Synthesis of RGO/γ-Fe2O3 nanocomposite for the removal of heavy metals from aqueous solutions
-
Mai Duc Dung
Abstract
Reduced graphene oxide/maghemite (RGO/γ-Fe2O3) material was successfully synthesized by combining the modified Hummers method with co-precipitation (RGO 10 wt.%). γ-Fe2O3 nanoparticles with a particle size of ∼14.8 nm were distributed on the surface of RGO sheets. Results of Brunauer–Emmett–Teller analysis showed that RGO/γ-Fe2O3 had a mesoporous structure and a narrow capillary size distribution curve at about 13 nm. The specific surface area of the RGO/γ-Fe2O3 was 168 m2·g−1. The RGO/γ-Fe2O3 nanocomposite was used to adsorb arsenic As(V) and a mixture of heavy metals (As(V), Cr(VI), Pb(II), and Fe(III)) in water. The maximum adsorption efficiency of As(V) reached 98.9% after 45 min with an adsorption capacity of 5.93 mg·g−1, higher than the simultaneous adsorption of the four metal ions. Competitive adsorption decreased in the order As(V), Cr(VI), Pb(II), and Fe(III). Therefore, RGO/γ-Fe2O3 could be used as an effective adsorbent to remove heavy metals from aqueous solutions.
Acknowledgments
The authors thank Cuong N. D. of AIST for the useful discussions and Loan T. T. of ITIMS for the XRD analysis through Rietveld refinement method and this manuscript’s proofreading.
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: This research was funded by the Hanoi University of Science and Technology under grant number T2020–SAHEP–035.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Guo, X., Du, B., Wei, Q., Yang, J., Yan, L., Xu, W. J. Hazard Mater. 2014, 278, 211–220. https://doi.org/10.1016/j.jhazmat.2014.05.075.Suche in Google Scholar PubMed
2. Vu, H. C., Dwivedi, A. D., Le, T. T., Seo, S. H., Kim, E. J., Chang, Y. S. Chem. Eng. J. 2017, 307, 220–229. https://doi.org/10.1016/j.cej.2016.08.058.Suche in Google Scholar
3. Singh, R., Singh, S., Parihar, P., Singh, V. P., Prasad, S. M. Ecotoxicol. Environ. Saf. 2015, 112, 247–270. https://doi.org/10.1016/j.ecoenv.2014.10.009.Suche in Google Scholar PubMed
4. Li, W., Zhang, L., Peng, J., Li, N., Zhang, S., Guo, S. Ind. Crops Prod. 2008, 28, 294–302. https://doi.org/10.1016/j.indcrop.2008.03.007.Suche in Google Scholar
5. Zamani, H. A., Ganjali, M. R., Faridbod, F., Salavati-Niasari, M. Mater. Sci. Eng. C 2012, 32, 564–568. https://doi.org/10.1016/j.msec.2011.12.009.Suche in Google Scholar
6. Zong, P., Wang, S., Zhao, Y., Wang, H., Pan, H., He, C. Chem. Eng. J. 2013, 220, 45–52. https://doi.org/10.1016/j.cej.2013.01.038.Suche in Google Scholar
7. Maksoud, A., Elgarahy, A. M., Farrell, C., Al-Muhtaseb, A. H., Rooney, D. W., Osman, A. I. Coord. Chem. Rev. 2020, 403, 213096. https://doi.org/10.1016/j.ccr.2019.213096.Suche in Google Scholar
8. Ghasemabadi, S. M., Baghdadi, M., Safari, E., Ghazban, F. J. Environ. Chem. Eng. 2018, 6, 4840–4849. https://doi.org/10.1016/j.jece.2018.07.014.Suche in Google Scholar
9. Ubhi, M. K., Kaur, M., Singh, D., Greneche, J. M. Process. Appl. Ceram. 2017, 11, 247–257. https://doi.org/10.2298/PAC1704247K.Suche in Google Scholar
10. You, J., Zhao, Y., Wang, L., Bao, W., He, Y. J. Phys. Chem. Solids 2020, 142, 109441. https://doi.org/10.1016/j.jpcs.2020.109441.Suche in Google Scholar
11. Barbosa de Andrade, M., Sestito Guerra, A. C., Tonial Dos Santos, T. R., Cusioli, L. F., De Souza Antonio, R., Bergamasco, R. J. Environ. Chem. Eng. 2020, 8, 103903. https://doi.org/10.1016/j.jece.2020.103903.Suche in Google Scholar
12. Wang, Y., Wei, X., Qi, Y., Huang, H. Chemosphere 2021, 263, 127563. https://doi.org/10.1016/j.chemosphere.2020.127563.Suche in Google Scholar PubMed
13. Dung, M. D., Nga, T. T. V., Lan, N. T., Thanh, N. K. Anal. Sci. 2022, 38, 427–436. https://doi.org/10.1007/s44211-022-00064-z.Suche in Google Scholar PubMed
14. Hai, N. H., Phu, N. D., Luong, N. H., Chau, N., Chinh, H. D., Hoang, L. H., Leslie-Pelecky, D. L. J. Kor. Phys. Soc. 2008, 52, 1327–1331. https://doi.org/10.3938/jkps.52.1327.Suche in Google Scholar
15. Lan, N. T., Chi, D. T., Dinh, N. X., Hung, N. D., Lan, H., Tuan, P. A., Thang, L. H., Trung, N. N., Hoa, N. Q., Huy, T. Q., Quy, N. V., Duong, T. T., Phan, V. N., Le, A. T. J. Alloys Compd. 2014, 615, 843–848. https://doi.org/10.1016/j.jallcom.2014.07.042.Suche in Google Scholar
16. Wyckoff, R. W. G. Crystal Structures, 2nd ed.; Interscience Publishers: New York, vol. 1, 1963.Suche in Google Scholar
