Nano-confined synthesis of highly ordered mesoporous carbon and its performance as electrode material for electrochemical behavior of riboflavin (vitamin B2) and dopamine

Highly ordered mesoporous carbon (MC) has been synthesized from sucrose, a non-toxic and costeffective source of carbon. X-ray diffraction, N2 adsorption–desorption isotherm and transmission electron micrograph (TEM) were used to characterize the MC. The XRD patterns show the formation of highly ordered mesoporous structures of SBA15 and mesoporous carbon. The N2 adsorptiondesorption isotherms suggest that the MC exhibits a narrow pore-size distribution with high surface area of 1559 m2/g. The potential application of MC as a novel electrode material was investigated using cyclic voltammetry for riboflavin (vitamin B2) and dopamine. MC-modified glassy carbon electrode (MC/GC) shows increase in peak current compared to GC electrode in potassium ferricyanide which clearly suggest that MC/GC possesses larger electrode area (1.8 fold) compared with bare GC electrode. The electrocatalytic behavior of MC/GC was investigated towards the oxidation of riboflavin (vitamin B2) and dopamine using cyclic voltammetry which show larger oxidation current compared to unmodified electrode and thus MC/GC may have the potential to be used as a chemically modified electrode.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.