Search

Abstract Pyrolysis of waste furniture wood, mixed plastics and waste tyres was examined separately and in different combinations from the perspective of improved value products and energy production. The effect of different combinations of furniture wood, plastics and tyres on the product distribution during co-pyrolysis was analyzed. The experimental work throughout this study was performed at a temperature of 500 °C. Prior to the pyrolysis experiments, thermogravimetric analysis was done to assess the thermal degradation behavior of all selected feedstocks. During individual pyrolysis, mixed plastic wastes produced 70.6 wt% of pyrolysis liquid, which is 37.9% and 33.4% more than furniture wood and waste tyres. During co-pyrolysis, a binary blend of mixed plastics and waste tyres produced 63.3 wt% of pyrolysis liquid, which is 7.65% more than the logarithmic mean value, indicating a positive synergistic interaction on liquid production. The liquid yield of the ternary blend was observed to be as low as 54.4 wt% due to the lower volatiles present in the blend. The presence of volatiles in the feedstock is correlated with the production of liquids by individual and co-pyrolysis. For feed-flexible, more efficient, and cleaner operating systems, the increased liquid production offers crucial information. With the use of Fourier transform infrared spectroscopy (FT-IR) and gas chromatography/mass spectroscopy (GC–MS), the impact of different combinations on product characterization was investigated. The characterization study of the pyrolysis liquid obtained from mixed plastics showed the presence of different aromatic and aliphatic compounds. The findings offered viable options for efficiently utilizing waste materials, particularly plastics and tyres to improve the quality and substance of products.

This work modifies the architecture of conventional CNN with the integration of Multi-resolution Analysis (MRA) in a CNN framework for Diabetic Retinopathy (DR) diagnosis and grading. Here, the HF sub-bands are subjected to optimized activations and are directly fed to the fully connected layers, as it encompasses edge features. Unlike FD-Relu, the proposed function preserves significant negative coefficients, compared to the S-Relu, the proposed third-order S-Relu is optimized such that it sustains the activations in the range suitable for the wavelet coefficients. The coefficients of higher-order terms of the proposed 3rd-order S-Relu are optimized with PSO, fitting the maximum energy of the wavelet sub-bands to ensure High Frequency (HF) edge preservation. The authors re-architecture 3 different CNNs published in the Retinal Image analysis field, with spatial and wavelet inputs with optimized activations. The highest accuracy of 96% is attained with the AlexNet re-architecture, with 35,126 fundus images secured from the Kaggle dataset. As we can infer the proposed re-architecture wavelet CNN outperformed the multiscale shallow CNNs, multiscale attention net, and stacked CNNs with a 6.6, 0.3, 0.7 per cent increase in accuracy. The entire implementation of the wavelet CNN is made available under source code.

Keeping the sustainability concept in view, a green, biocompatible, eco-friendly, and advantageous nano-composite was fabricated by impregnation of chitosan derived from waste shrimp shells onto Magnesium oxide nanoparticle (MgO) for exploiting in acenaphthene (ACN) removal from wastewater resources. The tested nanocomposite showed a high potential for ACN adsorption at pH 5.5. The thermodynamic evaluations laid the spontaneity nature of the process (∆G ≤-25.93 kjmol−1) bearing a ∆H value of 12.701 kjmol−1 which shows a favorable physisorption phenomenon. Kinetic evaluations demonstrated that the studied adsorption system conforms to pseudo-first order model to a greater extent. Other kinetic evaluations (Dumwald–Wagne/ Boyd model) revealed the important role of film diffusion in such an adsorption system, especially in the initial stages of the process. The equilibrium scrutinization under isothermal conditions displayed that the process has proceeded via mono-layer adsorption with a qmax value of 116.88 mg/g (at 298 K), for which Langmuir model showed a high goodness-of-fit. Overall, the new green adsorbent was highly advantageous in removal of ACN, and hence it is worth to be checked for the possibility of removing similar contaminants from polluted agricultural wastewater resources.

By applying heat to the feedstock during the thermal treatment of biomass for the production of biogas, the organic material's biodegradability can be greatly increased. Biogas production is a huge research area for alternate energy production technology. Increased biodegradability, improved methane yield, pathogen, and weed seed destruction, and overall process efficiency are all benefits of this type of pretreatment. It is a useful pretreatment technique for maximizing the production of biogas because it can decrease inhibitory compounds, and increase the digestibility of biomass. This work focused on increasing the efficiency of biogas production from lignocellulosic biomass of pigeon pea stalks by a novel thermal pretreatment. The pigeon pea stalk is initially imposed to physical pretreatment (PT) by an automatic hammer mill which is considered as a base for comparing performance. Thermal pretreatment was carried out for one hour, and two hours durations at different temperatures like 100 °C, 125 °C, 150 °C, 175 °C, and 200 °C. Compared to physically pretreated pigeon pea stalks, 200ᴼC thermal pretreated pigeon pea stalks for two hours have produced 88.41 % higher biogas, 16.14 % increase of cellulose, 19.9 % higher volatile solid removal, and 3.94 % lesser lignin. The enhanced chemical characteristics were ensured by analyzing the chemical composition variations through the FTIR, XRD, and SEM images. So, this is recommended for enhanced biogas production.

This study explores the transformative impact of three-dimensional printing, or additive manufacturing, in the development of bamboo-based 3D printing parts. Recently, there has been growing interest in incorporating natural fibers, such as bamboo, into polymers to enhance the structural integrity and strength of 3D-printed polymeric materials. This paper thoroughly examines the opportunities and obstacles associated with using additive manufacturing techniques to print bamboo fiber composites. This study includes an analysis of the mechanical properties, thermal properties, biodegradability, and environmental benefits of bamboo fiber composites. It also covers the processing methods and the printing parameters of bamboo fiber composites. This paper review focuses on the future prospects of bamboo fiber composites as a sustainable material in additive manufacturing based on the analysis of the existing literature and the recent research developments.

This study examines the effect of different infill patterns and percentages on the compressive strength attributes of carbon fiber-reinforced PETG samples printed using fused deposition modeling, employing response surface methodology. Carbon fiber-enhanced PETG (polyethylene terephthalate glycol) composites represent a cutting-edge advancement in additive manufacturing, drawing significant interest due to their impressive mechanical attributes. The experimentation involves modifying printing parameters such as the infill pattern (tri-hexagon, cubic, or line) and infill density (40%, 60%, and 80%). These parameter values were obtained through a central composite experimental design utilizing response surface methodology. The compressive strength of the 3D-printed carbon fiber-reinforced PETG specimens is assessed following ASTM D695 standards. Research indicates that increasing the density of the infill results in enhanced compressive strength. Specifically, specimens featuring an 80% infill density with a tri-hexagon pattern demonstrate a notable compressive strength of 39.16 MPa. By employing regression analysis and optimization techniques, the study predicts experimental outcomes accurately. These findings offer valuable insights into refining the manufacturing process of carbon fiber-reinforced PETG components. This advancement holds potential benefits across various engineering fields, particularly in automotive and aerospace industries, where strength and durability are essential.

