化学
电解质
氧化还原
循环伏安法
伏安法
离子
电化学
无机化学
支撑电解质
分析化学(期刊)
电极
物理化学
色谱法
有机化学
作者
Marco F. Suárez-Herrera,Micheál D. Scanlon
出处
期刊:Analytical Chemistry
[American Chemical Society]
日期:2020-07-01
卷期号:92 (15): 10521-10530
被引量:11
标识
DOI:10.1021/acs.analchem.0c01340
摘要
The interface between two immiscible electrolyte solutions (ITIES) is ideally suited to detect redox-inactive ions by their ion transfer. Such electroanalysis, based on the Nernst-Donnan equation, has been predominantly performed using amperometry, cyclic voltammetry, or differential pulse voltammetry. Here, we introduce a new electroanalytical method based on alternating-current (AC) voltammetry with inherent advantages over traditional approaches such as avoidance of positive feedback iR compensation, a major issue for liquid|liquid electrochemical cells containing resistive organic media and interfacial areas in the cm2 and mm2 range. A theoretical background outlining the generation of the analytical signal is provided and based on extracting the component that depends on the Warburg impedance from the total impedance. The quantitative detection of a series of model redox-inactive tetraalkylammonium cations is demonstrated, with evidence provided of the transient adsorption of these cations at the interface during the course of ion transfer. Since ion transfer is diffusion-limited, by changing the voltage excitation frequency during AC voltammetry, the intensity of the Faradaic response can be enhanced at low frequencies (1 Hz) or made to disappear completely at higher frequencies (99 Hz). The latter produces an AC voltammogram equivalent to a "blank" measurement in the absence of analyte and is ideal for background subtraction. Therefore, major opportunities exist for the sensitive detection of ionic analyte when a "blank" measurement in the absence of analyte is impossible. This approach is particularly useful to deconvolute signals related to reversible electrochemical reactions from those due to irreversible processes, which do not give AC signals.
科研通智能强力驱动
Strongly Powered by AbleSci AI