Issue 31, 2019

A novel method for textile odor removal using engineered water nanostructures

Abstract

The malodor attached to textiles not only causes indoor environmental pollution but also endangers people's health even at low concentrations. Existing technologies cannot effectively eliminate the odor. Herein, an effective and environmentally friendly technology was proposed to address this challenging issue. This technology utilizes electrospraying process to produce Engineered Water Nanostructures (EWNS) in a controllable manner. Upon application of a high voltage to the Taylor cone, EWNS can be generated from the condensed vapor water through a Peltier element. Smoking, cooking and perspiration, considered the typical indoor malodorous gases emitted from human activities, were studied in this paper. A headspace SPME method in conjunction with GC-MS was employed for the extraction, detection and quantification of any odor residues. Results indicated that EWNS played a significant role in the deodorization process with removal efficiencies for the three odors were 95.3 ± 0.1%, 100.0 ± 0.0% and 43.7 ± 2.3%, respectively. The Reactive Oxygen Species (ROS) contained in the EWNS, mainly hydroxyl (OH˙) and superoxide radicals Image ID:c9ra01988j-t1.gif are the possible mechanisms for the odor removal. These ROS are strong oxidative and highly reactive and have the ability to convert odorous compounds to non-odorous compounds through various chemical reaction mechanisms. This study showed clearly the potential of the proposed method in the field of odor removal and can be applied in the battle against indoor air pollution.

Graphical abstract: A novel method for textile odor removal using engineered water nanostructures

Supplementary files

Article information

Article type
Paper
Submitted
15 Mar 2019
Accepted
15 May 2019
First published
05 Jun 2019
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2019,9, 17726-17736

A novel method for textile odor removal using engineered water nanostructures

L. Zhu, Y. Liu, X. Ding, X. Wu, W. Sand and H. Zhou, RSC Adv., 2019, 9, 17726 DOI: 10.1039/C9RA01988J

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