journal article·2025-11-14Open access

Review of Magnetic Nanomaterials for the Electrochemical Sensing of Antibiotics

Josip Radić, Lidija Fras Zemljič, Sara Perša, Olivija Plohl

ACS Applied Nano Materials· Volume 8· Issue 45

Abstract

Antimicrobial resistance (AMR) is increasing worldwide. This is due to the widespread and often uncontrolled release of antibiotics into surface water, drinking water, and the food chain. The traces of antibiotics (ng/L to μg/L) bioaccumulate, disrupt ecosystems, and accelerate AMR, yet regulatory monitoring remains inadequate. Sensitive analytical methods for the detection and quantification of antibiotics at trace levels in complex matrices are therefore essential. Conventional techniques, i.e., liquid or gas chromatography, mass spectrometry, and capillary electrophoresis, offer high accuracy but are associated with costly instrumentation, lengthy workflows, and extensive sample preparation. Electrochemical sensors based on advanced nanomaterials, particularly magnetic nanoparticles (MNPs), have attracted considerable interest due to their advantages in sensitivity and selectivity, wide linear dynamic ranges, extremely low limits of detection (LOD) and quantification (LOQ), low instrument cost, and rapid response. This review provides a critical overview of recent advances in MNP-based electrochemical platforms for antibiotic detection. The focus is on wet-chemical synthesis routes, modification approaches, and strategies for integrating magnetic nanocomposites into electrodes. Synergistic improvements through hybrid architectures are emphasized, combining MNPs with carbon nanomaterials, biopolymers, metal–organic frameworks, and molecularly imprinted polymers. Trends over the last five years have shown that applications in environmental monitoring, food safety, and drinking water are increasing. We summarize the key performance metrics, outline the current technical bottlenecks, such as long-term stability and large-scale manufacturing, and provide an outlook for the future. Taken together, these developments position MNP-based electrochemical sensors as versatile, highly effective tools for curbing antibiotic pollution and slowing the advancement of AMR.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Biosensors and Analytical Detection · Advanced biosensing and bioanalysis techniques · Analytical chemistry methods development

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

Affiliations

Faculty of Chemistry and Technology, Department of Analytical and Environmental Chemistry, University of Split, Ruđera Boškovića 35, 21000 Split, Croatia

Faculty of Mechanical Engineering, Laboratory for Characterization and Processing of Polymers, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia

Funding

The Slovenian Research and Innovation Agency

References (100+)

