The current work focuses on utilization of ANN (artificial neural network) for the prediction of performance and tailpipe emissions of Garcinia gummigutta methyl ester (GGME) enriched with H₂ and TiO₂ nano additives. For experimentation, H₂ gas was introduced to the mixes containing TiO₂ nanoparticles. Diesel, B10 blend (10% GGME biofuel + 90% Diesel), B20 (20% GGME biofuel + 80% Diesel), Diesel-TiO₂ (Mineral Diesel with 100 ppm TiO₂ nano additives), B10-H₂-TiO₂ (B10 blend with 100 ppm nano additives + 5 L/min of H₂) and B20-H₂-TiO₂ (B20 blend with 100 ppm nanoparticles + 5 L/min of H₂) were considered for experimentation. A constant mass flow rate of 10 L/min was used for the hydrogen flow throughout the test procedures. Test results were carefully analyzed to determine the performance and emission measures. Different speeds between 1800 and 2800 rpm were used for each test. When combined with pure Diesel and mixtures of biodiesel, these nanoparticles and hydrogen enhanced the performance data. For instance, the brake-specific fuel consumption was reduced but the power, torque, and thermal efficiency were increased. Although there was a modest rise in NO emissions, the primary goal of lowering CO, CO₂, and other UHC emissions was met. The ANN models confirm and agreed the Diesel engine experimental work possesses minimal root mean square error (RMSE) and correlation coefficient values were estimated. This ideal model predicts and optimizes the engine output at a higher accuracy level, which gives better results compared with other empirical and theoretical models.

This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions. Key parameters, including spray penetration, droplet size distribution, and evaporation rates, are modeled and validated against experimental data. The findings reveal that nanoparticle-enhanced fuels, coupled with LSTM-based predictive analytics, lead to superior combustion performance and lower pollutant formation. This interdisciplinary approach provides a robust framework for designing next-generation CI engines with improved efficiency and sustainability. Diesel engine performance and emissions were found to be influenced by variations in combustion chamber geometry, underwent validation through simulation using Diesel-RK. Re-entrant bowl profile in quaternary blend is found to exhibit 31.3% higher BTE and 8.65% lowered BSFC than the conventional HCC bowl at full load condition. Emission wise, re-entrant bowl induced 90.16% lowered CO, 59.95% lowered HC and 15.48% lowered smoke owing to improved spray penetration and faster burning of soot precursors. However, the NO_x emissions of DBOPN-TRCC were found to be higher. The simulation outcomes, derived from Diesel-RK, were subsequently compared with empirical data obtained from real-world experiments. These experiments were systematically carried out under identical operating conditions, employing different piston bowl geometries.

Perovskite solar cells (PSCs) hold potential for low-cost, high-efficiency solar energy, but their sensitivity to moisture limits practical application. Current fabrication requires controlled environments, limiting mass production. Researchers aim to develop stable PSCs with longer lifetimes under ambient conditions. In this research work, we investigated the stability of perovskite films and solar cells fabricated and annealed in natural air using four different anti-solvents: toluene, ethyl acetate, diethyl ether, and chlorobenzene. Films (about 300 nm thick) were deposited via single-step spin-coating and subjected to ambient air-atmosphere for up to 30 days. We monitored changes in crystallinity, electrical properties, and optics over time. Results showed a gradual degradation in the films’ crystallinity, morphology, and electro-optical properties. Notably, films made with ethyl acetate exhibited superior stability compared to other solvents. These findings contribute to advancing stable and high-performance PSCs manufactured under normal ambient conditions. In addition, we also discuss the possible machine learning (ML) approach to our future work direction to optimize the materials structures, and synthesis process parameters for future high-efficient perovskite solar cells fabrication.

The growing interest in alternative fuels stems from the need to address energy security and economic challenges. Hydrogen fuel is considered a promising solution due to its versatility, efficiency, and potential for zero emissions, as well as continuous technological advancements and increasing policy support. This study intends to enhance Chlorophyta growth by incorporating different volume percentages of cerium oxide (CeO₂) as feedstock for hydrogen production through hydrothermal gasification. The research findings indicate that the highest concentration (0.2 vol %) of CeO₂ resulted in the maximum growth of 0.63 μ/day, which was utilized for hydrogen extraction. Additionally, the investigation explored the impact of Chlorophyta algae loading (0.05-0.15 g/mL) and processing temperature (500-650°C) on molar fraction, hydrogen yield, gasification efficiency, carbon efficiency, and total hydrogen production. Higher feedstock (0.15 g/mL) and gasification temperature (650°C) were found to improve hydrogen fraction (61.8%), yield (10.2 mol/kg), gasification efficiency (43.8 %), and total hydrogen production (62.5%).

Higher-end science and technology facilitate the human community with a sophisticated life despite it curses by abundant pollution. The alarming demand for sustainability pressurizes the manufacturing sector to ensure sustainable manufacturing. Since Molybdenum di sulfide (MoS₂) and avocado oil are known solid and liquid lubricants respectively, hence, it is a worthwhile attempt to implement the bio-based degradable avocado oil enriched with nano Molybdenum di sulfide (nMoS₂) particles as a potential machining fluid for CNC-end milling. Different proportions of avocado oil and nMoS₂ were used to synthesise four distinct machining fluids to assess the individual impact of avocado oil and nMoS₂ particles. The emulsification and sonication were employed to synthesise the fluids. A hybrid Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) was followed to scrutiny the effect of novel machining fluid on machining objectives. The experimental results of physio-chemical properties revealed that avocado-rich 0.5% nMoS₂ excels among others. The L₁₆ orthogonal array experiments associated with statistical analysis explored the developed machining fluid (A6W4/0.5) that significantly impacts the machining objectives. The experimental results manifest that nearly 64.87% of surface roughness and 93.3% of tool wear have been reduced during machining in the presence of A6W4/0.5 fluid than A4W6/0.75. The improved performance of the novel machining fluid upholds its potential to replace conventional fluids and ensure green manufacturing.