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Selective adsorption and dissociation of NO, NO2, and N2O molecules on Si-doped haeckelite boron nitride nanotube: an investigation for sensitive molecular sensors and catalysts

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Abstract

The current study describes the investigation of the adsorption NO, N2O, and NO2 on haeckelite boron nitride nanotube doped with Si (Si-doped haeck-BNNT) by means of density functional theory calculation (DFT). The obtained results confirmed the energetic stability of the optimized geometries and revealed that the adsorption of the gas molecules with the nanotube sidewall is a spontaneous process. The calculated work function of Si-doped haeck-BNNT in the presence of gas molecules is greater than that of a bare Si-doped haeck-BNNT sheet. The energy gap of the Si-doped haeck-BNNT is sensitive to the adsorption of the gas molecules, which implies possible future applications in gas sensors. For most of the adsorption configurations studied, the adsorption energies for the SiB-doped haeck-BNNT are higher than those for SiN-doped haeck-BNNTones. The N2O gas molecule is totally dissociated into N2 and O species through the adsorption process, while the other gas molecules retain their molecular forms. Thus, the SiN-doped haeck-BNNT is a likely catalyst for dissociation of the N2O gas molecule. Our findings divulge promising potential of the doped haeck-BNNT as a highly sensitive molecular sensor for NO and NO2 detection and a catalyst for N2O dissociation.

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Akbar Hassanpour: data curation, conceptualization. Maedeh Kamel: writing—review and editing, investigation, resources. Saeideh Ebrahimiasl: methodology, software, supervision. Abdol Ghaffar Ebadi: funding acquisition, project administration. Sattar Arshadi: data curation, supervision. Zahra ghulinezhad ahangari: writing—original draft.

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Correspondence to Maedeh Kamel.

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Hassanpour, A., Kamel, M., Ebrahimiasl, S. et al. Selective adsorption and dissociation of NO, NO2, and N2O molecules on Si-doped haeckelite boron nitride nanotube: an investigation for sensitive molecular sensors and catalysts. J Mol Model 28, 6 (2022). https://doi.org/10.1007/s00894-021-04981-0

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