Metal matrix composites have captured considerable interest in tribological applications, largely owing to their remarkable characteristics, which include a high strength-to-weight ratio and a low wear rate. This investigation delves into the exploration of hybrid metal matrix composites, where cobalt and chromium play the role of reinforcing agents within a bronze foundation. These composites were manufactured through a powder metallurgy process, utilizing cobalt and chromium metal powders with a particle size of 40 μm. Various weight percentage ratios (2.5%, 5.0%, and 7.5%) were utilized to create these composite specimens. To assess their tribological performance, the composite samples were subjected to a sliding wear test using a pin on disk machine, following the ASTM G99 standards. The wear characteristics of these composites were analyzed using the Taguchi method, considering parameters such as the applied load, speed, reinforcement percentage, and sliding distance. In addition, we conducted an analysis of variance on the collected data. To analyze the wear behavior of these hybrid metal matrix composites based on bronze, we utilized both multiple linear regression analysis and a signal-to-noise ratio assessment. The results indicate that the inclusion of cobalt and chromium metal powders as reinforcement materials enhances the tribological properties of the bronze matrix material.

Shape memory and super elasticity are two outstanding properties of Cu–Al–Mn SMAs together with additional copper-based shape memory alloys that make them superior to NiTi alloys. It's quite hard to machine Cu–Al–Mn SMA. The machinability properties of Cu–Al–Mn SMAs during Wire Electrical Discharge Machining (WEDM) were experimentally investigated using molybdenum wire as the electrode material. Material Removal Rate (MRR) and Surface Roughness (SR) are two machining qualities that are studied in relation to the combinational values of pulse time (Ton), pause time (Toff), and peak current (Ip), which are selected as the changeable input process factors. From the experimental investigation, it was found that both MRR and Ra values were increased with an increase in pulse on time(Ton), pulse off time(Toff), and peak current (IP)values. Because the wire electrode contains maximum current under these conditions, it is easy to remove more material at the tool and work material interface, resulting in moderate surface finish. Surface quality was higher on samples machined at low energy input than it was on samples machined at high energy input. This SEM micrograph clearly shows that a lower pulse on time produces a superior surface quality.SEM images and the analysis of the machined surface morphology on the surface of the CAM7 alloy clearly showed that the machined surface of the alloy had a lower surface roughness value at a lower Ip-2A and was associated with a higher surface roughness value at a higher Ip-6A, as well as a lot of uncleared debris and blow holes.

The development of sustainability in industry has made it imperative to utilize waste and accessible natural resources properly. Our goal is to transform the naturally occurring resources, tamarind seed powder (TSP), eelgrass (EG), and adamant creeper (AC), into goods that are beneficial to society. In this study, different weight proportions of alkali-treated AC, EG, and TSP were combined to make hybrid natural fiber (NF) composite materials using the hand layup process. The material strength of the alkali treated (15% AC, 20% EG, and 15% TSP) composite sample was attained 211 MPa, 278 MPa, 21.7 J/m2, and 84.6 for tensile, flexural, impact, and hardness in that sequence. The storage modulus, loss modulus, and mechanical loss factor were measured and plotted against temperature in a dynamic mechanical analysis (DMA) experiment. All composites had higher storage and loss moduli and a lower mechanical loss factor, indicating more elastic properties. The findings demonstrated that adding fiber-filled TSP fillers tended to make the epoxy matrix more viscoelastic stiffness. As the weight percentage of fibers in the composite increases, discernible changes in the structural damping capacity have also been noticed. The thermal characteristics of composites were inspected by thermogravimetric analysis (TGA), and the TGA outcomes substantiated that the thermal constancy of a 20% EG-reinforced composite was good. The mechanical, dynamic, and thermal qualities of this composite material will yield significant benefits for the industrial sector.

Abstract All the countries in the world are now searching for renewable, environmentally friendly alternative fuels due to the shortage and environmental problems related with the usage of conventional fuels. The cultivation of cereal and noncereal crops through agricultural activities produces waste biomasses, which are being evaluated as renewable and viable fossil fuel substitutes. The thermochemical properties and thermal degradation behavior of Sesbania grandiflora residues were investigated for this work. A fluidized bed reactor was used for fast pyrolysis in order to produce pyrolysis oil, char and gas. Investigations were done to analyze the effect of operating parameters such as temperature (350–550 °C), particle size (0.5–2.0 mm), sweeping gas flow rate (1.5–2.25 m3/h). The maximum of pyrolysis oil (44.7 wt%), was obtained at 425 °C for 1.5 mm particle size at the sweep gas flow rate of 2.0 m3/h. Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry methods were used to examine the composition of the pyrolysis oil. The pyrolysis oil is rich with aliphatic, aromatic, phenolic, and some acidic chemicals. The physical characteristics of pyrolysis oil showed higher heating value of 19.76 MJ/kg. The char and gaseous components were also analyzed to find its suitability as a fuel.

The study utilized Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to identify the optimal combination of three key factors for producing polymer composites: nanoparticle percentage, silane concentration, and silane dipping duration. RSM and Analysis of Variance (ANOVA) were applied to explore the relationships between these variables and their effects on composite properties. Additionally, the ANN was used to analyze multiple factors, revealing a strong correlation between predicted and observed results. The findings highlighted that silane treatment was the most influential factor in enhancing the composite's flexural strength. Fiber-related properties, particularly the duration of silane dipping, significantly impacted both flexural strength and hardness. Nanoparticles further strengthened the fiber matrix by encouraging effective agglomeration. The ANN demonstrated 95% accuracy in predicting flexural strength and hardness, and its predictions were validated by comparing them with experimental data and the regression model's outcomes. Silane concentration also notably influenced flexural properties. The RSM analysis identified the optimal combination for maximizing flexural strength and hardness: 5% nanoparticles, 10% silane concentration, and a 20-minute silane dipping time.

Fused Deposition Modeling (FDM) has emerged as a pivotal additive manufacturing technology that enables the creation of complex geometries with high precision and repeatability, particularly in the fabrication of biomedical implants. The functional additive manufacturing components, such as custom prosthetics, dental implants, and surgical guides, are expected to have dimensional accuracy and superior surface finish to ensure biocompatibility, osseointegration, and optimal tissue-implant interaction. This study aimed to optimize the FDM process parameters to minimize circularity error and surface roughness (Ra) using the Taguchi method, with a focus on biomedical implant fabrication. The printing temperature, printing speed, and layer thickness were identified as significant factors affecting circularity and Ra. Orthogonal arrays of Taguchi L9 were devised with three levels of each factor. Circularity and Ra were measured on the printed specimens, which included biomedical implant prototypes. The signal-to-noise (S/N) ratio response analysis determined the optimal parameter combinations. Results showed that the most dominant factor for circularity error reduction was build orientation, while layer thickness had the highest influence on Ra. The confirmation runs validated that the Taguchi-optimized parameters reduced the circularity error by 42% and Ra by 67% compared to default settings. Therefore, the Taguchi method proved effective in identifying the ideal process combinations for biomedical implant fabrication. The findings can help FDM users in the biomedical sector minimize dimensional errors and improve surface finish quality, ultimately enhancing the performance and reliability of medical implants.

The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease’s prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and untrustworthy data sources. Using ensemble learning with optimised k-means helps with these problems; it reduces overfitting and improves reliability by using methods like k-means clustering, frog feap algorithm (FLA), boosting, and bagging to balance datasets and show the complexity of symptoms. In this research, we present a new strategy that makes use of a hybrid optimised K-Means algorithm combined with the FLA, along with thresholding and morphological approaches, and ensemble learning. There are two main stages to our work. In the initial phase, we preprocess low-contrast MRI images and delineate the breast tumour area from the segmented MRI images. Furthermore, we employ Discrete Wavelet Transformation (DWT) to extract the most salient features. In the next step, the retrieved features will be used as input parameters for an ensemble-learned breast tumour classifier. To determine whether a low-contrast breast tumour MRI picture has a benign or malignant tumour after model training, we use a variety of classifiers, such as k-nearest neighbours (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), and artificial neural networks (ANN). Through a voting mechanism that averages the accuracy of all models, our suggested framework achieves a remarkable 98.8% accuracy rate. In addition, proposed model is quite efficient & reliable to detect the kind of breast tumour or cancer with a sensitivity of 98.02% and a specificity of 97.5%.

