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Ab initio characterization and experimental validation on the roles of oxygen-containing groups in graphene based formaldehyde sensors

Abstract

Development of formaldehyde (HCHO) sensors employing reduce graphene oxide (rGO) as sensing materials calls for a profound, atomic level understanding on the roles of oxygen-containing groups. In this work, the performances of rGO-based HCHO sensors were investigated with ab initio calculation and experimental validation. Density functional theory (DFT) simulations were performed to calculate the adsorption energy (Eads) and charge transfer (∆Q) for the adsorption of HCHO on pristine graphene, rGO with epoxides, rGO with hydroxyl group, and rGO with carboxyl group. Results show that the incorporation of oxygen-containing groups leads to an obvious increase of Eads and ∆Q, with an order of carboxyl group > hydroxyl group > epoxides > pristine graphene. The enhancement of Eads and ∆Q could increase the variation of concentration of charge carries, the conductance change of sensing materials, and hence the sensor response. Experimental measurements indicate that with the decreasing C/O atomic ratio from 16.2 to 6.6, the sensor response to 1 ppm HCHO increases from 0.10% to 0.73%, confirming the DFT calculation results. Moreover, even with a certain C/O atomic ratio of ~6.6, rGO with 6.80% carboxyl groups exhibits a distinctly larger response to 0.2-3 ppm HCHO, compared with the counterpart with 3.09% carboxyl groups. The as-obtained insights into the effects of oxygen-containing groups on the response of rGO to HCHO could be instructive in preparing rGO-based HCHO sensors for advanced performances.

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Supplementary files

Publication details

The article was received on 23 Jun 2017, accepted on 04 Sep 2017 and first published on 04 Sep 2017


Article type: Paper
DOI: 10.1039/C7AN01051F
Citation: Analyst, 2017, Accepted Manuscript
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    Ab initio characterization and experimental validation on the roles of oxygen-containing groups in graphene based formaldehyde sensors

    L. Duan, Z. Bo, X. Chen, H. Qi, J. Yan and K. Cen, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C7AN01051F

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