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High-performance NO2 sensors based on spontaneously functionalized hexagonal boron nitride nanosheets via chemical exfoliation

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Abstract

Atomically thin hexagonal boron nitride nanosheets (BNNSs) have been widely explored for various applications due to their unique properties; however, sensing gas molecules with high sensitivity and selectivity remains challenging due to their inherently low chemical reactivity. Herein, we report a chemiresistor-type NO2 gas sensor based on chemically exfoliated sulfate-modified BNNSs (S-BNNSs) with high sensitivity, fast response, excellent selectivity and good reversibility. The response behaves linearly in a wide NO2 concentration range, giving a high sensitivity of 1.645 ppm−1. More intriguingly, the limit of detection (LOD) of this S-BNNS based sensor is experimentally found to be as low as 20 ppb, apparently much lower than the threshold exposure limit required by the American Conference of Governmental Industrial Hygienists (200 ppb). Our theoretical calculations reveal that the sulfate groups spontaneously grafted to S-BNNSs can effectively alter their electronic structure and enhance the surface adsorption capability towards NO2. This is accompanied by a strong charge transfer induced by adsorbed NO2, consequently improving the sensing performance. This work extends the potential of functionalized S-BNNSs in a wide range of NO2 sensing and/or capturing applications, such as environmentally hazardous vehicle exhaust and combustion emission monitoring, just to name a few.

Graphical abstract: High-performance NO2 sensors based on spontaneously functionalized hexagonal boron nitride nanosheets via chemical exfoliation

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Publication details

The article was received on 20 Aug 2019, accepted on 17 Oct 2019 and first published on 18 Oct 2019


Article type: Paper
DOI: 10.1039/C9NR07153A
Nanoscale, 2019, Advance Article

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    High-performance NO2 sensors based on spontaneously functionalized hexagonal boron nitride nanosheets via chemical exfoliation

    Y. He, D. Li, W. Gao, H. Yin, F. Chen and Y. Sun, Nanoscale, 2019, Advance Article , DOI: 10.1039/C9NR07153A

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