AbstractThis study investigates two configurations of novel solar energy-assisted combined cooling, heating and power (CCHP) systems suitable for the dairy industry. The system uses carbon dioxide (CO2) as the working fluid. The cooling and heating outputs were used for the simultaneous chilling and pasteurization of milk. The electrical power produced in the turbine is used to run the compressor and cater to the miscellaneous electrical demands of the dairy farm, thereby achieving grid independence. A detailed thermodynamic analysis was performed on the two configurations (with and without reheating) to identify the impact of various independent parameters on system performance. In addition, an irreversibility analysis was performed on the components in both configurations to obtain an idea of the losses occurring in the system. It is observed that the addition of the reheater provides a better specific net power output by approximately 4% over a variation of 6000 kPa in the cycle maximum pressure.

Cupola slag is a waste material of the steel and iron industries. Its composition is determined by the cupola furnace and other elements used in steel and iron manufacturing. This paper investigates the characterization behavior of various cupola slag materials. As a result, x-ray fluorescence (XRF), x-ray diffraction (XRD), thermogravimetry differential thermal analysis, and scanning electron microscopy (SEM) methods were used to characterize three cupola slag samples from distinct origins. In addition, various physical properties were used to compare different cupola slags. The specific gravity values of CS-1 (cupola slag-1 sample), CS-2 (cupola slag-2 sample), and CS-3 (cupola slag-3 sample) are 1.36, 2.5, and 2.917, respectively. The density and water absorption for CS-1, CS-2, and CS-3 are 1414.86, 1477.71, and 1796 kg/m3, and 0.37%, 0.32%, and 0.26%, respectively. Cupola slag also includes a larger percentage of lime, according to XRF data, which contributes to its improved binding characteristics. A higher calcium oxide content in CS-3 could facilitate the pozzolanic process. The presence of angular particles that aid in material binding is seen in the SEM image. Compounds with a nanostructure are then flawlessly blended into the mixture and grouped with calcium alumina silicates formed by cement hydration. The XRD pattern of cupola slag exhibits high peaks, indicating that the material is crystalline in character and can be utilized as sand. It also shows the presence of other chemical compounds, such as silica, which ranges from 30% to 45%. CS-1 and CS-2 have comparable XRD patterns. However, CS-3 has a somewhat different pattern because of the greater CaO content. Weight loss begins at higher temperatures, which shows that the material is stable at higher temperatures, according to a thermo-gravimetric study. The differential thermal analysis curve of CS-3 indicates that the material remains stable up to a temperature of 600 °C. The physical characteristics of all cupola slag samples show that cupola slag may be utilized to make sustainable building materials because of its lower specific gravity, density, and water absorption.

Sustainable material development techniques help in finding and employing sustainable materials without compromising quality. Composite materials are crucial in structural design, automotive manufacture, and aeronautical engineering. Advanced materials that use reinforcement and filler ingredients must be developed strategically to improve strength and performance. Hence, this study develops hybrid nanocomposites with hybrid nanofillers (MWCNTs and SiO2) and hybrid fibers (basalt and E-glass) and optimizes the competition to maximize elastic behavior and stiffness. Using a hand layup approach, composite samples were made by altering mass fractions of two filler materials (0%, 1%, 1.5%, and 2% by weight) and epoxy matrices (40%, 38%, 37%, and 36%). Shore D hardness and dynamic mechanical analysis (DMA) were used to evaluate the composites. The storage modulus, loss modulus, and damping coefficients are examined using DMA. Specifically, the largest storage modulus is 4.86 × 1010 Pa at 61 °C, while the peak loss modulus is 1.01 × 1010 Pa at 80 °C. The highest damping coefficient is 0.25. Note that 1.5% MWCNTs and SiO2 filler materials independently contribute to the excellent storage and loss modulus. However, an outstanding damping coefficient was achieved without filler materials. The highest achieved shore D hardness value is 88. Filler materials are used to achieve this high hardness.

This piece of research investigates the performance of biodiesel in terms of combustion, consumption, and emission produced from tamarind seeds through the transesterification process. The tamarind seeds are a sustainable source. To enhance engine performance, the three different oxide nanoparticles of Alumina oxide (Al2O3), silicon oxide (SiO2), and magnesium oxide (MgO) were blended as additives with the base fuel. The experiments were done over four blends, namely Diesel (D), tamarind seed oil 20 % diesel 80 % (D80TSOB20), TSO20 with Al2O3 nanoparticles (D80TSOB20A), TSO20 with SiO2 nanoparticles (D80TSOB20S), and TSO25 with MgO nanoparticles (D80TSOB20M). The nanoparticles were blended with fuel by ultrasonication process. and the stability was ensured through measure of Zeta-potential values of nano-fuels (Nanofluid). The fitness of fuel was ensured through characterization of them (including standard fuel of pure diesel). The different fuel blends were tested in a single-cylinder, direct-injection, 4-stroke diesel engine at a constant speed of 1600 rpm with different load conditions like 0 %, 25 %, 50 %, 75 %, and 100 %. The combustion performance, fuel consumption, and emission levels of the different fuel blends were derived from the experimental observations. Experimental results show that a higher HRR and cylinder pressure were reported as 75 J/deg and 77 bar for the D80TSOB20A nanofuel than other fuels tested. Similarly, a higher BTE of 32 % was recorded at full load conditions for the same D80TSOB20A nano-fuel blend, which is a better BTE value than Diesel. Increases the BTE value (all fuels) due to higher fuel rates at higher loads for obtaining higher power. Furthermore, the engine's CO, UHC, NOX, and smoke capacity using tamarind seed oil biodiesel with Al2O3 nanoparticles gives a lesser value than other fuel blends.

This work intended to improve the precision and machining efficiency of Magnesium alloy (Mg–Li–Sr) using Wire electrical discharge machining (WEDM). Mg–Li–Sr alloy is prepared through inert gas assisted stir casting route. Taguchi approach is used for experimental design for WEDM parameter such as pulse OFF time, pulse ON time, wire feed rate, servo voltage and current. L27 orthogonal array is considered to understand the influence of control parameter such as Kerf Width (KW), Roughness of the surface (Ra), Material Removal Rate (MRR). Integration of the CRITIC (Criteria Importance Through Intercriteria Correlation) -WASPAS (Weighted Aggregated Sum Product Assessment) multi-objective optimization method with Artificial Neural Network (ANN) modelling with different network structure for prediction and optimization is a novel approach that significantly improves prediction accuracy and machining outcomes. The developed ANN model with better R2 value of 99.9 % has better ability for prediction while correlated with formulated conventional regression equation. The error percentages identified through confirmation tests for regression and ANN models are Ra - 8.5 % and 3.4 %, MRR - 5.9 % and 2.8 %, KW - 6.7 % and 2.2 % respectively. Optimal output response attained by CRITIC-WASPAS approach yields surface roughness of 4.62 μm, material removal rate of 0.073 g/min and kerf width of 0.388 μm.

