Emerging role of graphene oxide as sorbent for pesticides adsorption: Experimental observations analyzed by molecular modeling

doi:10.1016/j.jmst.2020.02.033

Abstract

Abstract:

The accumulation of pesticide residues in the environment due to their persistence and stability is causing increasing health concern. Indeed, researchers have rekindled their interest in eliminating pesticides from the environment by a range of biological and chemical approaches. In particular, graphene oxide (GO) has drawn great attention because it impressively enhances adsorption of pesticides in aqueous solutions, which provides promising environmental applications on water purification to remove pesticide residuals. However, although multiple studies have highlighted the adsorption of environmental contaminants by GO, the underlining molecular mechanisms remain limited. Consequently, we further delved into the knowledge regarding their adsorption molecular mechanism that is of both practical and theoretical importance. It was revealed that the π-π stacking and van der Waals interactions accounted for the major adsorption interactions between GO and its removing pesticides through integrating both density functional theory (DFT) calculation, fully atomistic molecular dynamics (MD) simulation, and binding free energy calculation. These findings not only bridged the theoretical gap of the adsorption mechanisms of GO, but also provided a venue for visualizing the adsorption process, which were essential for guiding its future adsorption applications.

Key words: Graphene oxide, Environmental contaminants, Density functional theory, Molecular dynamics simulation, Binding free energy calculation

Hanxun Wang, Baichun Hu, Zisen Gao, Fengjiao Zhang, Jian Wang. Emerging role of graphene oxide as sorbent for pesticides adsorption: Experimental observations analyzed by molecular modeling[J]. J. Mater. Sci. Technol., 2021, 63: 192-202.

Figures/Tables 14

Table 1 Computational properties of pesticides.

Name	logP	Solvent accessible surface (?²)	Polar surface area (?²)	GlideScore (kcal/mol)
Carbaryl	2.496	407.88	38.33	-21.463
Catechol	0.987	256.64	40.46	-13.511
Fluridone	3.416	513.51	17.07	-29.230

Fig. 1. Structures, binding energies and lipophilic surfaces of carbaryl, catechol and fluridone. The surfaces are shown in solid styles. Warm colors indicate lipophilic features, whereas cold colors indicate hydrophilic features.

Table 2 Energy terms for pesticides interacting with graphene oxide.

Pesticides	Total energy (kcal/mol)	Van der Waal (kcal/mol)	Electrostatic (kcal/mol)
Carbaryl	-19.36	-17.56	-1.80
Catechol	-17.11	-10.05	-7.06
Fluridone	-27.17	-24.76	-2.41

Fig. 2. Solvent environment for molecular dynamic simulation of carbaryl (orange CPK), catechol (green CPK) and fluridone (blue CPK) with graphene oxide.

Fig. 3. Dynamics process of carbaryl (orange sticks), catechol (green sticks) or fluridone (blue sticks) interacting with graphene oxide (thin grey sticks) during the whole MD simulations.

Fig. 4. Conformational distribution of carbaryl (orange sticks), catechol (green sticks), or fluridone (blue sticks) surrounding graphene oxide extracted from MD frames. Graphene oxide backbones were shown as thin grey sticks.

Fig. 5. Binding patterns and MD properties of carbaryl (orange sticks), catechol (green sticks), or fluridone (blue sticks) with graphene oxide obtained from MD simulation. Dotted blue lines indicated ??-?? stacking contacts. MD properties were monitored during MD simulation, including Ligand RMSD, Radius of Gyration (rGyr), Molecular Surface Area (MolSA), Solvent Accessible Surface Area (SASA), and Polar Surface Area (PSA).

Fig. 6. Simulation event analysis for carbaryl. (A) The dihedral was defined according to the relative position of carbaryl (orange stick) and graphene oxide (grey stick). (B) Time evolution of planar angles in carbaryl-graphene oxide complex. (C) Frequency of planar angles of carbaryl conformations from MD trajectory. (D) Planar angle distribution of carbaryl conformations from MD trajectory in the polar scatter plot. The scatter points stood for conformations, and concentric circles represented the simulation time.

Fig. 7. Simulation event analysis for catechol. (A) The dihedral was defined according to the relative position of catechol (green stick) and graphene oxide (grey stick). (B) Time evolution of planar angles in catechol-graphene oxide complex. (C) Frequency of planar angles of catechol conformations from MD trajectory. (D) Planar angle distribution of catechol conformations from MD trajectory in the polar scatter plot. The scatter points stood for conformations, and concentric circles represented simulation time.

