In this study, we compared the wetting and electrowetting properties of a planar parylene (poly(p-xylylene)) film to those of a nanostructured parylene film. To generate the nanostructured film, we used an aligned array of multiwalled carbon nanotubes as a template; a thin coating of parylene was deposited on the nanotube template to generate a parylene film with a nanoscale roughness structure. Static contact angle measurements indicated a very significant increase in the water contact angle from approximately 73 degrees for planar parylene to approximately 110 degrees for the nanotemplated parylene. In addition, we performed electrowetting experiments to dynamically tune the contact angle by application of electric potential. Interestingly, the flat parylene film showed contact angle saturation at an applied voltage of approximately 40 V, while the nanotemplated parylene film did not experience saturation in the contact angle response even for voltages up to 80 V. These results show that engineering a nanoscale roughness structure to a polymer film results in significant changes to the wetting and electrowetting properties of the polymer.
This paper presents the Model Predictive Control (MPC) of magnetized Tetrahymena pyriformis (T. pyriformis) using a magnetic field. The magnetized T. pyriformis are generated by feeding spherical iron oxide particles into the cells. Using an external magnetic field, we change the movement direction of the cell, but the speed of the cell remains constant regardless of the strength of the external magnetic field. The contributions of this paper are threefold. First, the discrete-time plant model of the magnetized cell is generated using the least-squares method. Second, using the model of each cell, they are controlled to follow a reference track by an external magnetic field with MPC. Third, by using a predictor-like scheme to execute the plant input before the measurement of the cell position, we successfully solve the image-processing delay problem in the feedback system. In our results, we show three comparisons between different control schemes and an initial tracking to prove the effectiveness of the control approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.