Sustainable energy harvesting plays a crucial role in advancing the goals outlined in the United Nations Sustainable Development Goals (SDGs). Solar flat plate collectors (FPC) are integral to sustainable energy harvesting, aligning closely with several Sustainable Development Goals (SDGs) particularly goals 7, 8, 9, 13 and 17. The heat transfer fluid is one of the most important inputs in the solar flat plate collector system. The improvement in the properties of heat transfer fluid significantly improves the collector efficiency. Though Nanofluid is one of the solutions, this investigation is motivated by the use of Al2O3 and SiO2 along Syltherm 800 in the form of nanofluids and hybrid nanofluid to improve the efficiency of FPC. The Syltherm 800 fluid (BF), Syltherm 800/Al2O3 nanofluid (ANF), Syltherm 800/SiO2 nanofluid (SNF), and Syltherm 800/Al2O3/SiO2 hybrid nanofluid (HNF) were tested and analysed to improve FPC performance. The results exemplified that using HNF improved the outlet temperature to the high of 126.9 °C, the average heat absorption of 4734.5 W, and the heat transfer coefficient (HTCO) of 166.3 W/m2K. The maximum average thermal efficiency and exergy efficiency of about 76.3%, and 49.8% respectively. Based on the experimental outcomes of the Syltherm 800/Al2O3/SiO2 hybrid nanofluid is recommended to improve the FPC thermal performances.

Sustainable Energy device's efficiency enhancement by utilizing the waste of tobacco is focused on this research. The carbon nanoparticles derived from the waste of tobacco through a novel approach. The produced Carbon nanoparticles were used to improve the efficiency of sustainable solar collectors in the form of nanofluid. When it comes to improving thermal performance, using nanofluids in evacuated tube collectors (ETSC) is the best option because of the improved thermo-physical characteristics of the working fluid. In this investigation, Syltherm oil was preferred for base fluid to prepare nanofluids with the concentrations of Carbon nanoparticles 0.05 vol%, 0.1 vol%, and 0.2 vol%. Additionally, a spiral coil heat exchanger was introduced to facilitate heat transfer between the nanofluid and water for heating applications. Results indicate that Syltherm/0.2 % carbon nanofluid exhibits higher thermal performance compared to neat Syltherm oil and other nanofluids. Notably, Syltherm/0.2 % carbon nanofluid demonstrated an average peak heat acquisition of 769.1 W and the lowest heat loss of 156.7 W. Furthermore, the thermal and exergy efficiencies are measured at 68.2 % and 42.5 %, respectively. These findings suggested that higher concentrations of carbon nanoparticles lead to increased heat transfer rate and improved efficiency compared to low-concentrated Nanofluids in the ETSC system.

Glycerol is a key by product of the biodiesel extraction process. The rapid growth of biodiesel establishments will continue to result in glycerol oversupply. Purified glycerol can be used as an antifreeze in automotive radiators since it has a freezing point of −30 °C and a boiling temperature of 160 °C. Heat transfer by automotive radiators is important in optimizing engine fuel economy. The goal of this research is to employ purified bio glycerol as a heat transfer fluid and a coolant in vehicle radiators. The proposed water mixed bio glycerol convective heat transfer rate is experimentally compared to standard ethylene glycol. The rate of heat transfer by bio glycerol is determined by passing the fluid through an automobile radiator test apparatus. For this objective, an Automobile Radiator test rig is devised and built. Experiments with distilled water blended bio-glycerol in volume ratios of 50:50, 70:30, and 60:40 were carried out, and the test findings revealed that the 70:30 water bio glycerol volume ratio created a higher heat transfer rate when compared to the water ethylene glycol mixture.

Aluminium alloy is one of the widely used lightweight materials and is utilized in the production of a wide variety of components. The AA8026 properties enhancements through optimizing the synthesizing parameters through the Taguchi route is the aim of this investigation. Thereby establishing control over the stir casting process to alter the wear and microhardness of AA8026 aluminium alloy by hybrid reinforcement. There were 16 samples of AA8026 + Zr2O3/TiO2 composite prepared as per Taguchi L16 orthogonal array. The process parameters varied four levels such as stirring time from 15 min to 30 min, quantity of reinforcement from 3 wt% to 12 wt%, molten temperature from 750 °C to 900 °C, and stirring speed from 450 rpm to 600 rpm. The fabricated samples are investigated for microhardness and wear properties. From the analysis, it is inferred that stirring speed is highly influenced by wear and microhardness properties. The results exemplify that 0.086 mm3/m was minimum wear and 180.2 HV was maximum microhardness achieved.

This research explores into analyzing the mechanical characteristics of banana fiber and ramie fiber composites bolstered with silicon carbide (SiC) nanoparticles. The integration of SiC nanoparticles aims to amplify the mechanical robustness and endurance of these natural fiber composites. Through water absorption assessments, we evaluated the composites’ resistance to moisture. In addition, we assessed the mechanical performance via hardness, impact, and three-point bending tests. The results indicate a significant enhancement in the mechanical attributes of both banana and ramie fiber composites due to SiC nanoparticle inclusion. The results reveal a notable enhancement in mechanical characteristics following the incorporation of SiC nanoparticles. Notably, composites containing 8% SiC nanoparticles demonstrated the highest hardness value of 102 HV and the lowest water absorption percentage of 1.5%. In addition, these composites exhibited a superior flexural strength (80 MPa) and modulus of elasticity (4.3 GPa), alongside a maximum impact energy absorption of 18 J. These findings underscore the beneficial influence of SiC nanoparticles on the mechanical properties of the composites, including increased strength, reduced water absorption, enhanced hardness, and improved impact resistance.

Geotechnical designs and analyses of earth structures and foundations exclusively involve the assessment and consideration of unsaturated soil shear strength. The laboratory testing equipment and methods for predicting the unsaturated soil shear strength are complicated and more expensive. The experimental program attempted to involve undrained triaxial and filter paper for evaluating the unsaturated soil shear strength of identically compacted clayey soil. This study undertakes a comparison of shear strength in clayey soil under undrained loads, examining both saturated and unsaturated conditions. A 60 kPa air entry suction value is the key point at which linearity between the unsaturated shear strength parameter Øb and effective friction Ø′ with 15° linear slopes turns to non-linearity. Unsaturated shear strength increased by 22.76% in optimally wet conditions, 52.68% in optimum conditions, and 77.81% in optimally dry conditions as compared to saturated shear strength. This study utilizes an artificial neural network (ANN) to predict clayey soil’s unsaturated shear strength, finding that the optimal ANN configuration (2-5-1 topology, Levenberg–Marquard optimization, and logsig transfer function) achieved high reliability with a correlation coefficient (R) of 0.9289 and mean square error values of 2.22, 7.12, and 3.012 for training, testing, and validation, respectively. This experimental investigation improves our understanding of clayey soil shear strength and emphasizes the importance of saturation and moisture content in geotechnical assessments under undrained loading conditions.

The purpose of this study was to see how silane treatment affected the tensile and impact strength of composites constructed from jute/kenaf/glass fibers with a nano-graphene filler and to investigate the impact of major treatment factors, such as silane concentration, immersion duration, and nano-filler, on composite characteristics. To conduct systematic trials and improve these variables, the Response Surface Methodology (RSM) with central composite designs was used. To precisely forecast the tensile and impact strength of the nano-composite following silane treatment, quadratic models were built. By changing the silane concentration, immersion period, and nano-filler, they discovered ideal conditions for increasing tensile strength. The best ranges for silane concentration and immersion duration were discovered to be 15 wt. % and 30 min, respectively. Given the conditions, the composite impact strength increased by 51% and its tensile strength improved by 22% as compared to the values achieved from RSM optimization. These results highlight the practical importance of silane treatment, especially in improving tensile and impact strength and strengthening the interfacial adhesive characteristics of organic fibers and polymer matrices.

