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    On-site detection of substance abuse is an important approach in the preventive and intervention protocols implementations. It is known that the traditional methods are heavy, time-consuming, and need a high level of logistical requirements. As such, biosensors represent great potential to simplify and improve substance abuse detection. In this study, we have designed a functionalized screen-printed electrode (SPE) electrochemical biosensor with cobalt oxide nanoparticles and single-chain antibody fragments (scFvs) for cocaine detection. Different electrochemical techniques such as differential pulse voltammetry, cyclic voltammetry, and electrochemical impedance spectrometry were used to examine the functionality of the designed biosensor. Furthermore, SEM observations were performed to observe the surface changes after functionalization. The results showed that the linearity ranged between 5.0 and 250 ng/mL and a detection limit of 3.6 ng/mL (n = 6). These results were compared to results obtained from Q-TOF/MS where four different matrices (serum, sweat, urine, and saliva) were spiked with 100 ng/mL cocaine and were analyzed by both methods (Biosensor and Q-TOF/MS). Results showed a higher performance of the biosensor compared to traditional methods. In addition, the selectivity of the biosensor was shown in the presence of different interferents where the designed platform showed a specific response to only cocaine. In conclusion, the designed biosensor proposes great potential for portable and on-site substance abuse detection in addition to boasting the capability of reuse of the SPE and thus, reducing the costs related to such applications. Copyright © 2020 Elsevier B.V. All rights reserved.


    Serdar Sanli, Hichem Moulahoum, Ozge Ugurlu, Faezeh Ghorbanizamani, Zinar Pinar Gumus, Serap Evran, Hakan Coskunol, Suna Timur. Screen printed electrode-based biosensor functionalized with magnetic cobalt/single-chain antibody fragments for cocaine biosensing in different matrices. Talanta. 2020 Sep 01;217:121111

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    PMID: 32498832

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