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This work investigates the development, characterization, and evaluation of nanostructured catalysts intended especially for environmentally benign energy conversion. We developed nanostructured catalysts by modifying the sol-gel method and varying the precursor material and reaction conditions ratios. The morphological differences between the synthesised catalysts were shown. Among catalyst 3's best features were its large surface area and pore volume. Noteworthy activity and selectivity were shown by catalyst 3. Low overpotential was attained along with high current density and faradaic efficiency. The stability studies proved that Catalyst 3 was durable since, over many cycles, its electrochemical performance scarcely altered. The requirement of carefully adjusting the synthesis conditions to tailor nanostructured catalysts for specific energy conversion applications is highlighted by these findings. The main objectives going forward should be to enhance the processes involved in producing anything and to find novel chemical combinations that may accelerate the effective and environmentally benign conversion of energy. By addressing these problems, nanostructured catalysts have the potential to greatly progress renewable energy technology and lessen environmental impact worldwide.

This research examines the profound effects of integrating IoT-enabled predictive maintenance in sustainable transportation fleets. By using real-time sensor data, this implementation aims to enhance fleet dependability and operational efficiency. The fleet, including a variety of vehicles such as electric buses, hybrid cars, electric trucks, CNG-powered vans, and hybrid buses, is constantly monitored using IoT sensors that capture important characteristics like engine temperature, battery voltage, and brake wear percentages. The predictive maintenance algorithms adapt maintenance schedules in response to live sensor data, enabling a proactive strategy that tackles prospective problems before they result in major failures. The examination of the maintenance records reveals prompt actions, showcasing the system’s efficacy in reducing operational interruptions and improving the overall dependability of the fleet. Moreover, the examination of percentage change confirms the system’s flexibility, demonstrating its capacity to anticipate fluctuations in engine temperature, battery voltage, and brake wear. The findings highlight the system’s ability to adapt to various operating situations and its contribution to lowering maintenance expenses while enhancing operational effectiveness. The established approach incorporates ethical issues, such as data security and privacy, to ensure responsible adoption of IoT technology. This study has broader ramifications beyond the particular dataset, providing a detailed plan for incorporating IoTenabled predictive maintenance into contemporary transportation infrastructures. The study’s findings offer valuable insights into the potential of proactive maintenance strategies to transform the transportation industry towards sustainability. This contributes to a future where fleets operate with increased efficiency, reduced environmental impact, and improved reliability.

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