Due to their excellent mechanical features, concrete-filled steel tube (CFST) columns are usually employed in high-rise constructions; it is also significant to consider fire safety while designing buildings. The fire-resistance performance of fibre-reinforced CFST columns is superior to that of unreinforced CFST columns under identical conditions. The post-peak behavior of CFST columns can be enhanced by incorporating fibres to reduce the brittleness of concrete. The research on this topic is limited and considerable research gaps are available in the literature. This research examines the structural performance of steel fibre-reinforced concrete (SFRC) filled steel tubes exposed to ambient and elevated temperatures. For this purpose, fourteen CFST columns were investigated and separated into two groups. The first group of CFST columns was filled with three diverse grades of conventional concrete with and without fibres, which was subjected to ambient temperature. The second group is identical to the first group except all columns are exposed to a temperature of 1050 ℃. The parameters examined were the mechanical properties of concrete, load-carrying capacity, load-deflection response, axial load, and axial strain and ductility ratio. Furthermore, using different codes, a comparison was made for the column's empirically determined load-carrying capacity with the theoretical value. Besides, the morphological characteristics of concrete were examined using energy dispersive spectroscopy (EDS), scanning electron microscope (SEM), Fourier-transform infrared spectroscopy (FTIR) and X-ray powder diffraction (XRD). The findings show that the SFRC-filled column has superior structural qualities when subjected to elevated temperatures. At high temperatures, calcium hydroxide decomposes, and the steel tube yields, causing the column to fail by premature local bucking.

The optimization of resources and reduction of costs through efficient inventory management are paramount to organizational success. This study undertakes a comparative analysis of two distinct forecasting methodologies, Exponential Smoothing (ES) and Gradient Boosting (GB), within the framework of materials forecasting aimed at inventory minimization. Our study introduces innovation by methodically scrutinizing these approaches within a unified framework, shedding light on their merits and shortcomings. This comparative analysis gives practitioners a practical roadmap for the optimal forecasting strategy to streamline inventory management operations. Methodologies are evaluated based on their efficiency in predicting material demand, encompassing metrics such as accuracy, computational efficiency, and suitability across various inventory management scenarios. Response surface methodology entails refining processes to modify factorial variables’ configurations to attain a desired peak or trough in response. The SPSS results show that the ES method has 43.20%, surpassing the accuracy of the inventory optimization model, which stood at 65.08%. The response surface methodology results show that 45.20% profit was achieved for the variable and operational cost process parameters. This research seeks to unveil the traces of each method, facilitating decision-makers in selecting an optimal forecasting strategy tailored to their specific inventory management requirements. The analysis shows that the ES method surpasses the accuracy of the GB machine learning for material forecasting to minimize inventory.

Breast cancer is a medical condition characterized by the uncontrolled proliferation of cells in the breast tissue. Breast cancer can originate from various parts of the breast, and methods such as Breast ultrasound, Diagnostic mammogram, Breast magnetic resonance imaging, and Biopsy are currently used for its diagnosis. However, these methods have certain limitations, and their size can be a hindrance. To overcome this, low-power, flexible antennas can be designed for bio-communication between medical equipment and external instrumentation. Flexible and wearable antennas have advantages such as affordability, ease of fabrication, and high gain. In this article, a microstrip patch antenna operating at 2.45 GHz and made of polyamide material is designed using High Frequency Structure Simulator software. The simulation results show the patch antenna has a gain of 1, −14.81 dB return loss at 2.45 GHz based on |S11| ≤ −10 dB. The directive radiation pattern with axial ratio of 63.39 dB and voltage standing wave ratio ≤3. Furthermore, the hardware development of proposed antenna with polyamide substrate provides the resonance frequency nearing to simulation results as 2.318 GHz with return loss of −28.19 dB. Based on mathematical analysis, simulation and hardware results, the proposed antenna is a superior option for breast cancer detection.

This study primarily focused on assessing the feasibility of utilizing textile effluent sludge (TES) in the creation of sustainable building materials, demonstrating the possibility of using this material and eventually generating new market opportunities. The physical, chemical, and mineralogical characterization of textile sludge was carried out after preliminary crushing and grain-size sorting. Five samples collected from the effluent treatment facility of textile industries situated in the Vidarbha region of Maharashtra, India, underwent characterization to explore their potential innovative applications in the construction sector. Various physical tests, such as specific gravity, density, water absorption, and sieve analysis, were performed on these samples. Chemical characterization was carried out using x-ray diffraction, x-ray fluorescence, a field emission scanning electron microscope coupled with energy-dispersive x-ray spectroscopy, Fourier transform infrared spectroscopy, and thermogravimetry/differential thermal analysis. The results obtained demonstrate the potential of TES in the creation of sustainable building materials. In addition, the mix proportion of TES was further optimized using LINGO software to meet the chemical specifications required for cement.

The present study employs the S/N ratio and multiple regression analysis to evaluate the mechanical properties of laterized concrete with fly ash and steel fiber. The study considers compressive, split tensile, and flexural strengths as responses at 7 and 28 days. The Taguchi L16 (4 *4) orthogonal array experimental design is applied to optimize the performance of mechanical properties of concrete by using the design of experiments. The replacement of 25% of the granite aggregate with laterite aggregate resulted in a 12.44% increase in the strength of M30-grade concrete after 28 days. The composite's compressive strength decreases when the replacement level exceeds 25%. The split tensile and flexural strengths of the composite showed an increase of 14.54% and 16.93%, respectively, compared to the control mix. Furthermore, the test outcomes for durability factors like drying shrinkage and rapid chloride penetration in steel fiber reinforced fly ash-based lateritic concrete mixes with a 50% replacement rate adhere to the prescribed standards, verifying their designed performance over the designated structural lifespan. However, after multiple regression analysis, it is found that the error related to the correlation between experimental and analytical value strength is about 5%. Microstructural studies of the ideal mix were performed at 28 days to further understand the morphology of the laterized concrete containing fly ash and steel fiber.

While urbanisation has numerous advantages, it causes greater risks to the environment and human health because of the release of heavy metals, various organic and inorganic contaminants, personal care products, and pharmaceuticals. Though several actions are being taken daily to lessen the release of harmful substances, there is still an immediate need to find a suitable solution to protect the environment. Nanotechnology has multifaceted applications, and there is extensive evidence of the emerging applications of nanoremediation, especially for soil and water pollution. Iron nanoparticles showed outstanding removal efficiency towards hexavalent chromium (100 %). Likewise, several publications on soil and water remediation employ nanomaterials based on metals, carbon, and polymers. However, most of the previously conducted works present the key nanoremediation results without depicting each nanomaterial's advantages and disadvantages. Hence, this work critically reviews the pros and cons of each nanomaterial with a special focus towards novel approaches using green synthesised nanomaterials that are completely eco-friendly and hence preferred for the removal of various contaminants without producing harmful effects. However, some bottlenecks exist in fully implementing the green nanoparticles for Nanoremediation. Thus, the review discusses the limitations of green nanomaterials that need to be addressed soon to maintain environmental sustainability. Finally, this review presents opportunities for future work in assessing the eco-safety of each nanomaterial that boosts the further utilisation of nanotechnology in the sustainable remediation of contaminated soil and water.

