journal article·2023-07-14

Fe3O4 Nanoparticle/Graphene Oxide Composites as Selective Probes and Self-Matrixes for Pesticide Detection by Electrochemistry and Laser Desorption/Ionization Mass Spectrometry

Jutiporn Yukird, Numpon Insin, Rungroj Chanajaree, Nadnudda Rodthongkum

ACS Applied Nano Materials· Volume 6· Issue 13
Publisher page

Abstract

An Fe 3 O 4 nanoparticle/graphene oxide (GO) magnetic nanocomposite is synthesized and first applied as a selective probe for simultaneous detection of carbofuran (CBF) and carbendazim (CBZ) by electrochemistry and LDI-MS with MS imaging in real fruit samples. The high binding energies of the Fe 3 O 4 nanoparticle/GO magnetic nanocomposite with target pesticides are verified by density functional theory. The Fe 3 O 4 /GO nanocomposite-modified electrode enhances the electrocatalytic property of the pesticides and provides a linear range of 1–250 μM with LODs of 0.06 and 0.18 μM for CBF and CBZ, respectively. For LDI-MS, after magnetic separation, CBF and CBZ bound to the magnetic nanocomposite are readily ionized by using Fe 3 O 4 /GO as a self-matrix, leading to high ion abundance with a linear range of 0.25–1000 μM and LODs of 0.25 μM for both pesticides. Furthermore, CBF and CBZ bound to Fe 3 O 4 /GO spiked in pear are visualized by imaging MS, illustrating the distribution of these pesticides in real pear peels. LOD values obtained from both detection techniques are lower than the maximum residue level (MRL) in the database of these pesticide residues, suggesting that this platform might be an alternative tool for the selective detection of pesticide residues in fruit samples.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Electrochemical sensors and biosensors · Electrochemical Analysis and Applications · Pesticide Residue Analysis and Safety

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Metallurgy and Materials Science Research Institute, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand

Department of Chemistry, Faculty of Science, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand

Center of Excellence in Responsive Wearable Materials, Chulalongkorn University, Soi Chula 12, Phayathai Road, Pathumwan, Bangkok, 10330, Thailand

Funding

Chulalongkorn University · NSRF

References (61)

