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DFT Studies of Photocatalytic Properties of Silicon- and Boron-doped Gallium-nitride Nanotubes for Hydrogen Evolution and Carbondioxide Capture

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Abstract

Using density functional theory, this work investigated the photocatalytic potentials of gallium nitride nanotubes (GaNNTs) under separate doping of Si and B impurities. The study was performed by analyzing various parameters such as structural properties, band gap, effects of bond length variation, photogeneration properties, as well as analyzing overpotential values. Structural analysis results showed overall good stability when 3.12% of Si and B dopants were used separately on pristine GaNNT. The most stable bond length for doping GaNNT with either B or N atoms was 1.47 Å, which is in good agreement with previously reported experimental data. It was also found that Si-doped GaNNT absorbs light more than B-doped GaNNT. In addition, all doped systems absorb more light in parallel compared to the perpendicular direction of the axis of the nanotubes. Based on the obtained data, it was found that both Si-doped and B-doped GaNNT are suitable as photocatalysts for CO2 reduction and hydrogen evolution. The practical implications of our findings is the ability of the studied materials to help reduce atmospheric contamination caused by excessive emission of greenhouse gases.

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Acknowledgements

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RP23019)

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Y.S. Itas developed the concept and wrote the main manuscript, S.T., R.R., M.U.K. and M.K. and S.A.A. reviewed the manuscript, S.A.A. and J.E.G. Supported the theoretical methodology, R. Razali provided the materials and computational packages, M.U.K. provided technical, theoretical and conceptual reviews.

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Correspondence to Yahaya Saadu Itas.

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Itas, Y.S., Razali, R., Tata, S. et al. DFT Studies of Photocatalytic Properties of Silicon- and Boron-doped Gallium-nitride Nanotubes for Hydrogen Evolution and Carbondioxide Capture. Silicon 16, 2757–2770 (2024). https://doi.org/10.1007/s12633-024-02872-6

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