Show source-provided references
1. EEB - The European Environmental Bureau. The problem of pharmaceutical pollution . https://eeb.org/the-problem-of-pharmaceutical-pollution/.
2. Pharmaceuticals in the environment: Commission defines actions to address risks and challenges ; European Commission, 2022. https://europa.eu/rapid/press-release_IP-19-1597_en.htm (accessed Januaray 7, 2022).
3. European Union Strategic Approach to Pharmaceuticals in the Environment. Communication from the Commissionto the European Parliament, the Council and the European Economic and SocialCommittee 2019 , 2019.
4. A pharmaceutical strategy for Europe ; European Commission, 2025. https://health.ec.europa.eu/medicinal-products/pharmaceutical-strategy-europe_en (accessed October 11, 2025).
5. Circular economy action plan ; European Commission, 2025. https://environment.ec.europa.eu/strategy/circular-economy-action-plan_en (accessed July 10, 2025).
6. Proposal for a Decision of the European Parliamentand of the Council on a General Union Environment Action Programme to 2030 . https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52020PC0652&print=true1/38.
7. Fetting C. · 2020
8. Antimicrobial resistance from environmental pollution among biggest emerging health threats, says UN Environment ; UN Environment Programme, 2025. https://www.unep.org/news-and-stories/press-release/antimicrobial-resistance-environmental-pollution-among-biggest (accessed July 10, 2025).
9. 10.3390/microorganisms706018010.3390/microorganisms7060180 ↗
10. 10.1016/j.jafr.2025.10181510.1016/j.jafr.2025.101815 ↗
11. 10.14202/vetworld.2022.662-67110.14202/vetworld.2022.662-671 ↗
12. 10.1016/S0140-6736(21)02724-010.1016/S0140-6736(21)02724-0 ↗
13. 10.3390/nano706011910.3390/nano7060119 ↗
14. World Health Organization. Antimicrobial resistance . https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance.
15. A European One Health Action Plan against Antimicrobial Resistance (AMR) Contents ; European Commission, 2025. http://www.who.int/entity/drugresistance/documents/surveillancereport/en/index.html (accessed July 10, 2025).
16. 10.1016/j.Chemosphere.2006.03.02610.1016/j.Chemosphere.2006.03.026 ↗
17. 10.1021/acs.jafc.9b0133410.1021/acs.jafc.9b01334 ↗
18. 10.1016/j.bioelechem.2020.10768710.1016/j.bioelechem.2020.107687 ↗
19. 10.1016/j.scitotenv.2025.18054310.1016/j.scitotenv.2025.180543 ↗
20. 10.1016/j.envres.2022.11285510.1016/j.envres.2022.112855 ↗
21. 10.1016/j.isci.2024.11078910.1016/j.isci.2024.110789 ↗
22. 10.1016/j.envpol.2025.12591210.1016/j.envpol.2025.125912 ↗
23. 10.1016/j.jhazmat.2019.12080710.1016/j.jhazmat.2019.120807 ↗
24. European Medicines Agency (EMA). Maximum residue limit assessment reports . https://www.ema.europa.eu/en/veterinary-regulatory-overview/research-development-veterinary-medicines/maximum-residue-limits-mrl/maximum-residue-limit-assessment-reports.
25. Food and Agriculture Organization of the United Nations. Maximum Residue Limits (MRLs)and Risk Management Recommendations (RMRs) for Residues of Veterinary Drugs in FoodsCXM 2–2024 . https://www.fao.org/fao-who-codexalimentarius/sh-proxy/hu/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex%252FStandards%252FCXM%2B2%252FMRL2e.pdf.
26. 10.1016/j.trac.2012.03.02010.1016/j.trac.2012.03.020 ↗
27. 10.4172/2153-2435.100038010.4172/2153-2435.1000380 ↗
28. 10.1016/j.jelechem.2017.01.00310.1016/j.jelechem.2017.01.003 ↗
29. 10.1016/j.ijantimicag.2015.06.01610.1016/j.ijantimicag.2015.06.016 ↗
30. 10.3390/chemosensors1009036310.3390/chemosensors10090363 ↗
31. 10.1021/acsomega.3c0806010.1021/acsomega.3c08060 ↗
32. Bard A. J. · 2001
33. 10.1016/j.Chemosphere.2022.13641610.1016/j.Chemosphere.2022.136416 ↗
34. Shivappa Adarakatti P. · 2018 · 15 · 5810.1039/9781788013895-00058 ↗
