FePO4 based single chamber air-cathode microbial fuel cell for online monitoring levofloxacin

FePO4 based single chamber air-cathode microbial fuel cell for online monitoring levofloxacin
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FePO4基单室空气阴极微生物燃料电池在线监测左氧氟沙星

DOI:
10.1016/j.bios.2016.12.021
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发表时间:
2017
影响因子:
12.6
通讯作者:
Liu Shaomin
Liu Shaomin
中科院分区:
工程技术1区
文献类型:
--
作者:
Zeng Libin;Li Xinyong;Shi Yueran;Qi Yefei;Huang Daqiong;Tade Moses;Wang Shaobin;Liu Shaomin

文献摘要

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基于单室微生物燃料电池(SC-MFC),以FePO 4纳米颗粒(NPs)代替传统的Pt/C作为阴极催化剂,构建了一种简单、灵敏的左氧氟沙星(LEV)生物电化学传感器。在此组装的传感器装置中,FePO 4 NPs显著促进了阴极上的氧的电氧化,这有助于加速SC-MFC的电压输出,可以为LEV检测提供有力的保证。采用扫描电子显微镜(SEM)、X射线衍射(XRD)、傅里叶变换红外光谱(FTIR)和X射线光电子能谱(XPS)对FePO 4纳米粒子进行了表征。在优化的COD条件下(3 mM),LEV的浓度范围为0.1-1000 µg/L,线性范围为0.1-100 µg/L。在此浓度范围内,LEV对产电细菌阳极的作用时间短于10 min,然后恢复正常。该系统具有长期稳定性,连续运行14个月保持稳定的发电量。此外,还利用密度泛函理论(DFT)对检测机理进行了量子化学计算。
A bio-electrochemical strategy was developed for constructing a simple and sensitive levofloxacin (LEV) sensor based on a single chamber microbial fuel cell (SC-MFC) using FePO4nanoparticles (NPs) as the cathode catalyst instead of traditional Pt/C. In this assembled sensor device, FePO4NPs dramatically promoted the electrooxidation of oxygen on the cathode, which helps to accelerate the voltage output from SC-MFC and can provide a powerful guarantee for LEV detection. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) and X-ray photoelectron spectroscopy (XPS) were used to fully characterize the FePO4NPs. Under the optimized COD condition (3 mM), the LEV with a concentration range of 0.1–1000 µg/L could be detected successfully, and exhibited the excellent linear interval in the concentration range of 0.1–100 µg/L. During this range of concentrations of LEV, a temporary effect on the anode of exoelectrogenic bacterial in less than 10 min could occur, and then came back to the normal. It exhibited a long-term stability, maintaining the stable electricity production for 14 months of continuous running. Besides, the detection mechanism was investigated by quantum chemical calculation using density functional theory (DFT).