The present study focuses on the microwave-assisted transesterification (MAT) employed in Bauhinia variegate (BV) biodiesel production in a shorter reaction time with a better yield. The oil was extracted and characterized for physiochemical, fatty acid composition, and Fourier transform infrared analysis. The lower acid value (<2) indicated that the oil can be processed to the transesterification process. MAT was optimized for maximum biodiesel production using the statistical tool with maximum biodiesel yield of 96.83% at the optimum condition of oil to methanol ratio - 1:7 (v/v), catalyst concentration - 1 (wt%), power - 450 W at 10 min. The result was well in agreement with the physicochemical properties of ASTM D6751 standard prescribed limits. Nevertheless, the BV diesel physiochemical properties were higher than that of petrodiesel. It is then blended with petrodiesel to investigate the calorific value, density, kinematic viscosity, flash point, cloud and pour point. It was found that up to 20% biodiesel blending gives close results with petrodiesel properties. From experimental results, the MAT process was efficient in a shorter time technique for BV biodiesel production. This beneficial approach leads to reducing the production cost of biodiesel in a continuous mode.
This study investigates the catalytic pyrolysis of four different plastic wastes, namely, Polyethylene Terephthalate (PET), biaxially oriented polypropylene (BOPP), metallized PET (MET/PET), and metallized BOPP (BOPP/MET) was systematically investigated in a pilot-scale packed-bed reactor operated in batch mode. The reactions were performed without catalysts and using two different catalysts, namely, ZSM-5 and zeolite. Catalytic pyrolysis increased liquid pyrolysis oil production and promoted deoxygenation, particularly for polyolefin-rich feedstocks. Also, GC-MS and FT-IR analyses confirmed that PET and metallized PET (MET/PET) produced high-quality hydrocarbon oils dominated by C-1-C-10 fractions (56-64%), suitable for gasoline applications, with reduced oxygenate content (similar to 13-14%). Catalytic pyrolysis greatly improved liquid oil yield, and zeolite had a maximum yield of 46.67%wt, whereas ZSM-5 catalysis favored more gas production through secondary cracking reactions. GC-MS results showed that hydrocarbon-rich oils containing C1-C10 fractions (56-64%) with reduced oxygenates were produced. Catalytic pyrolysis significantly improved liquid oil yield, with zeolite achieving a maximum of 46.67 wt.%, while ZSM-5 favored gas formation. BET analysis showed a higher surface area for zeolite (297.44 m(2)/g) compared to ZSM-5 (188.97 m(2)/g), correlating with enhanced catalytic performance. Life Cycle Assessment (LCA) results revealed that zeolite catalyst substantially reduced Global Warming Potential, Acidification Potential, and Eutrophication Potential compared to non-catalytic processes. Additionally, the lowest environmental impact was observed for the pyrolysis conversion of biaxially oriented polypropylene (BOPP) feedstock with zeolite catalyst. The results demonstrate that catalytic pyrolysis enables efficient conversion of non-recyclable plastic waste into low-oxygenate liquid fuels, while also recovering carbon-rich char. This process provides a sustainable thermochemical route toward circular economybased plastic waste management. Polyethylene Terephthalate and multilayer plastics favored increased gas and char formation.
The technique of large-volume fat grafting is gaining recognition in plastic and reconstructive surgery for its potential to restore facial volume and enhance aesthetic outcomes. However, a major challenge remains—the unpredictable retention of grafted fat over time. To improve consistency and long-term results, a deeper understanding of the biological mechanisms governing fat survival and integration after transplantation is essential. This case series examines the long-term outcomes of large-volume autologous fat grafting to the face, focusing on fat survival, patient follow-up and technique refinement for enhanced facial rejuvenation.
Manganese naphthenate (C₂₂H₁₄MnO₄), a black oil-soluble liquid, was used for the first time as a bifunctional homogeneous catalyst in heavy oil upgrading under steam conditions. The catalyst combines two key functions: cracking, due to the 6% manganese content, and hydrogen donation from the naphthenate component, which enhances hydrogenation reactions, increases the H/C ratio, and facilitates hydrodesulfurization. Experiments were conducted in an autoclave reactor using heavy oil with high sulfur content under three temperatures (200, 250, and 300 °C) for 24 hours, in the presence and absence of the catalyst. The study employed several analytical techniques—viscosity measurement, SARA analysis, gas chromatography (GC), and X-ray diffraction (XRD)—to evaluate catalyst performance. Gas analysis by GC showed that, without the catalyst, the total light hydrocarbon gases (ΣC₁–C₄) and H₂S reached 0.8150 wt% and 1.2960 wt%, respectively, while with the catalyst, these increased to 1.4905 wt% and 1.5400 wt%. This indicates enhanced cracking and desulfurization activity. Viscosity measurements using a Brookfield Viscometer revealed that at 300 °C, the catalyst reduced the oil viscosity from 2802 mPa·s to 1437 mPa·s, achieving a 51% reduction, compared to only 29% (to 1962 mPa·s) without the catalyst. SARA analysis showed that the light fractions (saturates + aromatics) increased from 72% in the original oil to 80.76% with the catalyst and 75.69% without it, confirming significant cracking and hydrogenation. The presence of MnS in the solid residues after reaction further supports the catalyst’s participation in hydrodesulfurization. Overall, manganese naphthenate demonstrated excellent catalytic activity in upgrading heavy oil through combined cracking, hydrogenation, and desulfurization mechanisms under steam conditions, making it a promising candidate for improving the quality and flow properties of high-sulfur heavy crude oils.
Tamil, a Dravidian language family member, is widely spoken in numerous Indian states. But languages like Tamil, are under- represented on the web. Many NLP models perform worse with these languages when compared to English, the effects of which lead to subpar experiences in many web applications for most of the users. The number of Tamil websites has grown and continues to expand. Tamil newspapers, periodicals, and e-journals are examples. Therefore Tamil Question Answering System will be beneficial to many people who are in the rural areas and also for those who prefer our native Tamil Language. This paper aims to create a Question Answering System in Tamil using deep learning techniques, starting from question processing through an interface, retrieving the relevant context for the given question and finally the answer is displayed after appropriate processing. The model is evaluated using a manually created test dataset and the metrics are analyzed.
