Issue 14, 2023

A curtain purification system based on a rabbit fur-based rotating triboelectric nanogenerator for efficient photocatalytic degradation of volatile organic compounds

Abstract

Efficient removal of air pollution caused by volatile organic compounds (VOCs) and particulate matter (PM) through distributed energy collected from the environment is an effective strategy to achieve both energy conservation and better air quality. Herein, a curtain purification system based on a rabbit fur-based rotary triboelectric nanogenerator (RR-TENG) and a collaborative photocatalysis technology was designed for indoor air purification. The high electrostatic field from RR-TENG enhances formaldehyde adsorption, while it can also efficiently adsorb PM2.5 simultaneously. More interestingly, the ultrahigh electric field provided by RR-TENG promotes the separation of photogenerated electron–hole pairs of the g-C3N4/TiO2 composite photocatalyst, generating more superoxide radicals (⋅O2−), hydroxyl radicals (⋅OH), and holes (h+) and thereby improving the photocatalytic efficiency. In a simulated reaction chamber of 9 L, the formaldehyde removal rate of the system can reach 79.2% within 90 min and RR-TENG rapidly reduces PM2.5 from 999 μg m−3 to 50 μg m−3 within 60 s. This study proposes a curtain purification system integrating the function of energy collection and photocatalytic purification, which can be applied for improving air quality and human health.

Graphical abstract: A curtain purification system based on a rabbit fur-based rotating triboelectric nanogenerator for efficient photocatalytic degradation of volatile organic compounds

Supplementary files

Article information

Article type
Paper
Submitted
03 Feb 2023
Accepted
03 Mar 2023
First published
07 Mar 2023

Nanoscale, 2023,15, 6709-6721

A curtain purification system based on a rabbit fur-based rotating triboelectric nanogenerator for efficient photocatalytic degradation of volatile organic compounds

D. Yang, Z. Liu, P. Yang, L. Huang, F. Huang, X. Tao, Y. Shi, R. Lei, J. Cao, H. Li, X. Chen and Z. Bian, Nanoscale, 2023, 15, 6709 DOI: 10.1039/D3NR00507K

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