In the present study, Calcium oxide (CaO) obtained from eggshells has been used as a heterogeneous catalyst for biodiesel production from highly viscous non-edible rubber seed oil (RSO). Characterization of synthesized catalyst was done with the help of scanning electron microscope equipped with Energy dispersive spectrometry (SEM-EDS), X-ray diffraction (XRD) and Fourier transform Infrared spectroscopy (FTIR). Response surface methodology (RSM) with central composite design (CCD) was used to optimize the process parameters and ¹ H-NMR (Nuclear Magnetic Resonance) spectroscopy analysis was performed to find the conversion of RSO to biodiesel. A conversion of 99.7% of RSO to biodiesel was obtained at 12:1 methanol to oil molar ratio, 4 (wt%) of catalyst, and 3 hour reaction time with a quadratic regression model of R ² of value 0.9566 was obtained. The composition of prepared biodiesel is estimated with the help of Gas Chromatogram-Mass Spectroscopy (GC-MS) analysis. Artificial Neural Network (ANN) with Levenberg-Marquardt algorithm was also trained to predict biodiesel conversion and the value of R ² obtained was 0.9976. It was observed that predicted conversion values from ANN were better when compared to prediction using RSM.

Biochar has gained attention as an alternative source of solid energy and for the proper disposal of agricultural biomass waste (ABW). Microwave-assisted pyrolysis (MAP) is a promising approach for the production of biochar. This review article presents the beneficial use of biochar for soil fertilization, machine learning (ML), the circular bioeconomy, and the technology readiness level. The use of machine learning techniques helps to design, predict, and optimize the process. It can also improve the accuracy and efficacy of the biochar production process, thereby reducing costs. Furthermore, the use of biochar as a soil amendment can be an attractive option for farmers. The incorporation of biochar into soil has been shown to improve soil fertility, water retention, and crop productivity. This can lead to reduced dependence on synthetic fertilizers and increased agricultural yields. The development of a biochar economy has the potential to create new job opportunities and increase the national gross domestic product (GDP). Small-scale enterprises can play a significant role in the production and distribution of biochar, providing value-added products and helping to promote sustainable agriculture.

The adverse effects of UV (ultraviolet) radiation on polymeric materials and organic constituents can damage the molecular structure of human skin and polymeric materials, resulting in their degradation. Therefore, additives or reagents for UV-shielding must be used in related applications, including polymer compounds and skin cosmetics. Bio-based polymers have shown great potential as alternatives to conventional metallic and organic materials (e.g., TiO₂ and ZnO) in various applications; therefore, natural products have gained attention as a potential resource to overcome UV-induced health and environmental problems. In particular, biomass-derived materials such as lignin, fiber, and silica have been investigated as UV-shielding materials owing to their biocompatibility, biodegradability, and low carbon emissions. In this review, the UV-shielding effect and potential of various biomass-derived materials, such as silica, nanocellulose, and fibers, are reviewed. Among them, lignin is considered a promising UV-shielding material because of the presence of chromophores and functional groups capable of absorbing UV radiation of all ranges.

Application of a novel heterogeneous catalyst fluorite (calcined fluorspar CaF₂) in biodiesel preparation from a highly viscous non-edible rubber seed oil has been studied. Optimization of process parameters and effect of process parameters on biodiesel conversion is done using response surface methodology (RSM) with central composite design analysis. The optimum process conditions, i.e., 12:1 molar ratio (mol/mol), 4 (wt%) catalyst, and 5 h of reaction time for a conversion of 95.95% oil to biodiesel conversion were observed. A quadratic regression model to predict the conversion in terms of process parameters was obtained. Artificial neural network (ANN) with the Levenberg-Marquardt algorithm is also attempted, with the best performance of 0.80239 at epoch-3 and a mean square error of 1.24. On comparison of the coefficient of determination, R², the value obtained from both RSM and ANN it is observed that the ANN model predicts conversion better than RSM, with minimum error.