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Recent advances in fast-scan cyclic voltammetry.
Analyst ( IF 3.6 ) Pub Date : 2020-01-10 , DOI: 10.1039/c9an01925a
Pumidech Puthongkham 1 , B Jill Venton
Affiliation  

Fast-scan cyclic voltammetry (FSCV) at carbon-fiber microelectrodes (CFMEs) is a versatile electrochemical technique to probe neurochemical dynamics in vivo. Progress in FSCV methodology continues to address analytical challenges arising from biological needs to measure low concentrations of neurotransmitters at specific sites. This review summarizes recent advances in FSCV method development in three areas: (1) waveform optimization, (2) electrode development, and (3) data analysis. First, FSCV waveform parameters such as holding potential, switching potential, and scan rate have been optimized to monitor new neurochemicals. The new waveform shapes introduce better selectivity toward specific molecules such as serotonin, histamine, hydrogen peroxide, octopamine, adenosine, guanosine, and neuropeptides. Second, CFMEs have been modified with nanomaterials such as carbon nanotubes or replaced with conducting polymers to enhance sensitivity, selectivity, and antifouling properties. Different geometries can be obtained by 3D-printing, manufacturing arrays, or fabricating carbon nanopipettes. Third, data analysis is important to sort through the thousands of CVs obtained. Recent developments in data analysis include preprocessing by digital filtering, principal components analysis for distinguishing analytes, and developing automated algorithms to detect peaks. Future challenges include multisite measurements, machine learning, and integration with other techniques. Advances in FSCV will accelerate research in neurochemistry to answer new biological questions about dynamics of signaling in the brain.

中文翻译:


快速扫描循环伏安法的最新进展。



碳纤维微电极 (CFME) 的快速扫描循环伏安法 (FSCV) 是一种用于探测体内神经化学动力学的多功能电化学技术。 FSCV 方法的进展继续解决因测量特定位点低浓度神经递质的生物学需求而产生的分析挑战。本综述总结了 FSCV 方法开发在三个领域的最新进展:(1) 波形优化、(2) 电极开发和 (3) 数据分析。首先,FSCV 波形参数(例如保持电位、开关电位和扫描速率)经过优化,可监测新的神经化学物质。新的波形形状对特定分子(如血清素、组胺、过氧化氢、章鱼胺、腺苷、鸟苷和神经肽)具有更好的选择性。其次,CFME已用碳纳米管等纳米材料进行改性或用导电聚合物代替,以增强灵敏度、选择性和防污性能。不同的几何形状可以通过 3D 打印、制造阵列或制造碳纳米吸管来获得。第三,数据分析对于整理获得的数千份简历非常重要。数据分析的最新发展包括通过数字过滤进行预处理、用于区分分析物的主成分分析以及开发自动算法来检测峰值。未来的挑战包括多站点测量、机器学习以及与其他技术的集成。 FSCV 的进展将加速神经化学研究,以回答有关大脑信号动力学的新生物学问题。
更新日期:2020-02-17
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