Trilacunary Keggin Type Polyoxometalate-Conducting Polymer Composites for Amperometric Glucose Detection
Source abstract
Abstract
A new amperometric glucose sensor has been fabricated based on a composite film prepared by electrochemical polymerization of carbazole derivative with having free amino group in presence of the Keggin type polyoxometalate (POM) anion, ( n Bu 4 N) 3 [PW 9 O 34 ( t BuSiOH) 3 ]. During the electropolymerization of 3-Amino-9-ethylcarbazole (AAC) on graphite electrode, simultaneously the POM has entrapped in the produced PAAC polymer film. POM/PAAC composite film was successfully synthesized and characterized with electrochemical techniques and surface morphology analyses. Metal/organic conducting polymeric composite has been composed of positively charged PAAC based conducting polymer and negatively charged POM structures. The amperometric response of the POM/PAAC-GO x modified electrode versus varying concentrations of glucose was studied at a potential value of −0.7 V (Ag/AgCl). It was determined that the POM/PAAC composite structure with the best sensor response has the lowest oxidation potential among the composite structures prepared at different ratios. Sensitivity of the POM/PAAC based sensor platform for actual glucose detections has been calculated as 66.66μA mM −1 cm −2 with a linear detection range from 0.1 to 10 mM and detection limit of 0.099 mM. The POM/PACC composite has a unique structure thanks to such advantages the multiple redox reaction, high reactivity and rapid electron transfer of POMs. Especially, as-prepared this composite displayed high electrocatalytic activity for the amperometric detection of glucose with rapid response time, good sensitivity and reproducibility, acceptable recovery and simple preparation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Polyoxometalates: Synthesis and Applications · Conducting polymers and applications · Advanced Nanomaterials in Catalysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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