Volume 2, Issue 1

This study introduces a sustainable approach to synthesize zero-dimensional TiO 2 nanostructures using Allium sativum (garlic) clove extract as a biogenic capping and reducing agent. The synthesized particles were dried at 80 ºC for 24 hours and calcined at 450 ºC for 2 hours. X-ray diffractometer (XRD), Field Emission Scanning Electron Microscopy (FESEM), UV-vis spectroscopy, and photocatalytic activity analysis were used to characterize the synthesized nanoparticles. The XRD pattern revealed the prevalence of the tetragonal anatase phase in the synthesized nanostructures, with an average crystallite size of 6.45 nm. The Williamson-Hall plot method was employed to evaluate the type of strain induced in the synthesized particles, and the crystallinity of TiO 2 nanostructures was estimated to be 74.91%. The Rietveld analysis calculated the lattice parameters, cell volume, and other relevant parameters. FESEM image showed spherical nanostructures with an average size of 45.08 nm. UV-vis spectroscopy indicated an optically active region at ~361 nm, and the direct bandgap, calculated using the Tauc plot, was 3.28 eV. The photocatalytic efficiency of TiO 2 for methylene blue degradation reached 86.23% within 90 minutes, showing its potential for environmental remediation.

The geometric structure of graphene under an external electric field was investigated using the density functional theory (DFT). DFT calculations revealed that the hole injection into graphene highly modulates the geometric structure of graphene. The lattice parameter of graphene monotonically increased with increasing the hole doping concentration. The lattice constant is insensitive to the electron doping. The fragility and robustness against the hole and electron doping, respectively, are ascribed to the electronic structure of graphene under the electric field. The wave function analysis revealed that the holes are accommodated in the bonding π states, weakening covalent bonds between C atoms. In contrast, the electrons are primarily accommodated in nearly free electron states with a maximum amplitude above and below the graphene layer.

This paper investigates the design and performance analysis of a helical gear-pinion system intended for a reducer gearbox, which operates at a pinion speed of 1800 rpm and a power supply of 5 hp under moderate shock conditions for 8 to 10 hours daily. Using carbon steel (SAE 3150) for both parts, the study runs mathematical modelling to assess critical parameters and then designs it through SolidWorks integrated with GearTrax. Finite Element Analysis (FEA) conducted in ANSYS assesses the mechanical behavior under load, revealing a maximum deformation of 1.4672×10 −2 mm, maximum directional deformation (X-axis) of 1.2994×10 −2 mm, maximum equivalent (von Mises) stress of 125.79 MPa, and maximum principal stress of 138.99 MPa, all within acceptable limits for the material. The findings indicate that the gear-pinion system can operate reliably without premature failure. This research contributes to understanding gear design and performance, providing a foundation for future studies on material optimization and real-world operational conditions.

This study is devoted to the development of lightweight Aluminum alloy to reduce energy consumption and environment pollution in automobile sector. Aluminum alloy shows impressive strength though they are lightweight. The effect of Cu on Al-5.5Zn-2.5Mg-xCu alloy has been studied and improvement in mechanical properties within a range has been observed. The optical microstructure confirmed the formation of Cu enriched brittle phase by solid solution strengthening and it accelerates the hardness and tensile strength. After certain limit strength followed the opposite trend so the material can be used as high strength material when the Cu content will be in a certain limit. In automobile sector vehicle parts made with this alloy material has less weight than commonly used steel. The total weight reduction of a vehicle will reduce the energy consumption which will ultimately eliminate the emission of environment pollutant gas.

Burning fossil fuels like coal, oil, and natural gas releases significant CO 2 , driving carbon emissions in electricity production. This contributes to global warming, leading to climate change, extreme weather, rising sea levels, and harm to ecosystems and human health. Achieving zero emissions requires transitioning to low-carbon energy sources. This study uses various Machine Learning (ML) models for predicting low carbon electricity generation additionally eXpalinalbe Artificial Intelligence (XAI) to elucidate how ML model works and suggest some important factor contributing to the models outcome. Among several ML models RF outperforms other by achieving MSE of 2.782, RMSE of 2.782, MAE of 1.443 and R 2 of 99.3%. Also GBM and XGB performed closely as RF. When applying XAI tools like SHAP and Shapash to RF it reveals some important factors such as Electricity from renewables (TWh), Eectricity from fussil fuels (TWh) Renewable energy share in the total final energy consumption (%), and Electricity from nuclear. Which are crucial for future energy planning and policy decisions. Future study can use a more extensive data set, investigate economic aspects, and include real-time data to enhance predictive model performance.