Search for: All records

This study presents a method for the development of high-performance filter media with antibacterial properties from the nanofibers of recycled polyethylene terephthalate (PET), electrospun on used facemasks and sets the stage for generating high-performance filter media from recycled feedstocks with potential applications in personal protective equipment. The electrospinning solutions of PET contain 10 to 25 wt% of PET dissolved in a solvent mixture of hexafluoroisopropanol and dichloromethane. The electrospun nanofibers obtained from 10 wt % PET solution demonstrate the best performance in filtering airborne nanoparticles with a filtration efficiency of 87 ± 2.9 % and a quality factor of 1.31 per 2.54 cm of water pressure drop across the media. The study also evaluates the influence of benzalkonium chloride (BAC) salt initially dissolved in PET solution at a concentration of 10–50 wt% with respect to the PET content on nanofiber diameter and filtration efficiency. The presence of BAC leads to fiber diameter reduction, narrowing of fiber diameter distribution, and production of bead-free, smooth fibers. The filter media produced with BAC show filtration efficiency of 98 ± 0.8 % and a quality factor of 1.77 per 2.54 cm of water pressure drop, that exceed the filtration performance of a commercial N95 mask. The study establishes the antibacterial attributes of BAC, e.g., nanofibers containing BAC inhibit the growth of the gram-negative bacteria, Pseudomonas aeruginosa and the gram-positive bacteria, Staphylococcus aureus. The nanofiber mat, with 20 wt% or greater content of BAC, releases enough BAC in the aqueous media to inhibit the growth of both S. aureus and P. aeruginosa. 

This study investigates three types of foam core materials used in composite sandwich structures at various densities: H60, H100, F50, F90, PN115, PN200 and PN250. Three-point bending test is conducted to determine relationships between material and flexural properties at both room and low temperature Arctic conditions. X-ray micro-computed tomography is utilized to observe the microstructural relationships between foam density and mechanical properties of the core. This study evaluates Arctic temperature effects on mechanical properties for various types of foam core at varying densities with the intention for future Arctic applications. Although foam core materials become more brittle at a lower temperature, their flexural stiffness and flexural strength are further increased. However, due to the enhanced brittleness, the energy required for fracture is significantly reduced at low temperature conditions. This study utilizes statistical analysis to create contour plots and linear regression equations to predict flexural properties as a function of temperature and foam density. Molecular dynamics simulation is employed to verify experimental results to elucidate the effect of temperature on material behavior. This work provides a deeper understanding of how flexural strength relates to foam density, adding to existing data on foam strength properties under compressive, shear and tensile loads. 

This poster looks to apply machine learning in different aspects such as predicting dynamic viscosity of ionic liquids, determining parameters to generate a nonwoven mat through electrospinning, and predictions of extent of damage along with residual strength in fiber reinforced polymer composites. Through the use of machine learning we look to better understand the factors that go into each of these predictions and to eliminate time and costs in each process.