Description Detail

Vol.24, No.3(2017-3)(163-166) 
A Study to Improve Filtration Efficiency by Nanofibers
奈米纖維增進過濾材性能的研究
Wen-Pin Hung, Hung-Yi Hsiao, Shu-Hsien Tsai, Pei-Ying Chao, Yung-Pin Huang
洪文濱, 蕭弘毅, 蔡書憲, 趙蓓瑩, 黃泳彬
Traditional non-woven filter material has been widely used in the filtration field. Faced with an increasingly serious air or liquid pollution problems, To improve the existing non-woven filtration efficiency, often resulting in disadvantages of high air resistance. Nanofibers have nanometer scale, high specific surface area and high porosity, it has been widely used in filter material. The use of nanofibers laminated with PET wet lay non-woven, can improve the performance of the wet lay non-woven air filtration materials, improving air filtration efficiency and reduce air resistance. Electrospinning is the most direct and effective method for the preparation of a nanofibers. In this paper, PAN electrospun nanofibers and deposition on PET wet lay non-woven to study the effects of air filtration performance. The results showed that, as deposition 1.21 g / m2 PAN nanofiber on PET wet lay non-woven , its pressure loss is only 9.8 mm H2O, but air filtration efficiency increase from 6.8% to 99.2% (32 LPM, 0.3μm particle), get the best filter performance, the performance of the filter quality factor is 0.47 for PET wet lay non-woven is the four times than the substrate. Therefore, the PAN nanofiber laminated with non-woven with low basis weight of less than 1.5 g / m2 can effectively improve the filtration performance of air filtration materials.
傳統不織布已廣泛應用於過濾材領域。面對日益嚴重的空氣或液體汙染問題,現有不織布為提高過濾效率,常會造成高空氣阻力的缺點。奈米纖維具有奈米尺度、高比表面積及高孔隙率,已被廣泛應用於過濾材。利用奈米纖維與PET濕式不織布複合,可以增進濕式不織布空氣過濾材的過濾性能,提高空氣過濾效率及降低空氣阻力。靜電紡絲是一製備奈米纖維的最直接有效的方法。本文以靜電紡絲PAN奈米纖維與PET濕式不織布複合,研究對空氣過濾性能的影響。結果顯示,以PAN奈米纖維1.21 g/m2複合PET濕式不織布,其壓損為9.8 mm H2O,空氣過濾效率由6.8%提升至99.2 %(32 LPM,0.3μm particle),可獲得最佳過濾性能,其過濾性能品質因子達0.47,為PET濕式不織布的四倍。因此,以PAN奈米纖維在低基重小於1.5 g/m2的複合即可有效提升空氣過濾材的過濾性能。
Nanofibers, Electrospinning, Filtration Performance
奈米纖維, 靜電紡絲, 過濾性能
Year Volume
2020 27.1 | 27.2
27.3 |
2019 26.1 |
2018 25.1 |
2017 24.1 | 24.2
24.3 | 24.4
24.5 | 24.6
24.7 | 24.8
24.9 |
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23.3 | 23.4
23.5 | 23.6
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22.5 |
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21.3 |
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20.3 | 20.4
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