Show source-provided references
1. 10.1080/10807039.2020.180877710.1080/10807039.2020.1808777 ↗
2. 10.1016/j.envsci.2011.12.00510.1016/j.envsci.2011.12.005 ↗
3. 10.1155/2020/818032010.1155/2020/8180320 ↗
4. 10.1016/j.foodchem.2021.12996410.1016/j.foodchem.2021.129964 ↗
5. 10.1080/19440049.2021.193019910.1080/19440049.2021.1930199 ↗
6. 10.1016/j.microc.2020.10497510.1016/j.microc.2020.104975 ↗
7. 10.1080/0265203070158186410.1080/02652030701581864 ↗
8. 10.1002/jssc.20140025610.1002/jssc.201400256 ↗
9. 10.1016/j.jmmm.2018.11.09010.1016/j.jmmm.2018.11.090 ↗
10. 10.1021/acsami.7b1003010.1021/acsami.7b10030 ↗
11. 10.1016/j.talanta.2015.04.01810.1016/j.talanta.2015.04.018 ↗
12. 10.1016/j.jcis.2018.03.00810.1016/j.jcis.2018.03.008 ↗
13. 10.1016/j.talanta.2021.12210810.1016/j.talanta.2021.122108 ↗
14. 10.1021/acsami.1c2349610.1021/acsami.1c23496 ↗
15. 10.1007/s00604-016-1765-z10.1007/s00604-016-1765-z ↗
16. 10.1016/j.aca.2018.09.00110.1016/j.aca.2018.09.001 ↗
17. 10.1016/j.microc.2022.10739810.1016/j.microc.2022.107398 ↗
18. 10.1039/d0ra01068e10.1039/d0ra01068e ↗
19. 10.1007/s12274-014-0622-910.1007/s12274-014-0622-9 ↗
20. 10.1016/j.jmst.2020.02.03310.1016/j.jmst.2020.02.033 ↗
21. 10.1007/s10311-021-01262-310.1007/s10311-021-01262-3 ↗
22. 10.1016/j.foodchem.2020.12716510.1016/j.foodchem.2020.127165 ↗
23. 10.1016/j.synthmet.2018.09.01210.1016/j.synthmet.2018.09.012 ↗
24. 10.1016/j.trac.2021.11630310.1016/j.trac.2021.116303 ↗
25. 10.1021/acsami.5b1085310.1021/acsami.5b10853 ↗
26. 10.1039/c6ra04163a10.1039/c6ra04163a ↗
27. 10.1016/j.jelechem.2017.04.06110.1016/j.jelechem.2017.04.061 ↗
28. 10.1039/c7ra05982e10.1039/c7ra05982e ↗
29. 10.1016/j.aca.2014.12.02210.1016/j.aca.2014.12.022 ↗
30. 10.1016/j.msec.2014.08.06410.1016/j.msec.2014.08.064 ↗
31. 10.1016/j.aca.2015.05.02210.1016/j.aca.2015.05.022 ↗
32. 10.1038/srep0459410.1038/srep04594 ↗
33. 10.1007/s11434-012-4986-510.1007/s11434-012-4986-5 ↗
34. 10.1016/j.talanta.2017.07.05010.1016/j.talanta.2017.07.050 ↗
35. 10.1016/j.chroma.2011.09.02710.1016/j.chroma.2011.09.027 ↗
36. 10.1039/c2cc17696c10.1039/c2cc17696c ↗
37. 10.3390/mi1301011510.3390/mi13010115 ↗
38. 10.1080/09542299.2016.119892810.1080/09542299.2016.1198928 ↗
39. 10.1016/j.talanta.2020.12133410.1016/j.talanta.2020.121334 ↗
40. 10.1103/physrevlett.77.386510.1103/physrevlett.77.3865 ↗
41. 10.1103/physrevlett.100.13640610.1103/physrevlett.100.136406 ↗
42. 10.1063/1.44880010.1063/1.448800 ↗
43. 10.1063/1.44879910.1063/1.448799 ↗
44. 10.1039/b508541a10.1039/b508541a ↗
45. 10.1021/ja961802q10.1021/ja961802q ↗
46. 10.1039/b515623h10.1039/b515623h ↗
47. 10.1063/1.319172810.1063/1.3191728 ↗
48. 10.1002/wcms.8110.1002/wcms.81 ↗
49. Neese, F. Software update: the ORCA program system, version 4.0. 2018, vol 8 (1), p e1327.10.1002/wcms.1327 ↗
50. 10.1016/j.jmmm.2020.16751510.1016/j.jmmm.2020.167515 ↗
51. 10.1007/s11356-022-18725-x10.1007/s11356-022-18725-x ↗
52. 10.1007/s11581-020-03532-610.1007/s11581-020-03532-6 ↗
53. 10.1063/1.492812410.1063/1.4928124 ↗
54. 10.1021/jp973182110.1021/jp9731821 ↗
55. 10.1080/00032719.2012.67778210.1080/00032719.2012.677782 ↗
56. 10.1021/jasms.1c0013210.1021/jasms.1c00132 ↗
57. 10.1016/j.aca.2018.07.04910.1016/j.aca.2018.07.049 ↗
58. 10.1016/j.snb.2015.12.09110.1016/j.snb.2015.12.091 ↗
59. 10.1016/j.talanta.2016.12.09210.1016/j.talanta.2016.12.092 ↗
60. 10.3390/molecules2616505010.3390/molecules26165050 ↗
61. 10.1016/j.chroma.2008.10.12110.1016/j.chroma.2008.10.121 ↗