16.88 ± 7.18 µg/cm2/h in 24 h, respectively. The confocal study showed that α-MNG-PLGA NPs penetrated up to 230.02 µm deep into the skin layer compared to 15.21 µm by dye solution. MTT assay and radical scavenging potential indicated that α-MNG-PLGA NPs gel had a significant cytotoxic effect and antioxidant effect compared to α-MNG gel (p < 0.05). Thus, using the developed α-MNG-PLGA in treating skin cancer could be a promising approach.
The performance of a Pelton wheel is influenced by the jet created by the nozzle. Therefore, a Computational Fluid Dynamics (CFD) simulation was proposed. In this study, the significant output parameters (outlet velocity, outlet pressure, and tangential force component) and input parameters (different pressure and spear locations) were examined. In addition, the influencing parameters and their contributing percentages to the performance of the Pelton wheel were calculated using different optimisation techniques such as Taguchi Design of Experiments (DoE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA) and Criteria Importance Through Intercriteria Correlation (CRITIC). The effect of input factors on the output response was examined with DoE, and the results show that the inlet pressure had the most significant impact (97.38%, 99.18%, and 97.38%, respectively, for all different spear sites with a 99% confidence level). In terms of preference values, the TOPSIS and GRA results are comparable (best ranks for simulation runs #24 and #25 and least ranks for simulations #2 and #3, respectively). The CRITIC results for the pressure parameter are in good agreement with the Taguchi ANOVA analysis. The last spear location (5 mm after the nozzle outlet), with an inlet pressure of 413685 Pa generated the best result when employing the TOPSIS and GRA techniques. The outlet pressure of the nozzle was found to have a significant impact on the flow pattern of the Pelton Wheel based on the analysis of the CRITIC, Taguchi, and CFD results.
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.
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.
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.
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.