Copyright (c) 2025 md sayeed t, Kavil J, Anitha PK
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Computational DFT Studies of CO Adsorption on Different 2D Materials
Corresponding Author(s) : T. Md Sayeed
Asian Journal of Chemistry,
Vol. 37 No. 8 (2025): Vol 37 Issue 8, 2025
Abstract
Computational density functional theory (DFT) has been employed to predict the carbon monoxide (CO) adsorption on the surface of four studied different 2D materials like boron nitride, graphene, silicene and germanene. This study utilizes DFT calculations for graphene, two-dimensional hexagonal boron nitride, silicene, and germanene, along with their heterostructures with carbon monoxide. The stability of these materials has also been assessed. Adsorption energy is estimated and compared using self-consistent field calculations. The charge density distribution plot is thoroughly examined to confirm the bonding characteristics and electron delocalization in the relevant 2D material and adsorbed heterostructure. The findings from the calculation of CO adsorption energy revealed that graphene is a more effective adsorbate than boron nitride, whereas germanene demonstrates better performance than silicene, supported by consistent results from the adsorption energy analysis. The adsorption energy from self-consistent field energy calculations matches the charge distribution of two-dimensional materials after CO adsorption.
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- C.N.R. Rao, A.K. Sood, K.S. Subrahmanyam and A. Govindaraj, Angew. Chem. Int. Ed. Engl., 48, 7752 (2009); https://doi.org/10.1002/anie.200901678
- S. Hofmann, P. Braeuninger-Weimer and R.S. Weatherup, J. Phys. Chem. Lett., 6, 2714 (2015); https://doi.org/10.1021/acs.jpclett.5b01052
- D. Jose and A. Datta, Acc. Chem. Res., 47, 593 (2014); https://doi.org/10.1021/ar400180e
- T. Giousis, G. Potsi, A. Kouloumpis, K. Spyrou, Y. Georgantas, N. Chalmpes, K. Dimos, M.-K. Antoniou, H.J. Kim, G. Papavassiliou, A.B. Bourlinos, H.J. Kim, V.K.S. Wadi, S. Alhassan, M. Ahmadi, B.J. Kooi, G. Blake, D.M Balazs, M.A. Loi, D. Gournis and P. Rudolf, Angew. Chem. Int. Ed. Engl., 60, 360 (2020); https://doi.org/10.1002/anie.202010404
- A. Molle, J. Yuhara, Y. Yamada-Takamura and Z. Sofer, Chem. Soc. Rev., 54, 1845 (2025); https://doi.org/10.1039/D4CS00999A
- E. Bianco, S. Butler, S.Jiang, O.D. Restrepo, W. Windl and J.E. Goldberger, ACS Nano, 7, 4414 (2013); https://doi.org/10.1021/nn4009406
- W. Kohn and L.J. Sham, Phys. Rev., 140(4A), A1133 (1965); https://doi.org/10.1103/PhysRev.140.A1133
- P. Giannozzi, O. Baseggio, P. Bonfà, D. Brunato, R. Car, I. Carnimeo, C. Cavazzoni, S. de Gironcoli, P. Delugas, F. Ferrari Ruffino, A. Ferretti, N. Marzari, I. Timrov, A. Urru and S. Baroni, J. Chem. Phys., 152, 154105 (2020); https://doi.org/10.1063/5.0005082
- I, Kurnia, P, Siahaan and A, Suseno, IOP Conf. Ser.: Mater. Sci. Eng., 959, 012004 (2020); https://doi.org/10.1088/1757-899X/959/1/012004
- C. Kamal, arXiv, 1202, 2636 (2012); https://doi.org/10.48550/arXiv.1202.2636
- A.R. Soltani and M.T. Baei, Computation, 7, 61 (2019); https://doi.org/10.3390/computation7040061
- B. Feng, Z. Ding, S. Meng, Y. Yao, X. He, P. Cheng, L. Chen and K. Wu, Nano Lett., 12, 3507 (2012); https://doi.org/10.1021/nl301047g
- https://www.quantum-espresso.org/documentation/input-data-description/
References
C.N.R. Rao, A.K. Sood, K.S. Subrahmanyam and A. Govindaraj, Angew. Chem. Int. Ed. Engl., 48, 7752 (2009); https://doi.org/10.1002/anie.200901678
S. Hofmann, P. Braeuninger-Weimer and R.S. Weatherup, J. Phys. Chem. Lett., 6, 2714 (2015); https://doi.org/10.1021/acs.jpclett.5b01052
D. Jose and A. Datta, Acc. Chem. Res., 47, 593 (2014); https://doi.org/10.1021/ar400180e
T. Giousis, G. Potsi, A. Kouloumpis, K. Spyrou, Y. Georgantas, N. Chalmpes, K. Dimos, M.-K. Antoniou, H.J. Kim, G. Papavassiliou, A.B. Bourlinos, H.J. Kim, V.K.S. Wadi, S. Alhassan, M. Ahmadi, B.J. Kooi, G. Blake, D.M Balazs, M.A. Loi, D. Gournis and P. Rudolf, Angew. Chem. Int. Ed. Engl., 60, 360 (2020); https://doi.org/10.1002/anie.202010404
A. Molle, J. Yuhara, Y. Yamada-Takamura and Z. Sofer, Chem. Soc. Rev., 54, 1845 (2025); https://doi.org/10.1039/D4CS00999A
E. Bianco, S. Butler, S.Jiang, O.D. Restrepo, W. Windl and J.E. Goldberger, ACS Nano, 7, 4414 (2013); https://doi.org/10.1021/nn4009406
W. Kohn and L.J. Sham, Phys. Rev., 140(4A), A1133 (1965); https://doi.org/10.1103/PhysRev.140.A1133
P. Giannozzi, O. Baseggio, P. Bonfà, D. Brunato, R. Car, I. Carnimeo, C. Cavazzoni, S. de Gironcoli, P. Delugas, F. Ferrari Ruffino, A. Ferretti, N. Marzari, I. Timrov, A. Urru and S. Baroni, J. Chem. Phys., 152, 154105 (2020); https://doi.org/10.1063/5.0005082
I, Kurnia, P, Siahaan and A, Suseno, IOP Conf. Ser.: Mater. Sci. Eng., 959, 012004 (2020); https://doi.org/10.1088/1757-899X/959/1/012004
C. Kamal, arXiv, 1202, 2636 (2012); https://doi.org/10.48550/arXiv.1202.2636
A.R. Soltani and M.T. Baei, Computation, 7, 61 (2019); https://doi.org/10.3390/computation7040061
B. Feng, Z. Ding, S. Meng, Y. Yao, X. He, P. Cheng, L. Chen and K. Wu, Nano Lett., 12, 3507 (2012); https://doi.org/10.1021/nl301047g
https://www.quantum-espresso.org/documentation/input-data-description/