Fig. 8. Simulation event analysis for fluridone. (A) The three involving dihedrals were defined according to the relative position of fluridone (blue stick) and graphene oxide (grey stick). (B) Time evolution of benzene ring planar angles (the top panel), the frequency of planar angles of benzene ring in fluridone conformations (the bottom left panel), and the planar angle distribution of benzene ring in fluridone conformations in fluridone-graphene oxide complex (the bottom right panel). (C) Time evolution of N-methylpyridin-4(1H)one ring planar angles (the top panel), frequency of planar angles of N-methylpyridin-4(1H)one ring in fluridone conformations (the bottom left panel), and planar angle distribution of N-methylpyridin-4(1H)one ring in fluridone conformations in fluridone-graphene oxide complex (the bottom right panel). (D) Time evolution of trifluoromethylbenzene ring planar angles (the top panel), frequency of planar angles of trifluoromethylbenzene ring in fluridone conformations (the bottom left panel), and planar angle distribution of trifluoromethylbenzene ring in fluridone conformations in fluridone-graphene oxide complex (the bottom right panel).

Fig. 9. Graphics of the torsional conformation of each rotatable bond of carbaryl (A) and fluridone (B) on GO surface during MD simulations. The schematic 2D diagram of pesticides was shown with color-coded rotatable bonds. The dial plots of the angle of each bond after simulations were displayed on the left, and the bar charts of the torsional probability as a function of angle are shown on the right.

Table 3 Composition of binding energies between pesticides and graphene oxide.

Contaminants	Total energy (kcal/mol)	Nonbond energy (kcal/mol)	Van der Waals (kcal/mol)	Electrostatic energy (kcal/mol)
Carbaryl	-25.1087	-25.1087	-17.3917	-7.7170
Catechol	-15.5502	-15.5502	-11.4334	-4.1168
Fluridone	-28.9206	-28.9206	-21.5853	-7.3352

Fig. 10. The composition of binding energies between pesticides and graphene oxide.

Fig. 11. Plot of HOMO and LUMO orbitals as well as electrostatic potential (ESP) mapped on the surface of the molecular density for pesticides. (A-C) carbaryl-graphene oxide complex; (D-F) Catechol-graphene oxide complex; (G-I) Fluridone-graphene oxide complex. Increasing negative potentials were coded from blue color over green to red color. Calculations were carried out with Gaussian09 using 6-31G** basis set and M06-2X hybrid potential.

