Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightThis study shows that PAN fibers containing PTh as anionic sensors can be used as new recognition models.
📌 3 Maddede Bu Makale
Abstract
Electrospun fibers are widely used in various applications such as tissue engineering, wound healing, drug delivery, materials science, chemical industry, energy storage, and sensor thanks to their combination of unique properties such as large surface area, high mechanical stability, high porosity, and great electrical conductivity. In addition, conducting polymers (CPs) used in fiber structures offer an extraordinary range of materials due to their diverse properties such as electrical and optical properties, the possibility of both chemical and electrochemical synthesis, and ease of processing. Among CPs, polythiophene (PTh) is highly important due to its unique redox electrical behavior, ease of synthesis, and application in many fields. In this study, 10 wt% polyacrylonitrile (PAN) fibers (P1), 10 wt% PAN/1 wt% PTh fibers (P2), and 10 wt% PAN/3 wt% PTh fibers (P3) were produced using an electrospinning technique. The structures, the morphologies and the electroactivities of the electrospun fibers were characterized by Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), Thermogravimetric analysis (TGA), and Cyclic voltammetry (CV). FTIR, SEM-EDX and TGA results supported the presence of PTh in PAN fibers. The electrochemical behaviors of indium-tin-oxide (ITO) glasses coated with the P1, P2, and P3 fibers in phosphate buffer solution (PBS) at various concentrations were assessed by CV. These electrospun fibers containing PTh were used for phosphate anion sensing. For all fiber samples, the oxidation potential increased with a decreasing concentration of phosphate buffer solution. The obtained results indicated that the thermal stability and electrical conductivity of the fibers were affected by PTh. This study shows that PAN fibers containing PTh as anionic sensors can be used as new recognition models.