At present, composites have brought materials to a new era with superior characteristics. The proper selection of materials with optimal processing conditions is one of the prime factors in determining the efficiency of composites. This article introduces a new composite by fabricating a copper matrix with reinforced AlCoCrCuFe High Entropy Alloy (HEA). The ultimate theme of this research is to forecast the wear rate of the Cu–AlCoCrCuFe HEA composite. Using the pin on the disk, the wear test is carried out on varying combinations of parameters. Then Taguchi analysis is carried out to get the precise fit parameters. The wear rate mainly depends on the percentage of HEA, sliding distance, sliding velocity, and the load applied. Correlation analysis is performed to determine the exact parameter determining the optimal wear rate and the coefficient of friction. After feature extraction, the parameters are optimized, and the neural network regression is given the optimized parameters. The network has been trained, and predictions are made using it. The model successfully predicts the wear rate, as evidenced by the declining RMSE of 0.28 and rising R2 values up to 92%. The analysis shows a significant decline in the wear rate with HEA additions. In addition, the wear rate increases with a rise in load, sliding velocity, and sliding distance.

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

To create a hybrid material of ramie and flax fibers with silicon carbide (SiC) nanoparticles, it should have better thermal and mechanical properties. Across industries, sustainable materials are growing in demand for high performance, which is the impetus behind this study. A sustainable future is more about contributing to sustainable alternatives like ramie and flax fibers made from traditional synthetic materials. A key area focused on increasing strength, temperature resistance, and stiffness will be the addition of SiC nanoparticles to hybrid fibers. The research process carefully mixes the ramie and flax fibers with SiC nanoparticles to achieve optimal flexibility, thermal stability, and balance of strength. Hybrid material is examined using characterization techniques such as mechanical testing and scanning electron microscopy to determine the functioning of the microstructure. The possibility of composite material recycling, the impact of hybrid fibers, and accounting for natural fibers for sustainability are considered possible in this research. It should be innovative material development that has significant implications from the findings that meet environmental sustainability goals and satisfy performance requirements.

Poor planning and scheduling increase buying, storage, and obsolescence expenses. Material shortages increase labor, machine optimum time, etc. Industrial raw materials, semi-finished items, spares, and consumables have distinct consumption patterns, reorder points, purchase lead times, quantity limits, discounts, etc. To save money, machine learning predicts demand and prepares materials. This study employs ARIMA or Support Vector Machine (SVM) machine learning-based forecasting approaches to forecast materials for less inventory. Feature engineering eliminates seasonality, time series, and external demand and ignores data irregularities, missing figures, and disparities. This approach needs to adapt traits to factors, separate test and training data, and consider many future models to represent the best forecasts. Forecast reliability and consistency were examined for each model. Inventory management systems were evaluated for computational complexity and installation ease and found implementation issues. Both models’ input data and resilience were examined using sensitivity analysis. Accurate prediction SVM and ARIMA predict material demand differently. Meaningful statistics show the optimal model. Performance differences between SVM and ARIMA enhance model selection. Thinking about the execution of high inventory system integration and computational complexity, response surface methodology chooses factorial variables with the highest or lowest responses. Analysis of variance, factor analysis, and effect modeling expansions demonstrated for the response.

The current research investigates diesel and simarouba biodiesel blends (10%, 20%, & 30% by volume) in conventional and Low Heat Rejection (LHR) diesel engines, each rated at 4.4 kW. While optimization techniques like Response Surface Method and Taguchi have been extensively studied, the impact of LHR and optimization on LHR engine performance and emissions is rarely explored. Converting the conventional engine to LHR involved applying 300 μm of stabilized zirconia to the piston crown to enhance combustion efficiency. Performance and emissions were analyzed at rated injection pressure (200 bar) and timing (23° before top dead center - btdc). Experiments were continued on LHR engine by varying injection timings (advancing - 26°btdc and retarding - 20°btdc). Advanced injection timing showed significant improvement in performance of Low heat rejection engine. MINITAB statistical tool is used to optimize engine performance using Response Surface Method. The 20% blend showed improved performance in both engines. The optimum values for Low heat rejection engine responses are 26.8%, 0.32 kg/kW-h, 0.018%, 59.59 ppm, and 1419.03 ppm for brake thermal efficiency, brake specific fuel consumption, carbon monoxide, and unburnt hydrocarbons, respectively. Confirmation experiments aligned well with model predictions, indicating the potential of LHR engines to enhance thermal efficiency and reduce emissions.

The extensive study conducted has revealed that the mechanical and other characteristics of hybrid composites are greatly influenced by several aspects, such as the kind of fibre reinforcement, nanofillers, matrix materials, and the manufacturing methods employed. In the context of structural applications, the incorporation of Multi-wall Carbon Nanotubes (MWCNTs) as fillers in E-Glass fibres had a significant impact on the rapid alteration of mechanical properties, particularly strength, in the composites. This study aimed to create hybrid nanocomposites by including multi-walled carbon nanotubes (MWCNTs) and silicon dioxide (SiO2) as filler materials, Basalt/E-glass as fibres, and epoxy with hardener as the matrix. This study utilises statistical methods, specifically the Taguchi L16 Orthogonal Array (OA), to analyse the impact of various operating factors. The parameters selected for this study include MWCNTs/SiO2 fillers at varying weight percentages (0 %, 1 %, 2.9 %, and 3 %), an epoxy matrix with decreasing weight percentages (40 %, 39 %, 38 %, and 37 %), compression pressures ranging from 5 MPa to 35 MPa, and moulding temperatures ranging from 40 °C to 100 °C. The mechanical parameters being examined are the tensile strength and erosive wear. The utilisation of the hand layup and compression moulding technique achieved the development of these composites. By optimising the moulding temperature, significant improvements were achieved in the tensile strength, which reached 184.38 MPa. Similarly, reducing the percentage of fillers resulted in a substantial decrease in erosive wear, with a value of 95.06 mg/kg.

Vehicle emissions are responsible for about 30% of all air pollution in the world. Vehicle emissions can be significantly reduced through the use of selective catalyst reduction (SCR). The present work emphasizes the impact of thermal barrier-coated pistons on diesel engine performance as well as emission qualities. A Ni–Cr–Al–Y bond coat was applied to the tested engine piston that was 50 microns thick and a top coat that was 250 microns thick. These coatings were applied using the plasma spray technique to a combination of 2 mol. % of Gadolinium oxide (Gd2O3), 2 mol. % of Neodymium oxide (Nd2O3), 3 mol. % of Yttria (Y2O3), and continuing 93 mol. % of Zirconia (ZrO2). In a 4-stroke, 1-cylinder diesel engine, the testing was carried out utilizing diesel, Mahua, and Jatropha fuels with and without coating. The selective catalytic reduction technique was employed in the current test to reduce NOx emissions. The findings of this analysis indicate that the brake thermal efficiency of an insulated piston engine improved by 3.9%, and when JB 100 was chosen as the fuel, the insulated piston reduced brake-specific fuel consumption by 3.5% in comparison to the normal piston. In engines coated with SCR, hydrocarbon emissions were lowered by 20.1%, while carbon monoxide emissions were dropped by 13.4%. In comparison to the baseline engine, the oxide of nitrogen emissions were reduced by 39.1%.

