Bitlis Eren University

Despite increasing interest in borohydride hydrolysis for hydrogen generation, limited studies have explored carbon quantum dots (CQDs) derived from low-cost, renewable sources such as sucrose as catalyst supports.In this study, sucrose-derived CQDs were synthesized via a green hydrothermal method and employed as a support material for Ru catalysts.A novel hydrogen potential catalytic activity (HPCA) metric was introduced to compare the performance of Ru@CQDs in the hydrolysis of sodium borohydride (SBH) and potassium borohydride (PBH), addressing the lack of normalized efficiency metrics across different hydrogen sources.The ideal conditions for each hydrolysis reaction were established based on the hydrogen generation rate (HGR) after studying various catalyst characteristics using UV-Vis, UV-PL, TEM, and FT-IR analyses.The HGR values using 4 % NaBH₄ and 4 % KBH₄ were 167,588 and 122,527 mL g^-1 min^-1, resp.However, the hydrogen potential catalytic activity metrics (HPCA), calculated based on the hydrogen content of the sources, were 1.551,741 mL g^-1 min^-1 and 1.655,770 mL g^-1 min^-1 for NaBH₄ and KBH₄ hydrolysis, resp.This indicates that the rate of hydrogen production during KBH₄ hydrolysis is higher than that during NaBH₄ hydrolysis.For each hydrolysis reaction, the catalyst′s turnover frequency (TOF) was also determined to assess its effectiveness: 924.8 mol H₂.mol.Ru^-1.min^-1 for NaBH₄ hydrolysis and 573.3 mol H₂.mol Ru^-1.min^-1 for KBH₄ hydrolysis.Addnl., the exptl. data on the kinetics and activation energy of the hydrolysis processes, as well as the catalyst′s reusability, were analyzed.

This study introduces a comprehensive mathematical model designed to optimize traffic flow in a newly planned Organized Industrial Zone (OIZ). The proposed model focuses on four primary objectives: maximizing overall traffic flow, minimizing congestion, regulating peak-hour traffic, and effectively managing personnel and visitor traffic. The model incorporates traffic demand, road and intersection capacities, and vehicle types, leveraging these parameters to ensure efficient and sustainable traffic management. Unlike traditional simulation-based approaches, this model integrates optimization techniques to address the unique challenges of OIZs, such as logistics flows and personnel movements. Key components include decision variables for vehicle flows across road sections, robust capacity constraints for roads and intersections, and weighted parameters to balance objectives. The model also considers infrastructure improvements like road expansions, intersection adjustments, and traffic signal optimization. By optimizing traffic flow under various constraints, the model offers a strategic planning tool for decision-makers, enabling data-driven adjustments to infrastructure and traffic policies. Its adaptable structure allows for application in diverse OIZ projects, enhancing both operational efficiency and long-term sustainability. The study highlights the model's potential to serve as a benchmark for OIZ traffic management, addressing critical gaps in existing literature.