化学
检出限
碳纳米纤维
静电纺丝
电化学
生物传感器
碳化
电极
纳米纤维
纳米技术
核化学
色谱法
催化作用
有机化学
材料科学
生物化学
吸附
物理化学
聚合物
作者
Yukun Xing,Chengkai Lv,Yue Fu,Lan Luo,Jixiang Liu,Xiaoyu Xie,Fangfang Chen
出处
期刊:Talanta
[Elsevier]
日期:2024-01-20
卷期号:271: 125674-125674
被引量:10
标识
DOI:10.1016/j.talanta.2024.125674
摘要
Abnormal levels of dopamine (DA) and uric acid (UA) in the human body are valuable indicators for monitoring human health, as they are associated with certain diseases. Therefore, it is crucial to develop sensitive and simultaneous analytical techniques for DA and UA in diagnosing the related diseases. Herein, the Co- and Mo- doped carbon nanofibers (Co, Mo@CNFs) electrochemical biosensor was developed successfully for the sensitive and accurate simultaneous detection of DA and UA. A straightforward electrospinning technique followed by a carbonization process was employed for the synthesis of Co, Mo@CNFs, and the encapsulation of Co and Mo within CNFs served to not only prevent nanoparticle agglomeration, thus providing more active sites, but also to facilitate rapid electron transfer. By incorporating Co and Mo into CNFs, the electrocatalytic activity of the modified electrode was greatly improved due to the beneficial conductivity and synergistic effects of transition metals. This enhancement effectively addressed issues such as the overlapping anodic peaks that occur when DA and UA are oxidized concurrently. Due to the mentioned synergistic contributions, the modified Co, Mo@CNFs electrode (Co, Mo@CNFs/GCE) achieved remarkable sensitivity for the simultaneous detection of DA and UA, while also exhibiting strong anti-interference ability. The detection limits for DA and UA were 2.35 nmol L−1 and 0.16 μmol L−1, respectively. We applied the developed Co, Mo@CNFs/GCE electrochemical biosensor to detect DA and UA in 50-fold diluted serum and urine samples. The results affirm the biosensor's reliability and precision. Moreover, the developed Co, Mo@CNFs/GCE biosensor demonstrated excellent performance in simultaneously detecting DA and UA, providing an efficient and dependable detection approach for clinical diagnosis and bioanalysis.
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