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Article dans une revue

Graphene and g-C3N4-Based Gas Sensors

Abstract : The efficient monitoring of the environment is currently gaining a continuous growing interest in view of finding solutions for the global pollution issues and their associated climate change. In this sense, two-dimensional (2D) materials appear as one of highly attractive routes for the development of efficient sensing devices due, in particular, to the interesting blend of their superlative properties. For instance, graphene (Gr) and graphitic carbon nitride g-C3N4 (g-CN) have specifically attracted great attention in several domains of sensing applications owing to their excellent electronic and physical-chemical properties. Despite the high potential they offer in the development and fabrication of high-performance gas-sensing devices, an exhaustive comparison between Gr and g-CN is not well established yet regarding their electronic properties and their sensing performances such as sensitivity and selectivity. Hence, this work aims at providing a state-of-the-art overview of the latest experimental advances in the fabrication, characterization, development, and implementation of these 2D materials in gas-sensing applications. Then, the reported results are compared to our numerical simulations using density functional theory carried out on the interactions of Gr and g-CN with some selected hazardous gases' molecules such as NO2, CO2, and HF. Our findings conform with the superior performances of the g-CN regarding HF detection, while both g-CN and Gr show comparable detection performances for the remaining considered gases. This allows suggesting an outlook regarding the future use of these 2D materials as high-performance gas sensors.
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https://hal-u-picardie.archives-ouvertes.fr/hal-03641415
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Soumis le : jeudi 14 avril 2022 - 12:07:21
Dernière modification le : samedi 16 avril 2022 - 03:26:16

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Ahmed Kotbi, Mohammed Imran, Khaled Kaja, Ariful Rahaman, El Mostafa Ressami, et al.. Graphene and g-C3N4-Based Gas Sensors. JOURNAL OF NANOTECHNOLOGY, 2022, 2022, ⟨10.1155/2022/9671619⟩. ⟨hal-03641415⟩

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