材料科学
碳纳米纤维
聚丙烯腈
化学工程
纳米孔
纳米复合材料
表面改性
静电纺丝
循环伏安法
生物传感器
介电谱
碳纳米管
纳米技术
电化学
化学
电极
复合材料
聚合物
物理化学
工程类
作者
Kunal Mondal,Md. Azahar Ali,Chandan Singh,Gajjala Sumana,Bansi D. Malhotra,Ashutosh Sharma
标识
DOI:10.1016/j.snb.2017.02.050
摘要
Electrospinning was employed to synthesize a Ag nanoparticle (NP)-impregnated partially aligned and free-standing carbon nanofibers (CNFs) mat using a polyacrylonitrile (PAN) and silver nitrate blend followed by carbonization. Pyrolyzation of the PAN/AgNO3 blend produced the nanoporous CNFs, and the Ag NPs were grown within the CNFs via thermal decomposition of AgNO3. The fiber diameters of the synthesized CNFs ranged from 130 to 190 nm, and the size of the impregnated Ag NPs was ∼30 nm. The presence of the Ag NPs enhanced the electrical conductivity and promoted graphitization of the CNFs via the templating effect of the Ag NPs. These synthesized CNFs and AgCNFs nanocomposite were electrophoretically deposited onto indium tin oxide electrodes for detection of triglyceride molecules. Oxygen plasma treatment of the CNFs and AgCNFs surfaces resulted in enhanced loading of lipase and glycerol dehydrogenase bienzymes. The AgCNFs nanocomposite exhibited faster electron transfer than the CNFs, as corroborated by electrochemical impedance spectroscopy and cyclic voltammetry studies. Covalent functionalization via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide/N-hydroxysuccinimide coupling chemistry on a nanoporous CNFs surface led to higher stability of the fabricated biosensor toward triglyceride detection. The sensitivity was four-fold higher for the AgCNFs (1.232 μA mg/dL−1 cm−2) bioelectrode compared with the CNFs (0.33 μA mg/dL−1 cm−2) over a wide detection range (25–500 mg/dL). These biosensors exhibited excellent selectivity, good reproducibility, and faster response (10 s). Thus, enhanced graphitization and electrical conductivity of nanoporous CNFs via incorporation of Ag NPs yields a promising platform for the detection of triglyceride (TG) biomolecules.
科研通智能强力驱动
Strongly Powered by AbleSci AI