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  5. Biodegradable Electrospun Poly(lactic acid) Nanofibers for Effective PM 2.5 Removal

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Article
en
2019

Biodegradable Electrospun Poly(lactic acid) Nanofibers for Effective PM 2.5 Removal

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0 Files

en
2019
Vol 304 (10)
Vol. 304
DOI: 10.1002/mame.201900259

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Jinxi Zhang
Shaobo Gong
Chenchen Wang
+3 more

Abstract

Abstract In addition to the rapid urbanization and industrialization around the world, air pollution due to particulate matter is a substantial threat to human health. A considerable research effort has been devoted to the development of electrospun polymer nanofibers for air filter applications. Among these new technologies, electrostatic charge‐assisted air filtration is a promising technology for removing small particulate matter (PM). In this investigation, biodegradable electrospun poly( l ‐lactic acid) (PLLA) polymer nanofibers are employed for air filter applications. Electrostatic charges generated from the PLLA nanofiber can significantly enhance air filter applications. Compared with a 3M commercial respirator filter, electrospun PLLA fibrous filters exhibit a high efficiency of 99.3%. Even after 6 h of filtration time, the PLLA filtration membrane still exhibits a 15% improvement in quality factor for PM 2.5 particles than the 3M respirator. This is mainly attributed to the electrostatic force generated from the electrospun PLLA nanofibers, which significantly benefit submicron particle absorption. Due to their biodegradability, ease of fabrication, and relatively high efficiency, electrospun PLLA nanofibers show great promise in applications such as air cleaning systems and personal air purifier applications.

How to cite this publication

Jinxi Zhang, Shaobo Gong, Chenchen Wang, Dae‐Yong Jeong, Zhong Lin Wang, Kailiang Ren (2019). Biodegradable Electrospun Poly(lactic acid) Nanofibers for Effective PM 2.5 Removal. , 304(10), DOI: https://doi.org/10.1002/mame.201900259.

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Publication Details

Type

Article

Year

2019

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/mame.201900259

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