RESUMO
Coexisting with the current COVID-19 pandemic is a global reality that comes with unique challenges impacting daily interactions, business, and facility maintenance. A monumental challenge accompanied is continuous and effective disinfection of shared spaces, such as office/school buildings, elevators, classrooms, and cafeterias. Although ultraviolet light and chemical sprays are routines for indoor disinfection, they irritate humans, hence can only be used when the facility is unoccupied. Stationary air filtration systems, while being irritation-free and commonly available, fail to protect all occupants due to limitations in air circulation and diffusion. Hence, we present a novel collaborative robot (cobot) disinfection system equipped with a Bernoulli Air Filtration Module, with a design that minimizes disturbance to the surrounding airflow and maneuverability among occupants for maximum coverage. The influence of robotic air filtration on dosage at neighbors of a coughing source is analyzed with derivations from a Computational Fluid Dynamics (CFD) simulation. Based on the analysis, the novel occupant-centric online rerouting algorithm decides the path of the robot. The rerouting ensures effective air filtration that minimizes the risk of occupants under their detected layout. The proposed system was tested on a 2 × 3 seating grid (empty seats allowed) in a classroom, and the worst-case dosage for all occupants was chosen as the metric. The system reduced the worst-case dosage among all occupants by 26% and 19% compared to a stationary air filtration system with the same flow rate, and a robotic air filtration system that traverses all the seats but without occupant-centric planning of its path, respectively. Hence, we validated the effectiveness of the proposed robotic air filtration system.
RESUMO
Understanding the influence of surface roughness on the adsorption of ions from an ionic liquids (ILs) mixture is essential for designing supercapacitors. The classical density functional theory (DFT) is applied to investigate the adsorption behavior of ILs mixtures in rough nanopores. The model parameters for each ion are determined by fitting experimental data of pure IL density. The results show that the smaller anions are densely accumulated near the rough surface and are the dominant species at a high positive potential. The exclusion of larger anions is enhanced by roughness at almost all potentials. At negative potential, the surface roughness promotes the adsorption of cations, and the partition coefficient increases with roughness. The partition coefficient of smaller anions is virtually independent of roughness. At positive potential, the surface roughness only promotes the adsorption of smaller anions and raises the partition coefficient. The partition coefficient of smaller anions is far greater than one. The selectivity of smaller anions for rough surfaces is very high and increases with roughness. The surface charge of a more uneven surface is significantly higher (about 30%) at a high potential.
