Zagreb-type topological indices and entropy measures of zinc oxide and silicate networks
Zagreb-type topological indices and entropy measures of zinc oxide and silicate networks
Résumé
Metal–organic frameworks (MOFs) are attractive porous materials created by combining organic components with metals. These materials exhibit remarkable efficiency and have diverse applications in various medical fields. Recently, zinc-based MOFs have attracted significant attention because of their effective use in biosensing, cancer therapy, and drug delivery. Due to the various applications of MOFs, topological and entropy properties also emerge as important tools for development. This paper determines the new Zagreb-type topological indices
and their corresponding entropy measures for zinc oxide and silicate networks. Using Shannon’s entropy model, entropy measures based on the Zagreb-type topological indices are derived. The Zagreb-type topological indices and their entropy measures are compared through numerical computations and graphical representations. The relationship between Zagreb-type topological indices and the associated entropy measures is analyzed using curve fitting and Pearson correlation analysis.
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Références
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doi:10.1016/j.cej.2024.152377. ISSN 1385-8947.
[3] S. A. Lisa, S. Philip, H. Dominik, et al, Vectorial Catalysis in Surface Anchored Nanometer-Sized Metal–Organic Frameworks-Based Microfluidic
Devices, Angewandte Chemie International Edition. 61(8) (2022). doi: e202115100. doi:10.1002/anie.202115100.
[4] F. Zhiying, S. Lena, H. Karina, et al, Enhanced catalytic performance of palladium nanoparticles in MOFs by channel engineering, Cell Reports Physical
Science. 3(2) (2022). doi:10.1016/j.xcrp.2022.100757.
[5] S. R. Batten N. R, Champness, et at, Terminology of metal-organic frameworks and coordination polymers (IUPAC Recommendations 2013), Pure and Applied
Chemistry. 85(8) (2013) 1715–1724. doi:10.1351/PAC-REC-12-11-20.
[6] B. Thomas, C. Anthony, Amorphous Metal-Organic Frameworks, Accounts of Chemical Research. 47(5) (2014) 1555–1562. doi:10.1021/ar5000314.
[7] B. Thomas C. Fran¸cois-Xavier, et al, The changing state of porous materials, Nature Materials. 20(9) (2021) 1179–1187. doi:10.1038/s41563-021-00957-w.
[8] M. Mon, R. Bruno, J. Ferrando-Soria, et al, Metal-organic framework technologies for water remediation: towards a sustainable ecosystem, Journal of Materials
Chemistry A. 6(12) (2018) 4912–4947. doi:10.1039/c8ta00264a.
[9] J. Cejka J, Metal-Organic Frameworks Applications from Catalysis to Gas Storage, Wiley-VCH, 2011.
[10] W. Gao, M. Siddiqui, M. Naeem, N. Rehman, Topological characterization of carbon graphite and crystal cubic carbon structures, Mol. 22(9) (2017) 1496.
[11] S. Sharma, V.K. Bhat, S. Lal, The metric resolvability and topological characterization of some molecules in H1N1 antiviral drugs, Mol. Simul. 49(11) (2023)
1165–1178.
[12] M.F. Nadeem, M. Azeem, I. Farman, Comparative study of topological indices for capped and uncapped carbon nanotubes, Polycycl. Aromat. Compd. 42(7)
(2021) 4666–4683.
[13] X. Zhang, S Prabhu, M Arulperumjothi, S. M. Prabhu, M. Arockiaraj, V. Manimozhi, Distance-based topological characterization, graph energy prediction, and
NMR patterns of benzene ring embedded in P-type surface in 2D network, Scientific Reports. 14(1) (2024) 23766.
[14] Z. Raza, M. Arockiaraj, A. Maaran, S.R.J. Kavitha, K. Balasubramanian, Topological entropy characterization, NMR and ESR spectral patterns of
coronene-based transition metal-organic frameworks, ACS Omega. 8(14) (2023) 13371–13383.
[15] A. Rajpoot, L. Selvaganesh, Potential application of novel AL indices as molecular descriptors, J. Mol. Graph. Model. 118 (2023) 108353.
[16] M. Arockiaraj, J.C. Fiona, S.R.J. Kavitha, A.J. Shalini, K. Balasubramanian, Topological and spectral properties of wavy zigzag nanoribbons, Mol. 28(1) (2022) 152.
[17] A. Sattar, M. Javaid, Topological aspects of metal-organic frameworks: Zinc silicate and oxide networks, Computational and Theoretical Chemistry. 1222 (2023) 114056.
[18] M. T. Hussain, M. Javaid, U. Ali, A. Raza, Md Nur Alam, Comparing zinc oxide and zinc silicate-related metal-organic networks via connection-based Zagreb
indices, Journal of Chemistry. 2021. https://doi.org/10.1155/2021/5066394.
[19] M. Javaid, A. Sattar, On topological indices of zinc-based metal-organic frame works, Main Group Metal Chemistry. 45 (2022) 74–85.
[20] V. Ravi, K. Desikan, N. Chidambaram, On the computation of neighborhood degree sum-based topological indices for zinc-based metal-organic frameworks,
Main Group Metal Chemistry 46(2023) 20228043.
[21] R. Huang, M.F. Hanifb, M.K. Siddiqui, M.F. Hanif, On analysis of entropy measure via logarithmic regression model and Pearson correlation for Tri-s-triazine,
Computational Materials Science. 240 (2024) 112994.
[22] M. Arockiaraj, J. C. Fiona, A. J. Shalini, Comparative study of entropies in silicate and oxide frameworks, Silicon 16 (2024) 3205–3216.
https://doi.org/10.1007/s12633-024-02892-2.
[23] C.E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J. 27 (1948) 379-423.
[24] N. Rashevsky, Life, information theory, and topology, Bull. Math. Biol. 17 (1955) 229-235.
[25] M.M. Dehmer, M. Graber, The discrimination power of molecular identification numbers revisited, MATCH Commun. Math. Comput. Chem. 69 (2013) 785-794.
[26] A. Mowshowitz, M. Dehmer, Entropy and the complexity of graphs revisited, Entropy. 14 (2012) 559-570.
[27] S. Cao, M. Dehmer, Degree-based entropies of networks revisited, Appl.Math.Comput. 261 (2015) 141-147.
[28] S. Cao, M. Dehmer, Y. Shi, Extremality of degree-based graph entropies. Inf.Sci. 278 (2014) 22-33.
[29] M. Dehmer, L. Sivakumar, K. Varmuza, Uniquely discriminating molecular structures using novel eigenvalue-based descriptors, MATCH Commun. Math.
Comput. Chem. 67 (2012) 147-172.
[30] E. Estrada, N. Hatano, Statistical-mechanical approach to subgraph centrality in complex networks. Chem.Phys.Lett. 439 (2007) 247-251.
[31] E. Estrada, Generalized walks-based centrality measures for complex biological networks. J.Theor.Biol. 263 (2010) 556-565.
[32] D.S. Sabirov, I.S. Shepelevich, Information entropy in chemistry: an overview, Entropy. 23(10) (2021) 1240.
[33] R. Kazemi, Entropy of weighted graphs with the degree-based topological indices as weights, MATCH Commun. Math. Comput. Chem. 76 (2016) 69-80.
[34] M.P. Rahul, Joseph Clement, et al. Degree-based entropies of graphene, graphyne and graphdiyne using Shannon’s approach, Journal of Molecular Structure, 1210
(2022) 132797. https://doi.org/10.1016/j.molstruc.2022.132797.
Publiée
2025-10-30
Numéro
Rubrique
Research Articles
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