Particle Distribution and Heat Transfer of SiO2/Water Nanofluid in the Turbulent Tube Flow.

In order to clarify the effect of particle coagulation on the heat transfer properties, the governing equations of nanofluid together with the equation for nanoparticles in the SiO2/water nanofluid flowing through a turbulent tube are solved numerically in the range of Reynolds number 3000 ≤ Re ≤ 16,000 and particle volume fraction 0.005 ≤ φ ≤ 0.04. Some results are validated by comparing with the experimental results. The effect of particle convection, diffusion, and coagulation on the pressure drop ∆P, particle distribution, and heat transfer of nanofluid are analyzed. The main innovation is that it gives the effect of particle coagulation on the pressure drop, particle distribution, and heat transfer. The results showed that ∆P increases with the increase in Re and φ. When inlet velocity is small, the increase in ∆P caused by adding particles is relatively large, and ∆P increases most obviously compared with the case of pure water when the inlet velocity is 0.589 m/s and φ is 0.004. Particle number concentration M0 decreases along the flow direction, and M0 near the wall is decreased to the original 2% and decreased by about 90% in the central area. M0 increases with increasing Re but with decreasing φ, and basically presents a uniform distribution in the core area of the tube. The geometric mean diameter of particle GMD increases with increasing φ, but with decreasing Re. GMD is the minimum in the inlet area, and gradually increases along the flow direction. The geometric standard deviation of particle diameter GSD increases sharply at the inlet and decreases in the inlet area, remains almost unchanged in the whole tube, and finally decreases rapidly again at the outlet. The effects of Re and φ on the variation in GSD along the flow direction are insignificant. The values of convective heat transfer coefficient h and Nusselt number Nu are larger for nanofluids than that for pure water. h and Nu increase with the increase in Re and φ. Interestingly, the variation in φ from 0.005 to 0.04 has little effect on h and Nu.

Ontogenetic Transfer of Microplastics in Bloodsucking Mosquitoes Aedes aegypti L. (Diptera: Culicidae) Is a Potential Pathway for Particle Distribution in the Environment

The uptake and accumulation of microplastics (MPs) by bloodsucking mosquitoes Aedes aegypti L., carriers of vector-borne diseases, were investigated in the laboratory. In the experimental group, polystyrene (PS) particles were registered in insects of all life stages from larvae to pupae and adults. Ae. aegypti larvae readily ingested MPs with food, accumulating on average 7.3 × 106 items per larva in three days. The content of PS microspheres significantly decreased in mosquitoes from the larval stage to the pupal stage and was passed to the adult stage from the pupal without significant loss. On average, 15.8 items were detected per pupa and 10.9 items per adult individual. The uptake of MPs by Ae. aegypti did not affect their survival, while the average body weight of mosquitoes of all life stages that consumed PS microspheres was higher than that of mosquitoes in the control groups. Our data confirmed that in insects with metamorphosis, MPs can pass from feeding larvae to nonfeeding pupae in aquatic ecosystems and, subsequently, to adults flying to land. Bloodsucking mosquitoes can participate in MP circulation in the environment.

Automatic 3D cluster modelling of COVID-19 through voxel-based redistribution.

Computational analysis of virus dynamics provides a non-contact environment for the study of the vital object. Cluster modelling is an essential step to investigate the properties of a group of viruses, and an automatic approach is required for massive 3D data processing. The morphological complexity of individual virus limits the application of smooth function algorithms with a regular-shaped assumption. This paper proposed a voxel-based redistribution approach to generate the virus cluster with COVID-19 input automatically. Representative elementary volume analysis was performed to address the statistical influence from the digital sample size. Coordination number analysis and surface density measurement were conducted with COVID-19 input and spherical input for comparison. The proposed approach is in natural compatibility with the lattice Boltzmann method for fluid dynamics analysis. A virtual permeation simulation was performed with the COVID-19 cluster and spherical cluster to demonstrate the necessity to include spike protein structure in the cluster modelling.

Viral aerosol transmission of SARS-CoV-2 from simulated human emission in a concert hall.

The dispersion of aerosols was studied experimentally in several concert halls to evaluate their airborne route and thus the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreading. For this, a dummy was used that emits simulated human breath containing aerosols (mean diameter of 0.3 µm) and CO2, with a horizontal exhalation velocity of v = 2.4 m/s, measured 10 cm in front of the mouth. Aerosol and CO2 concentration profiles were mapped using sensors placed around the dummy. No substantial enrichment of aerosols and CO2 was found at adjacent seats, provided that (1) there were floor displacement outlets under each seat enabling a minimum local fresh air vertical flow of vv = 0.05 m/s, (2) the air exchange rate (ACH) was more than 3, and (3) the dummy wore a surgical face mask. Knowledge of dispersion of viral droplets by airborne routes in real environments will help in risk assessment when re-opening concert halls and theatres after a pandemic lockdown.

Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning.

The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 µm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.