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

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.

Colorimetric Nanozyme Sensor Array Based on Metal Nanoparticle-Decorated CNTs for Quantification of Pesticides in Real Water and Soil Samples

“…Although frequently employed conventional “lock-and-key” analysis techniques possess excellent sensitivity, they require specific receptors that have a significant affinity to recognize a particular pesticide, leading to costly and lengthy procedures due to the need for numerous antibodies for multipesticide assays. , Additionally, specialized equipment was required for numerous chromatography–mass spectrometry methods as well as flow injection analysis. , Compared with the above conventional methods, array-based pattern recognition has shown great interest in detection of multiple analytes in the sensing field, which places more emphasis on group discrimination than single analyte detection. − Unlike the lock-and-key sensing mode that relies on individual receptors, this strategy utilizes artificial arrays of cross-reactive sensor elements to generate discrete patterns specific to each analyte. The examination of the patterns acquired via the use of multivariate algorithms in machine-learning approaches unveils the specific identification and concentration of the substance being analyzed, enabling the concurrent detection of several substances. − Array-based sensing has been extensively utilized for quantification and the analysis of several toxic substances such as heavy metal ions, thiols, bacteria, biogenic amines, toxic gases, etc.…”