Prathyusha Engineering College was founded in 2001 by Shri P. Raja Rao, and is located in Tamil Nadu, India. It offers engineering courses at undergraduate and postgraduate levels. The college is affiliated with Anna University.
This research investigates the development of aluminium-based hybrid nanocomposites reinforced with microscale boron carbide (B4C) and nanoscale titanium dioxide (TiO2) to enhance mechanical strength and thermal performance simultaneously. A controlled micro–nano hybrid reinforcement approach, combined with tubular furnace sintering, was employed to promote uniform particle dispersion, improve interfacial bonding, and enhance densification. Composites containing a constant 5 wt
Electric vehicles (EVs) are becoming increasingly vital to modern power systems due to their substantial social and economic benefits. However, real-time Electric vehicle charging control remains challenging because of environmental uncertainties and the difficulty of optimizing multiple objectives simultaneously, such as reliability improvement, cost reduction, auxiliary service provision, and effective integration of renewable energy. To address these issues, we propose a predictive framework based on a Long Short-Term Memory fused Gated Recurrent Unit (LSTM-GRU) model for fuel economy prediction and charging control recommendation. Firstly, the simulation of Electric vehicles is conducted, and thereafter, pre-processing is accomplished. Afterwards, feature fusion (FF) is carried out utilizing Deep Q-network (DQN) with the Jaccard coefficient (JC). Then, data augmentation is performed utilizing oversampling method to increase dimensionality. After that, fuel economic prediction is done employing Long-Short Term Memory fused Gated recurrent unit, which is an amalgamation of Deep Long-Short Term Memory (DLSTM) and Gated Recurrent Unit (GRU). Finally, the recommendation for charging control is executed. In addition, Long-Short Term Memory fused Gated recurrent unit acquired minimum Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 0.118, 0.109 and 0.343, demonstrating its effectiveness in supporting intelligent and reliable EV charging control. Furthermore, the performance improvement of the proposed LSTM-GRU method over the traditional methods, such as Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), ensemble Stacked Generalization (ESG), Q-learning technique, LightGBM, and Group Learning Algorithm- convolutional neural network with bidirectional long short-term memory (GLA-CNN–Bi-LSTM), are 83.09
Despite growing interest in aluminium-based hybrid composites for tribological applications, a clear understanding and optimisation of the wear behaviour of Al/B4C/ZrO2 hybrid composites fabricated by powder metallurgy remains limited, particularly with respect to the combined influence of reinforcement composition and operating parameters. The research examines frictional behaviour and the parametric enhancement of an aluminium-based hybrid composite (HC) strengthened with boron carbide and zirconia particles, produced via powder metallurgy techniques. Composites containing 2, 5, and 8 wt% reinforcements were produced through high-energy ball milling, followed by compaction at 700 MPa and sintering at 750 degrees C under argon atmosphere to ensure uniform particle dispersion and strong interfacial bonding. Dry sliding wear experiments were performed using a pin-on-disc tribometer at normal loads of 10-30 N, with sliding speeds of 0.5-1.5 m/s and sliding distances of 500-1000 m. Experimental results demonstrated significant dependence of wear loss on both operating parameters and reinforcement composition. Taguchi-based optimisation determined the lowest wear rate at a load of 20 N, a sliding speed of 1.5 m/s, a sliding distance of 1000 m, and 5 wt% B4C, and 8 wt% ZrO2. Analysis of variance indicated that sliding velocity was the most influential parameter, contributing 44.94%, followed by sliding distance at 24.39% and applied load at 11.42%, whereas ZrO2 and B4C accounted for 10.28% and 4.75%, respectively, with an experimental error of 4.21%. Linear regression achieved 82.81% predictive accuracy, whereas Random Forest and Polynomial Regression improved R-2 to beyond 0.95 with minimal prediction errors. The integrated statistical and machine-learning framework provides reliable multi-parameter optimisation of hybrid aluminium composites for advanced tribological applications.
This study proposes a perovskite solar cell architecture integrating quantum dots (QDs) to harness additional sub‐bandgap photons. A thin low‐bandgap (1.35 eV) PbS‐QD layer is embedded within a wide‐bandgap (1.6 eV) MAPbI 3 absorber, forming a quantum well (QW) structure. Acting as a broadband multi‐bandgap absorber, this design enables additional absorption of sub‐bandgap photons (1.35–1.6 eV), with carrier transport occurring via thermionic emission across the QW barrier. The standard p–i–n MAPbI 3 device achieved a simulated efficiency of 25.6%, while the QW‐integrated device reached 28.1%. These results highlight the potential of quantum well architectures to advance next‐generation high‐efficiency perovskite photovoltaics.
The development of solid-state electrolytes that simultaneously ensure safety, sustainability, and efficient Mg2+ transport remain elusive for magnesium-ion batteries. In this work, a high-performance biodegradable solid polymer electrolyte is systematically designed through a synergistic combination of guar gum, glycerol, and magnesium chloride salt. The polymer-plasticizer-salt interactions effectively disrupt structural ordering, generating a highly amorphous ion-conducting network that facilitates enhanced Mg2+ transport. Vibrational and spectroscopic analyses reveal strong coordination between polymer functional groups and magnesium ions, while morphological observations confirm uniform and homogeneous electrolyte films. The optimized system exhibits an ionic conductivity of 1.02 & times; 10-4 S/cm at ambient conditions, along with a high ionic transference number of 0.96, indicating dominant ionic conduction. A stable electrochemical window of 2.05 V and an open-circuit potential of 2.43 V are achieved through linear sweep voltammetry and fabrication of primary Mg2+ ion cells. Notably, the electrolyte demonstrates excellent electrochemical reversibility and prolonged cycling sta-bility over 1000 cycles. This facile combination provides a promising pathway for the development of high-performance solid-state magnesium ion batteries.
