Electrostatic Spray Disinfection Using Nano-Engineered Solution on Frequently Touched Surfaces in Indoor and Outdoor Environments.

The COVID-19 pandemic has resulted in high demand for disinfection technologies. However, the corresponding spray technologies are still not completely optimized for disinfection purposes. There are important problems, like the irregular coverage and dripping of disinfectant solutions on hard and vertical surfaces. In this study, we highlight two major points. Firstly, we discuss the effectiveness of the electrostatic spray deposition (ESD) of nanoparticle-based disinfectant solutions for systematic and long-lasting disinfection. Secondly, we show that, based on the type of material of the substrate, the effectiveness of ESD varies. Accordingly, 12 frequently touched surface materials were sprayed using a range of electrostatic spray system parameters, including ion generator voltage, nozzle spray size and distance of spray. It was observed that for most cases, the surfaces become completely covered with the nanoparticles within 10 s. Acrylic, Teflon, PVC, and polypropylene surfaces show a distinct effect of ESD and non-ESD sprays. The nanoparticles form a uniform layer with better surface coverage in case of electrostatic deposition. Quantitative variations and correlations show that 1.5 feet of working distance, an 80 µm spray nozzle diameter and an ion generator voltage of 3-7 kV ensures a DEF (differential electric field) that corresponds to an optimized charge-to-mass ratio, ensuring efficient coverage of nanoparticles.

Novel sustainable filter for virus filtration and inactivation.

The COVID-19 pandemic has caused a multi-scale impact on the world population that started from a nano-scale respiratory virus and led to the shutdown of macro-scale economies. Direct transmission of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) and its variants through aerosolized droplets is a major contributor towards increasing cases of this infection. To curb the spread, one of the best engineered solutions is the use of face masks to prevent the passage of infectious saliva micro-droplets from an infected person to a healthy person. The commercially available masks are single use, passive face-piece filters. These become difficult to breathe in during strenuous activities. Also, they need to be disposed regularly due to accumulation of unwanted particulate and pathogens over time. Frequent disposal of these masks is unsustainable for the environment. In this study, we have proposed a novel design for a filter for enhanced virus filtration, better breathability, and virus inactivation over time. The filter is called Hy-Cu named after its (Hy) drophobic properties and another significant layer comprises of copper (Cu). The breathability (pressure drop across filter) of Hy-Cu is tested and compared with widely used surgical masks and KN95 masks, both experimentally and numerically. The results show that the Hy-Cu filter offers at least 10% less air resistance as compared to commercially available masks. The experimental results on virus filtration and inactivation tests using MS2 bacteriophage (a similar protein structure as SARS-CoV-2) show that the novel filter has 90% filtering efficiency and 99% virus inactivation over a period of 2 h. This makes the Hy-Cu filter reusable and a judicious substitute to the single use masks.

Prediction of respiratory droplets evolution for safer academic facilities planning amid COVID-19 and future pandemics: A numerical approach

Airborne dispersion of the novel SARS-CoV-2 through the droplets produced during expiratory activities is one of the main transmission mechanisms of this virus from one person to another. Understanding how these droplets spread when infected humans with COVID-19 or other airborne infectious diseases breathe, cough or sneeze is essential for improving prevention strategies in academic facilities. This work aims to assess the transport and fate of droplets in indoor environments using Computational Fluid Dynamics (CFD). This study employs unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations with the Euler-Lagrange approach to visualize the location of thousands of droplets released in a respiratory event and their size evolution. Furthermore, we assess the dispersion of coughing, sneezing, and breathing saliva droplets from an infected source in a classroom with air conditioning and multiple occupants. The results indicate that the suggested social distancing protocol is not enough to avoid the transmission of COVID-19 since small saliva droplets ( ≤ 12 μm) can travel in the streamwise direction up to 4 m when an infected person coughs and more than 7 m when sneezes. These droplets can reach those distances even when there is no airflow from the wind or ventilation systems. The number of airborne droplets in locations close to the respiratory system of a healthy person increases when the relative humidity of the indoor environment is low. This work sets an accurate, rapid, and validated numerical framework reproducible for various indoor environments integrating qualitative and quantitative data analysis of the droplet size evolution of respiratory events for a safer design of physical distancing standards and air cleaning technologies.

Occupant-centric robotic air filtration and planning for classrooms for Safer school reopening amid respiratory pandemics.

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.