Electric Discharge Machining (EDM) is essential for shaping and cutting tool steel. EDM’s precision in machining difficult materials and tool steel characteristics are well known. EDM efficiency requires reliable performance measurement parameters. The physical shape and mobility of the electrode tool are critical in EDM research. Layer machining is an advanced method that removes material in a sequential manner to produce intricate 3D shapes in tool steel and several other materials. The improvement in layer machining methods with precise toolpath algorithms, adaptive layer thickness management, and real-time monitoring systems is required to maximize precision and efficiency. Response surface methodology, the artificial neural network, and other techniques are necessary to optimize EDM operations and maximize performance. Many researchers experimented with electrode shapes and movement patterns to enhance the removal of material and the quality of surfaces. Investigation of complex electrode structures and innovative tool path strategies has been performed in previous studies. It was very difficult to consider various factors during the EDM operation; hence, the present review summarizes the positive outcomes of previous research. The review emphasizes optimizing pulse duration and discharge current to improve EDM efficiency. The present comprehensive review discusses research on EDM in three main areas: electrode tool geometry and motion, tool steel layer processing, and factors for measuring EDM performance. The objective of the present review is to focus on measuring material removal rates, surface roughness, tool wear, and energy usage. The present review concludes that EDM is crucial to machining tool steel and cutting tool materials. Integrating and hybrid machining technologies can improve performance, and improved optimization techniques are crucial. It also recognizes knowledge gaps and explores new frontiers in this dynamic field.

This study’s primary objective is to experimentally investigate the flexural and impact performance of composites composed of hybrid basalt/E-glass fiber-reinforced epoxy infused with multiwalled carbon nano-tubes (MWCNTs) and nano-silica (SiO2) in compliance with ASTM D790 and ASTM D6110 specifications. Recently, manufacturers considered using basalt fiber-based composites for various structural applications due to their excellent mechanical properties, high stiffness, and high strength-to-weight ratio. Each composite laminate was made by hand layup techniques and filled with equal proportions of SiO2 and MWCNT nanoparticles in different weight percentages, such as 0%, 1%, 2%, and 3%. The composites were made by using a symmetric stacking sequence of B/GG/BB/GG/BD/GG/B fibers. MWCNTs and SiO2 were evenly dispersed throughout the epoxy matrix with the assistance of an ultrasonicator and magnetic stirrer. The composite containing 2% fillers has an increased flexural strength by 20% from 307 to 378 MPa and flexural modulus by 30% from 11.181 to 15.901 Gpa, as well as an increased Charpy impact resistance by 45% from 236 to 418 J/m, compared with the composite without fillers. The interfacial interactions between the epoxy matrix, particles, and fibers significantly influenced the composite laminates’ flexural and impact characteristics. The accumulation of particles in the epoxy caused by the 3% fillers reduces the flexural strength and flexural modulus and impacts the performance due to the inadequate interfacial contact between the fibers and the epoxy matrix.

The essential objective of this work was to investigate the effect of silane treatment on the tensile and impact strength of composites made of jute/kenaf/glass fibers with the TiO2 nano-filler. This study also sought to optimize the factors connected with this treatment method. To develop and evaluate trials, the researchers used Response Surface Methodology (RSM) with central composite designs, emphasizing the optimization of silane concentration, immersion time, and nano-filler content. The researchers were able to effectively create quadratic models to forecast the tensile and impact strengths of the nano-composite throughout the silane treatment procedure. The adjustment of silane concentration, immersion duration, and nano-filler content discovered the best conditions for obtaining maximum tensile strength. The optimal values for silane concentration and immersion time have been identified to be 15 wt. % and 30 min, respectively. Considering these conditions, the composite’s tensile strength increased by 32.13% and its impact strength improved by 8.34% above the lowest values achieved from RSM optimization. The results demonstrate the practical value of silane treatment, notably in boosting tensile and impact strengths while enhancing interfacial adhesive among natural fibers and polymer matrices.

Aluminum alloy and its allied composites are gaining popularity and find their exclusive application in all industries for more than two decades. Due to their increased usage, Al metal matrix composites were experimented by reinforcing them with variety of particles. The current study attempted the use of titanium carbide (TiC) and boron nitride (BN) as reinforcing materials for the Al7050 alloy through the stir casting technique. The percentage of reinforcement of TiC was kept constant as 8% by wt., and the BN was varied as 2% and 3% by wt. Mechanical properties, such as compressive strength, tensile strength, flexural strength, impact energy, microhardness (Vickers), the microstructure were studied using an optical microscope and by corrosion tests using polarization techniques. The results conclude that the Al hybrid composite with 8% TiC and 3% BN provides superior performance.

Finding innovative, stable, and environmentally acceptable perovskite (PVK) sunlit absorber constituents has developed a major area of study in photovoltaics (PVs). As an alternative to lead-based organic-inorganic halide PVKs, these PVKs are being researched for use in cutting-edge PVK solar cells. While there has been progress in this field as of late, there are still several scientific and technical questions that have yet to be answered. Here, we offer insights into the big picture of PVK toxicity/instability research, and then we discuss methods for creating stable, non-toxic PVKs from scratch. It is also believed that the processing of the proposed PVKs, which occurs between materials design and actual devices, poses novel challenges. PVK PVs that are both stable and ecologically benign can be created if these topics receive more attention. It is interesting to note that although perovskite solar cells (PSCs) have impressive power conversion efficiency, their commercial adoption is hindered by lead toxicity. Lead is a hazardous material that can cause harm to humans and the environment. As a result, researchers worldwide are exploring non-toxic lead-free photovoltaics (PSCs) for a sustainable and safe environment. To achieve this goal, lead in PSCs is replaced by non-toxic or less harmful metals such as tin, germanium, titanium, silver, bismuth, and copper. A study has been conducted that provides information on the characteristics, sustainability, and obstacles of replacing lead with these metals in PSCs. The paper also explores solutions for stability and efficiency issues in lead-free, non-toxic PSC commercialization, including altering manufacturing techniques and adding additives. Lastly, it covers the latest developments/future perspectives in lead-free perovskite solar cells that can be implemented in lead-free PSCs.

This study explores the thermal and mechanical properties of a composite material formed by blending kenaf and basalt fibers with titanium oxide nanoparticles. To assess its performance under various conditions, we conducted thermogravimetric analysis (TGA) and hardness, sorptivity, and water absorption tests. During experimentation, composite samples were created using kenaf and basalt fibers as the reinforcing matrix and TiO2 nanoparticles as the modifier. Initial findings suggest that the addition of TiO2 nanoparticles enhances the mechanical properties and water resistance of the kenaf and basalt fiber composite. Samples reinforced with TiO2 demonstrated superior hardness and lower water absorption, i.e., KBT2 exhibited a hardness of 92 Hv and a water absorption rate of 6.4%. The value of 0.1% for KBT2 from the sorptivity investigation was promising, indicating its potential applicability in moisture-sensitive environments. In addition, TGA results show that KBT2 exhibited 0%, 2.1%, 4.7%, and 8.3% weight loss, showing improved thermal stability and high resilience to higher temperatures. In conclusion, the development of a novel material combining kenaf and basalt fibers reinforced with TiO2 nanoparticles presents a promising eco-friendly, durable, and lightweight option for engineering applications. Realizing the full potential of this composite material necessitates further characterization and optimization research.