17. Pecharroman, C., Gonzalezcarreno, T., Iglesias, J. E. Phys. Chem. Miner. 1995, 22, 21–29. https://doi.org/10.1007/BF00202677.Suche in Google Scholar
18. Loan, T. T., Huy, D. K., Chung, H. M., Thanh, N. K., Hoan, T. D., Duong, N. P., Soontaranon, S., Klysubun, W. Mater. Today Commun. 2021, 26, 101733. https://doi.org/10.1016/j.mtcomm.2020.101733.Suche in Google Scholar
19. Chandrasekaran, S., Hur, S. H., Kim, E. J., Rajagopalan, B., Babu, K. F., Senthilkumar, V., Chung, J. S., Choi, W. M., Kim, Y. S. RSC Adv. 2015, 5, 29159–29166. https://doi.org/10.1039/C5RA02934A.Suche in Google Scholar
20. Aliahmad, M., Moghaddam, N. S. Mater. Sci. Pol. 2013, 31, 264–268. https://doi.org/10.2478/s13536-012-0100-6.Suche in Google Scholar
21. Liang, C., Liu, H., Zhou, J., Peng, X., Zhang, H. J. Chem. 2015, 2015, 791829. https://doi.org/10.1155/2015/791829.Suche in Google Scholar
22. Hung, P. V., Cuong, T. V., Hur, S. H., Oh, E., Kim, E. J., Shin, E. W., Chung, J. S. J. Mater. Chem. 2011, 21, 3371–3377. https://doi.org/10.1039/C0JM02790A.Suche in Google Scholar
23. Abdulhadi Alwahib, A. A., Kamil, Y. M., Abu Bakar, M. H., Muhammad Noor, A. S., Yaacob, M. H., Lim, H. N., Huang, N. M., Mahdi, M. A. IEEE Photonics J. 2018, 10, 4801310. https://doi.org/10.1109/JPHOT.2018.2877190.Suche in Google Scholar
24. Shi, H., Li, W., Zhong, L., Xu, C. Ind. Eng. Chem. Res. 2014, 53, 1108–1118. https://doi.org/10.1021/ie4027154.Suche in Google Scholar
25. Salviano, L. B., Da Silva Cardoso, T. M., Silva, G. C., Dantas, M. S. S., Ferreira, A. M. Mat. Res. 2018, 21, 20170764. https://doi.org/10.1590/1980-5373-mr-2017-0764.Suche in Google Scholar
26. Feng, Q., Chen, Z., Zhou, K., Sun, M., Ji, X., Zheng, H., Zhang, Y. ChemistrySelect 2021, 6, 8177–8181. https://doi.org/10.1002/slct.202101844.Suche in Google Scholar
27. Zubir, N. A., Yacou, C., Motuzas, J., Zhang, X., Diniz Da Costa, J. C. Sci. Rep. 2014, 4, 4594. https://doi.org/10.1038/srep04594.Suche in Google Scholar PubMed PubMed Central
28. Ashok Kumar, K. V., Chandana, L., Ghosal, P., Subrahmanyam, C. Mol. Catal. 2018, 451, 87–95. https://doi.org/10.1016/j.mcat.2017.11.014.Suche in Google Scholar
29. Chandra, V., Park, J., Chun, Y., Lee, J. W., Hwang, I. C., Kim, K. S. ACS Nano 2010, 4, 3979–3986. https://doi.org/10.1021/nn1008897.Suche in Google Scholar PubMed
30. Gomaa, H., Shenashen, M. A., Yamaguchi, H., Alamoudi, A. S., Abdelmottaleb, M., Cheira, M. F., Seaf El-Naser, T. A., El-Safty, S. A. J. Clean. Prod. 2018, 182, 910–925. https://doi.org/10.1016/j.jclepro.2018.02.063.Suche in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Original Papers
- Modeling the band gap of spinel nano-ferrite material using a genetic algorithm based support vector regression computational method
- Influence of surfactant concentration on structural properties and corrosion behaviour of electrodeposited Ni–SiO2 nanocomposite coatings
- Synthesis of RGO/γ-Fe2O3 nanocomposite for the removal of heavy metals from aqueous solutions
- Synthesis of nickel oxide nanoparticles as an agent for antibacterial and wastewater remediation applications by calcination
- Synthesis and efficient electrocatalytic performance of Bi2O3/Dy2O3 nanoflakes
- Finite element assisted self-consistent simulations to capture texture heterogeneity during hot compression
- Improving mechanical properties of additive manufactured AZ31 by mechanical rolling
- News
- DGM – Deutsche Gesellschaft für Materialkunde
Artikel in diesem Heft
- Frontmatter
- Original Papers
- Modeling the band gap of spinel nano-ferrite material using a genetic algorithm based support vector regression computational method
- Influence of surfactant concentration on structural properties and corrosion behaviour of electrodeposited Ni–SiO2 nanocomposite coatings
- Synthesis of RGO/γ-Fe2O3 nanocomposite for the removal of heavy metals from aqueous solutions
- Synthesis of nickel oxide nanoparticles as an agent for antibacterial and wastewater remediation applications by calcination
- Synthesis and efficient electrocatalytic performance of Bi2O3/Dy2O3 nanoflakes
- Finite element assisted self-consistent simulations to capture texture heterogeneity during hot compression
- Improving mechanical properties of additive manufactured AZ31 by mechanical rolling
- News
- DGM – Deutsche Gesellschaft für Materialkunde
