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Mise en oeuvre de biocapteurs en vue de la détection de pesticides dans l'eau par diffusion Raman exaltée

Abstract : Surface-enhanced Raman scattering (SERS) was used to develop a biosensor for the detection of pesticides through the monitoring of the enzymatic activity of acetylcholinesterase (ACHE). Gold nanoparticles (AuNPs) were used as an active SERS substrate. The enhanced Raman signal of the analyte is optimized by testing several types of nanoparticles. Raman SERS allowed the direct detection of Paraoxon (PO) and carbaryl (CA) pesticides and the possibility of follow-up of the activity of the ACHE. In the absence of inhibitors, the acetylcholine (ATC) is transformed into acetic acid and choline by the enzyme ACHE. The measurement of ACHE activity is performed through the monitoring of ATC concentrations because its transformation is inhibited in the presence of pesticides. Results showed a linear correlation between the concentration of pesticides and the SERS signal of the untransformed ATC. The method was optimized for the quantification of paraoxon and carbaryl with a limit of quantification much lower than the one obtained with a direct detection. Their identification was also possible using chemometrics. This biosensors, based on the ACHE activities, was applied to the evaluation of emergent pollutants: additives of commercial polymers. Our results suggested that most of the tested polymers contained molecules that act as inhibitors of the ACHE. Finally, we propose another very simple approach to measure the ACHE activity using dynamic light scattering measurements. We found that the physicochemical parameters (aggregation) of AuNPs were strongly influenced by the enzymatic activity of ACHE when in contact with specified molecules, allowing to detect the presence of PO.
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Submitted on : Thursday, May 17, 2018 - 11:42:05 AM
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  • HAL Id : tel-01794107, version 1


Amal El Alami. Mise en oeuvre de biocapteurs en vue de la détection de pesticides dans l'eau par diffusion Raman exaltée. Physique [physics]. Université du Maine, 2017. Français. ⟨NNT : 2017LEMA1010⟩. ⟨tel-01794107⟩



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