Computer-aided study on the two-dimensional hexagonal boron phosphide nanosheets for selective detection of ambient toxic gas

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Tao Hai, Ali Basem, Mohamad J. Alfaker, Shelesh Krishna Saraswat, Tariq J. Al-Musawi, Rohit Sharma, A. Elawady, Masoud Alajmi, Rahadian Zainul

2024 Physica B: Condensed Matter Vol. 691 Article Cited by 2 SDG 17SDG 7SDG 12 Quartile

Abstract

As industrialization progresses, numerous harmful gases are released into the atmosphere. However, detecting these gases at low concentrations poses a significant challenge. Consequently, there is an urgent need to create advanced toxic gas sensors that can effectively identify and measure these substances. The utilization of two-dimensional materials offers promising opportunities for gas sensors due to their notable benefits, including substantial distinctive electronic properties and specific surface area. In this research, a single layer of hexagonal boron phosphide (h-BP) was employed as the substrate material. Using density-functional theory (DFT) calculations, the researchers investigated how ambient toxic gases, namely CO, SO2, HCN, and CS2, are absorbed on the surface of monolayers of h-BP. The findings indicate that interaction between h-BP and these gases is characterized by physical adsorption. The researchers conducted calculations on four ideal adsorption structures to assess the Bader charge, density of states (DOS), work function, and recovery time. The results revealed notable alterations in electronic features and work function of the h-BP monolayer when it was exposed to SO2, indicating substantial modifications in electrical signals while detecting gas. Findings of this investigation demonstrate that h-BP monolayer exhibits enhanced sensitivity and selectivity towards the hazardous gas SO2. © 2024 Elsevier B.V.

Affiliations

School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang, 330108, China; School of Computer and Information, Qiannan Normal University for Nationalities, Guizhou, Duyun, 558000, China; Artificial Intelligence Research Center (AIRC), Ajman University, P.O. Box:346, Ajman, United Arab Emirates; Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq; Department of Petroleum Engineering, Al-Amarah University College, Maysan, Iraq; Department of Electronics and Communication Engineering Engineering, Faculty of Engineering and Technology, Jain (Deemed-to-be) University, Karnataka, Bengaluru, 560069, India; Building and Construction Techniques Engineering Department, College of Engineering and Engineering Techniques, Al-Mustaqbal University, Babylon, 51001, Iraq; Scientific Affairs Department, Al-Mustaqbal University, Babylon, 51001, Iraq; School of Engineering and Technology, Shobhit University, Uttar Pradesh, Gangoh, 247341, India; Department of Mechanical Engineering, Arka Jain University, Jharkhand, Jamshedpur, 831001, India; Independent Researcher, India; Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Negeri Padang, Indonesia; Center for Advanced Material Processing, Artificial Intelligence, and Biophysics Informatics (CAMPBIOTICS), Universitas Negeri Padang, Indonesia; INTI International University, Negeri Sembilan, Nilai, 71800, Malaysia; Superior University, Lahore, Pakistan; Department of Electrical Engineering, Vivekananda Global University, Rajasthan, Jaipur, 303012, India

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