References 35

[1]	M.W. Aktar, D. Sengupta, A. Chowdhury, Interdiscip. Toxicol. 2 (2009) 1-12. DOI URL PMID
[2]	B.S. Choudri, Y. Charabi, Water Environ. Res. 91 (2019) 1342-1349. DOI URL PMID
[3]	B.S. Choudri, Y. Charabi, M. Ahmed, Water Environ. Res. 90 (2018) 1663-1678. DOI URL PMID
[4]	W.W. Gong, M.Y. Jiang, P. Han, G. Liang, T.T. Zhang, G.N. Liu, Environ. Pollut. 254 (2019), 112927. DOI URL PMID
[5]	M. Bilal, H.M.N. Iqbal, D. Barcel??, Sci. Total Environ. 689 (2019) 160-177. DOI URL PMID
[6]	M. Bilal, M. Adeel, T. Rasheed, Y. Zhao, H.M.N. Iqbal, Environ. Int. 124 (2019) 336-353. DOI URL PMID
[7]	M. Bilal, T. Rasheed, F. Nabeel, H.M.N. Iqbal, Y. Zhao, J. Environ, Manage. 234 (2019) 253-264. DOI URL
[8]	J.A. Leiva, P.C. Wilson, J.P. Albano, P. Nkedi-Kaza, G.A. O’Connor, ACS Omega 4 (2019) 17782-17790. DOI URL PMID
[9]	A. Gulkowska, I.J. Buerge, T. Poiger, R. Kasteel, Pest Manage. Sci. 72 (2016) 2218-2230. DOI URL
[10]	F. Abdollah, S.M. Borghei, E. Moniri, S. Kimiagar, H.A. Panahi, Water Sci. Technol. 80 (2019) 864-873. DOI URL PMID
[11]	G.H. Gao, Y.Y. Xing, T.T. Liu, J. Wang, X.H. Hou, Microchem. J. 146 (2019) 126-133. DOI URL
[12]	Y.D. Wang, M.T. Jia, X. Wu, T. Wang, J. Wang, X.H. Hou, Microchem. J. 146 (2019) 214-219. DOI URL
[13]	Z.K. Wang, J.Y. Zhang, B.C. Hu, J. Yu, J. Wang, X.J. Guo, J. Taiwan Inst, Chem. Eng. 95 (2019) 635-642. DOI URL
[14]	Q. Wang, W. Ma, Q. Tong, G. Du, J. Wang, M. Zhang, H. Jiang, H. Yang, Y. Liu, M. Cheng, Sci. Rep. 7 (2017) 7209. DOI URL PMID
[15]	W.X. Ma, Q.L. Tong, J. Wang, H.L. Yang, M. Zhang, H.L. Jiang, Q.H. Wang, Y.X. Liu, M.S. Cheng, RSC Adv. 7 (2017) 6720-6723. DOI URL
[16]	Y. Tong, L. Shao, X. Li, J. Lu, H. Sun, S. Xiang, Z. Zhang, Y. Wu, X. Wu, J. Agric, Food Chem. 66 (2018) 2616-2622. DOI URL
[17]	H.R. Nodeh, M.A. Kamboh, W.A.W. Ibrahim, B.H. Jume, H. Sereshti, M. M. Sanagi, Environ. Sci.: Processes Impacts 21 (2019) 714-726.
[18]	A. Markus, A.O. Gbadamosi, A.S. Yusuff, A. Agi, J. Oseh, Environ. Sci. Pollut. R 25 (2018) 35130-35142. DOI URL
[19]	P. Klongklaew, T. Naksena, P. Kanatharana, O. Bunkoed, Anal. Bioanal. Chem. 410 (2018) 7185-7193. DOI URL PMID
[20]	X. Hou, S. Tang, X. Guo, L. Wang, X. Liu, X. Lu, Y. Guo, J. Chromatogr, A 1571 (2018) 65-75. DOI URL
[21]	P. Wang, M. Luo, D.H. Liu, J. Zhan, X.K. Liu, F. Wang, Z.Q. Zhou, P. Wang, J. Chromatogr. A 1535 (2018) 9-16. DOI URL PMID
[22]	G. Liu, L. Li, D. Xu, X. Huang, X. Xu, S. Zheng, Y. Zhang, H. Lin, Carbohydr. Polym. 175 (2017) 584-591. URL PMID
[23]	K. Shrivas, A. Ghosale, N. Nirmalkar, A. Srivastava, S.K. Singh, S.S. Shinde, Environ. Sci. Pollut. R 24 (2017) 24980-24988. DOI URL
[24]	P.E. Czabotar, G. Lessene, A. Strasser, J.M. Adams, Nat. Rev. Mol. Cell Biol. 15 (2014) 49-63. DOI URL PMID
[25]	J. Gasteiger, M. Marsili, Tetrahedr. Lett. 19 (1978) 3181-3184. DOI URL
[26]	G. Robert, C. Gastaldi, A. Puissant, A. Hamouda, A. Jacquel, M. Dufies, N. Belhacene, P. Colosetti, J.C. Reed, P. Auberger, F. Luciano, Autophagy 8 (2012) 637-649. DOI URL
[27]	R.A. Friesner, J.L. Banks, R.B. Murphy, T.A. Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky, E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P. Francis, P.S. Shenkin, J. Med. Chem. 47 (2004) 1739-1749. DOI URL PMID
[28]	T.A. Halgren, R.B. Murphy, R.A. Friesner, H.S. Beard, L.L. Frye, W.T. Pollard, J.L. Banks, J. Med. Chem. 47 (2004) 1750-1759. DOI URL PMID
[29]	J.E. Jones, Proc. R. Soc. London, Ser. A 106 (1924) 463-477.
[30]	K.J. Bowers, E. Chow, H. Xu, R.O. Dror, M.P. Eastwood, B.A. Gregersen, J.L. Klepeis, I. Kolossvary, M.A. Moraes, F.D. Sacerdoti, Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters, Conference on High Performance Computing (Supercomputing) (2006) 84.
[31]	H. Sun, Z. Jin, C. Yang, R.L. Akkermans, S.H. Robertson, N.A. Spenley, S. Miller, S.M. Todd, J. Mol, Model. 22 (2016) 47.
[32]	Z.K. Wang, Z.S. Gao, S.S. Feng, J. Wang, X.J. Guo, Ind. Eng. Chem. Res. 58 (2019) 8072-8079. DOI URL
[33]	Y. Shudo, M.R. Karim, R. Ohtani, M. Nakamura, S. Hayami, ChemElectroChem 5 (2018) 238-241. DOI URL
[34]	H. Yan, H. Wu, K. Li, Y. Wang, X. Tao, H. Yang, A. Li, R. Cheng, ACS Appl. Mater. Interf. 7 (2015) 6690-6697. DOI URL
[35]	S. Gadipelli, Z.X. Guo, Prog. Mater. Sci. 69 (2015) 1-60. DOI URL