Fast-scan cyclic voltammetry (FSCV) is used with carbon-fiber microelectrodes for the real-time detection of neurotransmitters on the subsecond time scale. With FSCV, the potential is ramped up from a holding potential to a switching potential and back, usually at a 400 V s-1 scan rate and a frequency of 10 Hz. The plot of current vs. applied potential, the cyclic voltammogram (CV), has a very different shape for FSCV than for traditional cyclic voltammetry collected at scan rates which are 1000-fold slower. Here, we explore the theory of FSCV, with a focus on dopamine detection. First, we examine the shape of the CVs. Background currents, which are 100-fold higher than faradaic currents, are subtracted out. Peak separation is primarily due to slow electron transfer kinetics, while the symmetrical peak shape is due to exhaustive electrolysis of all the adsorbed neurotransmitters. Second, we explain the origins of the dopamine waveform, and the factors that limit the holding potential (oxygen reduction), switching potential (water oxidation), scan rate (electrode instability), and repetition rate (adsorption). Third, we discuss data analysis, from data visualization with color plots, to the automated algorithms like principal components regression that distinguish dopamine from pH changes. Finally, newer applications are discussed, including optimization of waveforms for analyte selectivity, carbon nanomaterial electrodes that trap dopamine, and basal level measurements that facilitate neurotransmitter measurements on a longer time scale. FSCV theory is complex, but understanding it enables better development of new techniques to monitor neurotransmitters in vivo.

中文翻译：

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用于多巴胺检测的快速扫描循环伏安法的基础知识。

快速扫描循环伏安法（FSCV）与碳纤维微电极一起使用，可在亚秒级的时间范围内实时检测神经递质。使用FSCV时，电势通常从400 V s-1扫描速率和10 Hz的频率从保持电势上升到切换电势，然后又返回。电流与施加电势之间的关系曲线（循环伏安图（CV））对于FSCV的形状与以扫描速度慢1000倍的传统循环伏安法的形状截然不同。在这里，我们探讨FSCV的理论，重点是多巴胺检测。首先，我们检查CV的形状。减去了比法拉第电流高100倍的背景电流。峰分离主要是由于电子传输动力学较慢，而对称的峰形是由于所有吸附的神经递质的彻底电解所致。其次，我们解释了多巴胺波形的起源，以及限制保持电位（氧还原），开关电位（水氧化），扫描速率（电极不稳定性）和重复速率（吸附）的因素。第三，我们讨论数据分析，从使用彩色图表进行数据可视化到自动算法（如主成分回归）将多巴胺与pH变化区分开来。最后，讨论了更新的应用程序，包括优化分析物选择性的波形，捕获多巴胺的碳纳米材料电极以及有助于较长时间范围内神经递质测量的基础水平测量。FSCV理论很复杂，