Built Environment & Energy Laboratory
Let the dream set sail

2020

Bian, Y., Chen, C., Wang, R., Wang, S., Pan, Y., Zhao, B., Chen, C.*, Zhang, L.* (2020). Effective removal of particles down to 15 nm using scalable metal-organic framework-based nanofiber filters. Applied Materials Today, 20, 100653. (Particle filtration, Ultrafine particle, Building)


Air pollution is currently a huge threat to human health, which leads to heavy demand for efficient air filters for filtration. Among which, ultrafine particles (UFPs, diameter less than 100 nm) can especially result in more severe health diseases. Here, a scalable metal-organic framework (MOF)-based nanofiber filter for high-efficiency particles ranged from 15 nm to 10 μm removal with facile and rapid one-step fabrication is originally presented. Note that the proposed strategy is only applicable for MOFs that do not require heating during synthesis. The MOF-filter has a high filtration efficiency of 99.1% for UFPs. For particle with size down to 15 nm, the filter also exhibits remarkable removal efficiency of 98.8%. The related particle removal mechanisms for the MOF composite filter were studied systematically. Furthermore, the durability and cleanability of MOF-filter were also validated. This work may shed light on the development of nanofibrous heterostructures for high-efficient nanoparticles removal, which hold great promise for reducing health risks. 

Pan, Y., Lin, C.-H., Wei, D., Chen, C.* (2020). Influence of surface roughness on particle deposition distribution around multi-slot cabin supply air nozzles of commercial airplanes. Building and Environment, 176, 106870. (Particle deposition, Particulate matter, Aircraft cabin)


Enhanced soiling due to particle deposition is often observed around multi-slot cabin supply air nozzles in commercial airplanes. This study aimed to investigate the influence of surface roughness on the particle deposition distribution around multi-slot cabin supply air nozzles of commercial airplanes. This investigation constructed a half-row cabin mockup installed with three 3D-printed supply air nozzles of a twin-aisle commercial airplane. A cutting method was proposed to measure the detailed particle deposition velocity distribution on the target surface of the nozzles covered by different grades of sandpaper with different roughness heights. This study also conducted numerical calculations for the particle deposition velocity distribution using an Eulerian particle deposition model. Both the experimental and modeling results show that strong particle deposition occurred on the cut samples near the slot dividers. When the surface roughness height was greater than or equal to 6 µm, the particle deposition velocity increased significantly with the surface roughness height. However, when the surface roughness height was less than or equal to 3 µm, the particle deposition velocity was relatively insensitive to the surface roughness. The analysis indicated that polishing the surfaces of cabin supply air nozzles may not effectively solve the problem of enhanced soiling in the aircraft cabin. 

Xia, T., Chen, C.* (2020). Toward understanding the evolution of incense particles on nanofiber filter media: its influence on PM2.5 removal efficiency and pressure drop. Building and Environment, 172, 106725. (Particle filtration, Particulate matter, Building)


Nanofiber filter media can potentially reduce exposure to PM2.5 in indoor environments because of the filters’ high particle-removal efficiency. To facilitate such filter use, this study conducted a series of experiments to understand the evolution of wetting liquid aerosols, taking incense particles as an example, on nanofiber filter media and the influence of this evolution on PM2.5 removal efficiency and pressure drop. Scanning electron microscope images were also taken to observe the nanoscale interactions between incense particles and the nanofiber network. The results show that the PM2.5 removal efficiency at first decreased as the loading mass increased, because interactions between the particles and the nanofiber network enlarged the pores. The evolution of pressure drop may consist of two stages, i.e., a first stage with a linear relationship, and a second stage with a steep increase in pressure drop with the loading mass. When the pore size became small enough, in addition to inertial impaction and Brownian diffusion, the capture mechanism of interception also became significant. Consequently, the second stage, with a steep increase, tended to occur. Finally, methods for establishing empirical equations for PM2.5 removal efficiency and pressure drop as a function of loading mass were proposed. 

Bian, Y., Wang, S., Zhang, L.*, Chen, C.* (2020). Influence of fiber diameter, filter thickness, and packing density on PM2.5 removal efficiency of electrospun nanofiber air filters for indoor applications. Building and Environment, 170,106628. (Particle filtration, Particulate matter, Building)


Electrospinning is a versatile technique to fabricate nanofiber filters with high PM2.5 removal efficiency and relatively low pressure drop. The eletrospun nanofiber filters may therefore be applied in buildings to reduce indoor exposure to PM2.5 and the associated adverse health effects. This study investigated the influence of various filter parameters, including fiber diameter, filter thickness, and packing density, on the PM2.5 removal efficiency. In this work, 25 nylon electrospun nanofiber filters with different filter parameters were prepared, and the PM2.5 removal efficiency of each sample was measured at five different face velocities. In total, 125 sets of measured data were obtained. The results show that the PM2.5 removal efficiency of nylon electrospun nanofiber filters was negatively associated with the fiber diameter, and positively associated with the thickness of the filter. However, there was no clear correlation between PM2.5 removal efficiency and packing density. This investigation further developed a semi-empirical model for predicting the PM2.5 removal efficiency of nylon nanofiber filters. The accuracy of the model was satisfactory with a median relative error of 7.9%.

2019 Particle