In ongoing decades, material researchers and scientists are giving more consideration towards the improvement of biobased polymer composites as various employments of items arranged by natural fibres and petrochemical polymers prompt natural awkwardness. The goal of this review paper is to provide an intensive review and applications of the foremost appropriate commonly used biodegradable polymer composites. It is imperative to build up the completely/incompletely biodegradable polymer composites without bargaining the mechanical, physical, and thermal properties which are required for the end-use applications. This reality roused to create biocomposite with better execution alongside the least natural effect. The utilization of natural fibre-reinforced polymer composites is concerned with the mechanical properties that are highly dependent on the morphology, hydrophilic tendency, aspect ratio, and dimensional stability of the natural fibre. With this in-depth consideration of eco-friendly biocomposites, structural application materials in the infrastructure, automotive industry, and consumer applications of the following decade are attainable within the near future.

The global boom in the importance of renewable energy (RE) resources has accelerated the transition from traditional energy sources to sustainable alternatives. Selecting an optimum RE power plant is a complex task influenced by geographical factors, climatic differences, and regional peculiarities. Furthermore, incorporating numerous conflicting criteria, such as technical, economic, environmental, and social variables, complicates the decision-making process, rendering it a multi-criteria decision making (MCDM) problem. This research aims to solve the current complicated decision-making process by utilizing the CRITIC integrated EDAS-CODAS-CoCoSo MCDM framework, specifically focusing on selecting the most suitable RE power plant to be developed in the Indian environment. Within the MCDM system, a case study was analyzed using five unique RE sources, namely geothermal, solar, wind, hydro, and biomass. The evaluation was based on six competing elements classified into three broad categories: mechanical features, economic considerations, and environmental implications. The CRiteria Importance Through Intercriteria Correlation method (CRITIC) was initially used to determine the objective criteria weights, which were subsequently integrated into the Evaluation based on Distance from Average Solution (EDAS), COmbinative Distance-based ASsessment (CODAS), and Combined Compromise Solution (CoCoSo) methodologies to determine the preference ranking order of the alternatives. The data indicate that hydroelectric power plants are the most ideal alternative, followed by solar electricity, while geothermal and biomass are the least favorable among the considered RE sources. This study analyzes the rankings of the three deployed MCDM models and checks their stability using a sensitivity analysis. Another feature of the final results shows that EDAS and CoCoSo perform slightly better than CODAS in terms of ranking efficiency. The in-depth study of these findings provides crucial insights and direction for policymakers and stakeholders in India’s renewable energy sector. Clinical trial number: Not applicable.

In this study, Al-4.5Cu alloy was reinforced with varying weight percentages of SiC particles (2%, 4%, 6%, and 8%) to create metal matrix composites via the stir casting method. The formation of intermetallic compounds was confirmed through energy dispersive spectroscopy and x-ray diffraction analysis. This article compares the performance of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models in predicting the Metal Removal Rate (MRR), tool wear rate, and surface roughness in the die-sinking electro-discharge machining (EDM) process of the ex-situ developed Al-4.5%Cu–SiC composites. The study considers three machine parameters—pulse on time (TON), pulse off time (TOFF), and current (I)—along with the weight fraction of SiC particles as input variables for the models. Both ANN and GEP models demonstrated high predictive accuracy for the EDM performance metrics, with correlation coefficients (R) ranging from 0.973 68 to 0.980 65 for the ANN model and 0.980 11 to 0.982 59 for the GEP model. Notably, the GEP model exhibited superior predictive capability, as evidenced by its higher correlation coefficients and lower root mean square error, indicating greater effectiveness in predicting the EDM process outcomes than the ANN model.

The imperative shift toward sustainability has driven contemporary scholars to explore the lubricating and cooling properties of vegetable oils in traditional metal-cutting processes. Palm oil, as an environmentally conscious derivative, emerges as a preferable option for the base fluid in Minimum Quantity Lubrication (MQL). However, its high viscosity impedes fluidity, limiting industrial applicability. In contrast, sunflower oil offers superior lubricating qualities and flowability. Consequently, efforts have been directed toward enhancing the lubricating efficacy of palm oil. Six blends of palm and sunflower oils (ranging from 1:0.5 to 1:3) were utilized as MQL fluids, followed by evaluations of machining outcomes, including average surface roughness, specific cutting energy, and tool wear. In addition, an integrated Shannon’s Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework was employed to determine the optimal volume ratio of the palm–sunflower oil blend. The TOPSIS analysis confirmed that the 1:2 ratio yielded the most favorable outcomes. Subsequent comparative analysis demonstrated that this optimal blend resulted in reductions of 16.79% and 14.92% in surface roughness, 11.82% and 10.98% in specific cutting energy, and 10.19% and 8.45% in tool wear compared to pure palm and sunflower oil media, respectively. Finally, sustainability assessments of various cooling media revealed that a minimal quantity of the blended bio-lubricant-based medium outperforms both compressed air and flooded media.

The environmental awareness and sustainable nature of plant-based fibers have forced material researchers and automakers to use natural fibers instead of petroleum-based fibers for various industrial applications. The need for environmentally and biodegradable fibers has created a demand in the transportation industry. In this study, bio castor seed shell (C), glass fiber (G), and SiC (SC) fillers in a constant weight fraction (10 %) were reinforced separately with varying weight fractions (5–25 %) of African teff and snake grass fibers to improve the mechanical properties of the hybrid composites. Both African teff and snake grass fibers were subjected to alkaline treatment to remove amorphous elements such as hemicellulose, lignin, and wax, resulting in high surface roughness. The hybrid composites were fabricated by the compression molding technique and their mechanical properties were characterized as per ASTM standards. The fractured surface of the treated fiber was examined by scanning electron microscopy. From the results, it was found that SC10SG20AT showed maximum mechanical properties compared to C10SG20AT and G10SG20AT due to higher load-bearing capacity of SiC filler. Therefore, SC10SG20AT can be recommended for lightweight applications.