The purpose of this study is to examine the mechanical properties of components produced through the Response Surface Methodology for polylactic acid, utilizing the Fused Deposition Modeling 3D printing technique. Polylactic acid is a commonly employed biodegradable polymer, making it a desirable substance for diverse applications. This study involves carrying out experiments to vary process printing parameters like layer height or thickness, part orientation, and infill density. The values of these parameters were obtained using a Response Surface Methodology Box–Behnken experimental design. The mechanical performance of the 3D Printed polylactic acid fabricated was assessed by evaluating their flexural and tensile strength. The test samples for measuring tensile and flexural strength are fabricated according to American Society for Testing and Material standards. The findings suggest that higher strength is achieved when using increased layer height and infill levels. The experimental results indicated that specimens with a filling ratio of 80% exhibited greater tensile strength, while the flexural strength of samples with 50% infill was observed to be higher. Regression analyses and multi-optimization techniques were employed to predict the experimental results. This study provides valuable insights that can significantly impact various industries. Our research on the complex interactions between process variables and mechanical properties has major implications for improving high-strength component manufacturing. As demand for dependable and efficient 3D-printed materials rises, our discoveries improve material design and manufacturing methods, making a significant contribution to the field.

Natural fiber composites are often sought after in industries, such as automotive and aerospace, due to their low density compared to traditional synthetic composites. Sisal and jute are renewable and biodegradable resources, making them attractive from a sustainability standpoint. The effect of carbon nanotube (CNT) insertion of a composed sisal and jute fiber composite material on the thermal and mechanical characteristics is investigated in this study. The primary objective of this research is to determine the exceptional mechanical strength and heat resistance that allows the composite-filled filling CNT to perform better than others. The methodical experimental approach was used to evaluate the effect of sisal and jute matrix and different amounts of CNTs’ mechanical and thermal properties. Thermal behavior is found by thermogravimetric analysis, and mechanical performance is used to qualify tensile strength, flexural strength, and impact resistance. The result suggests that CNTs may have reinforcing properties and significant tensile and flexural strength improvement. Impact resistance improved and increased the toughness of the composite material. The 7% CNT composite exhibited improvements in tensile strength of 63.9% and flexural strength of 46.6%, suggesting the synergistic reinforcing effect of CNTs. The high temperature from the use of need resistance shows promise for the composite material based on the tests of its capability for heat absorption and thermal stability. Various technical contexts are potentially useful in focusing on environmentally friendly material creation that exhibits exceptional thermal and mechanical properties.

The automotive, biomedical, and aerospace industries are among those with a rising need for lightweight materials with enhanced mechanical and tribological qualities. Composites based on magnesium alloys have attracted interest because of their excellent strength-to-weight ratio and promise to improve component performance. Magnesium (Mg) alloy-based composites find applications in sports and leisure equipment, aerospace, biomedical implants, and more. The research outlined here serves a critical need in the field of materials science and engineering, particularly regarding the development of advanced magnesium (Mg) alloy-based composites. In this study, we have created a new aluminum composite using the AZ31 alloy mixed with 5% boron carbide (B4C) and 5% molybdenum disulfide (MoS2) as reinforcement through a powder metallurgical technique. The magnesium alloy contains 3% aluminum and 1% zinc. Our research aims to understand the mechanical and tribological behaviors and the impact of Electrical Discharge Machining (EDM) process parameters on AZ31 magnesium alloy. We need to modify these properties for various applications. Many industrial researchers have studied the machinability of magnesium alloys using EDM. We conducted wear tests on AZ31 alloy reinforced with both B4C and MoS2 in altered quantities using a pin-on-disc setup. The outcome displays that the wear resistance of these composites is considerably better matched to other magnesium matrix composites (MMCs). We also measured various densities of the hybrid composite, including apparent density, green density, and sintered density, which were found to be 0.839, 1.495, and 1.504 g/cm3, respectively—better than other composites. In addition, the hybrid composite exhibited a substantial increase in micro hardness, reaching 22.012 HV, indicating improved wear resistance of the material. Comparatively, low density, minimum wear profile, and maximum hardness were recorded for the sample of AZ31 + 5%MoS2 + 5%B4C. The influence of EDM parameters was discussed.

Cathode Ray Tube (CRT) panel glass with higher silica content has a potential to be used as reinforcement material in magnesium matrix composites. The present study aims to formulate a wear resistant magnesium hybrid composite by reinforcing silica rich E-waste CRT panel glass powder (0, 10 and 20 wt. %) and boron nitride (0, 2 and 4 wt. %) solid lubricant processed through powder metallurgy route. Composition of the CRT panel glass powder is identified through EDS and XRD analysis is performed for both base material and reinforcements. The developed green MMC shows increased hardness (∼53 %), porosity (∼7 %) and density (∼7 %) with respect to addition of CRT reinforcement. Porosity of hybrid composite increased vastly to 9.2 % when the CRT and BN content are increased to 20 % and 4 % respectively. A slight increment in (10 %) hardness is observed for 2 % addition of BN with Mg and Mg/CRT composite whereas hardness of the Mg and Mg/CRT MMC decreases with further addition of BN. The wear resistance of the composite enhanced with raise in CRT and BN content whereas the co efficient of friction decreases with increases in BN percentage. Corrosion behaviour of Mg is also analysed and found enhanced with reinforcement addition. It is identified from the results that the addition of CRT% up to 10 % yields significant results whereas further increase in CRT % to 20 provides only minor enhancements in properties. Further, it is suggested to restrict the addition of BN to 2 %.

Aluminium alloys are indispensable in all manufacturing industries, particularly mechanical engineering. The objective of this study is to enhance the mechanical, wear, and corrosion properties of aluminium alloy AA5083 by incorporating nanoparticles as reinforcements, thereby creating hybrid aluminium nanocomposites. The base material utilised in this study was AA5083, with the reinforcement nanoparticles selected as Carbon nanotubes (CNTs) and Molybdenum disulfide (MoS2) at concentrations of 5 % and 3 % respectively. Nanocomposites were fabricated using the Powder Metallurgy (PM) technique, employing specific contrasting parameters including Ball mill speed (280, 320, 360, and 400 rpm), Mixing time (20, 30, 40, and 50 min), Compaction pressure (300, 350, 400, and 450 MPa), and Sintering time (2, 3, 4, and 5 h). The operating parameters of powder metallurgy were assessed using the Design of Experiments (DOE) L16 Orthogonal Array, and their corresponding outcomes were analyzed. The results revealed that there is a significant correlation between the mixing time and microhardness of the nanocomposites. The wear and corrosion reduction rates had a significant impact on the sintering time parameter. The present study determined that the highest recorded microhardness value was 136 VHN, while the lowest observed rates of wear and corrosion were 0.192 mm3/m and 0.00081 mm/year, respectively. Scanning electron microscopy (SEM) images clearly depicted the presence of various structural defects, such as pits, cracks, delamination, voids, and cusps, in the wear and corrosion samples that exhibited reduced strength.

The development of high wear/erosion resistance materials for defense application is found to be a challenging task among the researchers. The current study investigated the reinforcement effect of TiC and Rice husk ash (RHA) on Al6061 hybrid composites made using a two-step ultrasonic-assisted stir casting process. Four distinct Al6061 hybrid composites were created with varying TiC weight percentages (3, 6, 9, and 12 wt.%). RHA weight percentage was kept constant at 3%. The solid particle erosion test was carried out on each composite using a Taguchi L27 (33) orthogonal array. Experiments were conducted by varying the erodent particle impact angle, erodent velocity, and erodent discharge rate. The effect of each parameter on the erosion rate of the TiC/RHA Al6061 composite was discussed. The composite containing 12% TiC and 3% RHA showed improved erosion resistance. It was found that on increasing the TiC addition in the composite resulted in an increase in erosion resistance. SEM images were used to examine the surface failure on the composites caused by erodent impact.