Modeling and simulation of carbon nanotube amino-acid sensor: A first-principles study

[Display omitted] •Amino acid sensing is investigated using Carbon Nanotubes.•DFT calculations are done in combination with non-equilibrium Green’s function.•Interaction energy plots show adsorption of amino-acids on CNT surface.•Conductance measurements provide the detection mechanism of different...

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Veröffentlicht in:Computational and theoretical chemistry 2021-10, Vol.1204, p.113402, Article 113402
Hauptverfasser: Parvaiz, M. Shunaid, Shah, Khurshed A., Alrobei, H., Dar, G.N., Khanday, Farooq A., Muzaffar Ali Andrabi, S., Hamid, Rabia
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Sprache:eng
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Zusammenfassung:[Display omitted] •Amino acid sensing is investigated using Carbon Nanotubes.•DFT calculations are done in combination with non-equilibrium Green’s function.•Interaction energy plots show adsorption of amino-acids on CNT surface.•Conductance measurements provide the detection mechanism of different amino acids.•Molecular dynamics simulation shows the interaction of molecules at CNT surface. In this study, we have investigated a single-walled carbon nanotube (SWCNT) based device to detect the essential amino acids, Glutamine and Tryptophan, by analyzing changes in various properties of the device. The models were simulated using density functional theory in combination with non-equilibrium Green’s function formalism. Furthermore, interaction energy plots are obtained and a molecular dynamics study is done to investigate the interactions of the molecules with the SWCNT surface. The results show significant changes in the current and conductance due to the alteration in the geometry and aromaticity of the SWCNT system. The results also show suppressed density of states (DOS), leading to an increase in the electron density. Molecular dynamics study shows that the energy of the system remains conserved up to some small fluctuations, maintaining the stability of the system during the interaction. Thus, amino acid detection is reported using CNTs indicating reliable applications in medical diagnosis.
ISSN:2210-271X
DOI:10.1016/j.comptc.2021.113402