Cases of respiratory disease transmission in enclosed elevators have been reported frequently. In the post-pandemic era, in order to mitigate the spread of respiratory diseases in moving elevators, a multi-objective genetic optimization method based on a response surface model is used to optimize the elevator ventilation. The ventilation parameters were optimized for three objectives: reducing carbon dioxide concentration, maintaining human thermal comfort, and achieving energy conservation. First, a response surface model is established using the computational fluid dynamics method and the Kriging model to correlate the design variables (air supply velocity in x, y, and z directions and air supply temperature) with the output function (CO2 concentration, average temperature, and average velocity). Subsequently, the Pareto optimal solution set of ventilation parameters was obtained by employing a multi-objective genetic algorithm. Finally, the optimal air supply velocity, angle, and temperature were obtained for both peak periods of elevator traffic (13 passengers) and other situations (4 passengers) when the elevator is moving up and down, which satisfy the objectives of health, comfort, and energy conservation.

Polysulfide-ferricyanide redox flow batteries (PFRFBs) are gaining significant attention in long-duration energy storage for their abundant availability and environmental benignity. However, the sluggish kinetics of the polysulfide redox reactions have tremendously constrained their performances. To address this issue, we developed a NiMoS catalyst-modified carbon felt (NiMoS-CF) electrode, which significantly accelerates the electrochemical reaction rates and enhances the cycling stability of PFRFB. Our PFRFB system, integrated with the NiMoS-CF electrode, exhibited an energy efficiency of 70% and a voltage efficiency of 87%, with a remarkable doubling of its cycle life as opposed to the pristine carbon felt (CF) electrode at a current density of 40 mA cm⁻². Notably, during 2500 cycles of charge–discharge testing, we achieved an average coulombic efficiency exceeding 99%. These improvements in PFRFB performance can be attributed to the NiMoS-CF electrode’s large surface area, low resistance, and robust redox activity. This study offerings a novel approach for enhancing the electrochemical reaction kinetics and cycling stability in PFRFBs, laying a scientific foundation in the applications of practical PFRFBs for next-generation energy storage.

It is necessary to explore the effects of airflow in moving elevator cars on the diffusion of droplet nuclei and evaluate inhalation and touching risks due to the elevator car being a place where dangerous respiratory diseases can spread. A realizable k–ε turbulence model and discrete phase model were applied with gas–solid multiphase modeling principles to simulate the diffusion of droplet nuclei generated by coughs in a moving elevator. Four cases are discussed with differences in the elevator motion direction and air supply system. The results show that the elevator movement causes the airflow direction in respiratory regions to change by more than 30°, which is more evident in the absence of air supply. Under the combined effects of the airflow supply and the thermal plume, the high-temperature zone became a gathering point for droplet nuclei. The upward elevator movement increases the inhalation risk of a passenger who stands farther from an index patient and has the highest inhalation risk about 4.4 × 10⁻¹⁹. The downward movement increases the duration to about 40 s, for which that passenger remains at the highest risk. The area in front of the patient is the highest touching risk area, especially for downward motion. In addition, the button area is a high-risk area for touching because about 4% of cough droplet nuclei are deposited there. This may spread viable viruses if passengers touch the buttons, even if the coughing person is no longer in the elevator. Thus, elevator cars need to be regularly disinfected, especially the button area.

Smart classrooms are a relatively confined public space for college students. SARS-COV-2 and other respiratory viruses have been shown to pose a more significant threat to human health in relatively confined spaces. Using numerical simulation method to simulate the transmission and concentration distribution of virus-carrying droplets in smart classrooms in three different seasons (summer, winter, transitional seasons: spring and autumn). The Realizable k-epsilon model is used to simulate the airflow pattern in the smart classroom, and the Lagrangian method is used to simulate the transmission of droplets. The transmission process of droplets produced from the teacher standing on the platform and the student sitting on the seat is studied. The influence of three kinds of outdoor temperature on droplet transmission and the body deposition fraction of people in the smart classroom is analyzed. The results show that droplet transmission speed is maximum at the temperature of 5 degrees when the outdoor temperature is 5 degrees C, 20 degrees C, and 35 degrees C respectively. At 10 s, the transmission distance of droplets increases by 9.55% compared with that at 20 degrees C and 10.31% compared with that at 35 degrees C. In addition, the body deposition fraction is also affected by the location of the vent, with downwind contact being 6 times more likely than upwind contact. The research results can provide suggestions and measures for epidemic prevention and control in smart classrooms.

Vehicle acceleration typically occurs at traffic lights, intersections, or congested sections within urban streets, where high densities of pedestrians and vehicles pose a direct threat to respiratory health due to PM_2.5 dispersion. Computational Fluid Dynamics (CFD) simulations, combined with the Dynamic Mode Decomposition (DMD) method, are used to analyze the dynamic characteristics of PM_2.5 dispersion during vehicle acceleration. The DMD method can effectively analyze the dynamic change in pollutant concentration in an unsteady flow field and clarify the influence mechanism of vehicle acceleration on pollutant dispersion. The results indicate that PM_2.5 dispersion during the initial stage of acceleration is primarily influenced by low-frequency and large-scale flows, such as exhaust emissions, natural wind, and trailing vortices. In the middle stage, PM_2.5 dispersion tends to stabilize, while in the final stage, high-frequency modes dominate, and intense flow field fluctuations significantly enhance PM_2.5 dispersion. Furthermore, the analysis reveals the critical role of upward and downward airflow phenomena around the vehicle in driving PM_2.5 dispersion. This study offers a new perspective on the dispersion characteristics of PM_2.5 under unsteady flow conditions in urban streets and provides a scientific basis for developing speed management strategies to mitigate the impact of pollutant dispersion.