35. Policarpo Tonelli F. M. · 2024
36. 10.1021/acs.jafc.4c0942310.1021/acs.jafc.4c09423 ↗
37. Wang J. · 200610.1002/0471790303 ↗
38. 10.3762/bjnano.14.5210.3762/bjnano.14.52 ↗
39. 10.1016/j.molstruc.2025.14276210.1016/j.molstruc.2025.142762 ↗
40. 10.1016/j.talo.2025.10044810.1016/j.talo.2025.100448 ↗
41. 10.1016/j.aca.2019.02.05210.1016/j.aca.2019.02.052 ↗
42. 10.1016/j.teac.2019.e0006210.1016/j.teac.2019.e00062 ↗
43. 10.1016/j.Talanta.2015.06.01110.1016/j.Talanta.2015.06.011 ↗
44. 10.1016/j.trac.2016.02.01010.1016/j.trac.2016.02.010 ↗
45. 10.1016/j.trac.2018.08.02310.1016/j.trac.2018.08.023 ↗
46. 10.1016/j.jpba.2017.06.06210.1016/j.jpba.2017.06.062 ↗
47. 10.1016/j.trac.2018.11.04410.1016/j.trac.2018.11.044 ↗
48. 10.3390/app1309539110.3390/app13095391 ↗
49. 10.1016/j.teac.2017.02.00110.1016/j.teac.2017.02.001 ↗
50. 10.1016/j.bios.2020.11204610.1016/j.bios.2020.112046 ↗
51. 10.1016/j.cej.2021.13091310.1016/j.cej.2021.130913 ↗
52. 10.1016/j.tibtech.2019.01.00410.1016/j.tibtech.2019.01.004 ↗
53. 10.1016/j.trac.2018.10.01110.1016/j.trac.2018.10.011 ↗
54. 10.1016/j.trac.2017.10.00910.1016/j.trac.2017.10.009 ↗
55. 10.1016/j.snb.2019.04.07510.1016/j.snb.2019.04.075 ↗
56. 10.1016/j.trac.2017.03.00110.1016/j.trac.2017.03.001 ↗
57. 10.1016/j.trac.2014.06.01610.1016/j.trac.2014.06.016 ↗
58. 10.1039/D2RA07415J10.1039/D2RA07415J ↗
59. 10.1002/adma.20190438510.1002/adma.201904385 ↗
60. 10.1016/j.Talanta.2020.12207510.1016/j.Talanta.2020.122075 ↗
61. 10.1016/j.cej.2023.14281610.1016/j.cej.2023.142816 ↗
62. 10.1007/s11244-025-02180-210.1007/s11244-025-02180-2 ↗
63. 10.1016/j.foodchem.2023.13814510.1016/j.foodchem.2023.138145 ↗
64. 10.1039/D3SD00129F10.1039/D3SD00129F ↗
65. 10.1016/j.snb.2008.07.02010.1016/j.snb.2008.07.020 ↗
66. 10.3390/chemosensors907018710.3390/chemosensors9070187 ↗
67. 10.1039/D2AY00702A10.1039/D2AY00702A ↗
68. 10.1016/j.cej.2024.15480810.1016/j.cej.2024.154808 ↗
69. 10.1016/j.jiec.2024.03.01810.1016/j.jiec.2024.03.018 ↗
70. 10.1016/j.microc.2025.11537310.1016/j.microc.2025.115373 ↗
71. 10.3390/s2210368410.3390/s22103684 ↗
72. 10.1016/j.trac.2023.11738910.1016/j.trac.2023.117389 ↗
73. 10.1111/1541-4337.1326610.1111/1541-4337.13266 ↗
74. 10.3390/magnetochemistry504006410.3390/magnetochemistry5040064 ↗
75. 10.1016/j.aej.2024.10.02110.1016/j.aej.2024.10.021 ↗
76. 10.1016/j.snb.2023.13421510.1016/j.snb.2023.134215 ↗
77. 10.3389/fchem.2021.61560210.3389/fchem.2021.615602 ↗
78. 10.1016/j.foodchem.2022.13312010.1016/j.foodchem.2022.133120 ↗
79. 10.1016/j.envres.2022.11464310.1016/j.envres.2022.114643 ↗
80. 10.1016/j.apsusc.2023.15767210.1016/j.apsusc.2023.157672 ↗
81. 10.1016/j.foodchem.2024.14214110.1016/j.foodchem.2024.142141 ↗
82. 10.1021/acsomega.3c1039010.1021/acsomega.3c10390 ↗
83. 10.1016/j.microc.2025.11326510.1016/j.microc.2025.113265 ↗
84. 10.1016/j.jelechem.2020.11421610.1016/j.jelechem.2020.114216 ↗
85. 10.1016/j.microc.2024.11065410.1016/j.microc.2024.110654 ↗
86. 10.1039/D0MA00807A10.1039/D0MA00807A ↗
87. 10.3390/app1404162310.3390/app14041623 ↗
88. Cornell R. M. · 200310.1002/3527602097 ↗
89. 10.1039/C7EN01159H10.1039/C7EN01159H ↗
90. 10.1016/j.ccr.2021.21384610.1016/j.ccr.2021.213846 ↗
91. 10.3390/coatings1310177210.3390/coatings13101772 ↗
92. 10.1021/ar900002610.1021/ar9000026 ↗
93. 10.1118/1.483721610.1118/1.4837216 ↗
94. 10.1016/S0304-8853(99)00065-710.1016/S0304-8853(99)00065-7 ↗
95. 10.1021/acs.jpcc.9b0679010.1021/acs.jpcc.9b06790 ↗
96. 10.1038/s41467-019-10147-710.1038/s41467-019-10147-7 ↗
97. 10.1002/elan.20150053010.1002/elan.201500530 ↗
98. 10.1016/j.Talanta.2021.12210810.1016/j.Talanta.2021.122108 ↗
99. 10.1039/C4CS00087K10.1039/C4CS00087K ↗
100. 10.1021/acsmeasuresciau.5c0001410.1021/acsmeasuresciau.5c00014 ↗